case study on agricultural robotics

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• Published: 20 May 2021

Responsible development of autonomous robotics in agriculture

• David Christian Rose   ORCID: orcid.org/0000-0002-5249-9021 1 ,
• Jessica Lyon 1 ,
• Auvikki de Boon 1 ,
• Marc Hanheide 2 &
• Simon Pearson   ORCID: orcid.org/0000-0002-4297-4837 3  

Nature Food volume  2 ,  pages 306–309 ( 2021 ) Cite this article

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Despite the potential contributions of autonomous robots to agricultural sustainability, social, legal and ethical issues threaten adoption. We discuss how responsible innovation principles can be embedded into the user-centred design of autonomous robots and identify areas for further empirical research.

Adding to the list of environmental challenges facing agriculture, COVID-19 and the demographics of age, migration and urbanization pose a serious threat to the sustainability of farm businesses and food security 1 . In particular, farm businesses across the world are struggling to fill vacancies and provide safe working conditions for labourers.

Autonomous robots could help address these immediate challenges 2 . Whilst their physical manifestation comprises hardware, such as a vehicle combined with manipulators, their autonomy is derived from sophisticated algorithms rooted in artificial intelligence. These algorithms fuse sensor data to enable control and real-time decision support. Autonomous robots can perform tasks collaboratively with humans (so-called co-bots) or on their own 3 . Apart from isolated on-farm examples, autonomous platforms with robotic mobility that fuse multiple technologies across a single fleet (for example, crop forecasting, planting, harvesting and packing) are not yet fully implementable and face substantial barriers. However, there is already adoption of static robotic milking technologies in the dairy sector and in-field deployment of tractor-mounted robotic manipulators to remove weeds and protect crops from pests and diseases 2 .

We know, however, that the history of agricultural innovation is littered with failure and slow adoption, and there are legal, ethical and social concerns associated with autonomous agriculture 4 , 5 . Potential challenges, opportunities and consequences of autonomous agriculture (Fig. 1 ) are interlinked and depend on how technologies are designed and implemented. Many of these aspects have been discussed in the burgeoning literature on the social and ethical impacts of digitalization in agriculture 6 , 7 . Empirical research remains limited for autonomous farming robotics; potential issues have largely been extrapolated from empirical research on smart farming technologies in general or the use of autonomous robots in other workplaces. Here, we identify examples of responsible innovation principles being implemented and indicate where more needs to be done.

Positive (+), negative (–) and uncertain (+/–) consequences are indicated. Positive consequences denote opportunities to be harnessed, whereas negative consequences denote challenges to be overcome concerning the operationalization, adoption and/or deployment of innovations (see refs. 4 , 5 for more detail).

Responsible innovation in agriculture and beyond

The most widely used framework for responsible innovation was proposed by Stilgoe and colleagues 8 and involves four key components: anticipating the impacts of innovation; reflecting on one’s work and adapting accordingly (reflexivity); including a wide range of stakeholders in the design process; and responding to stakeholders’ concerns, ideas and knowledge by constructing appropriate institutional structures.

Guidance on responsible innovation — provided by funders such as the Engineering and Physical Sciences Research Council 9 , InnovateUK 10 and the European Commission — encourages companies to be cognisant of their responsibility and committed to responsible research and innovation principles, by exploring the challenges that could arise from innovation and acting on their findings in a transparent, inclusive and timely manner. Despite frequent calls for companies to conduct a transparent and iterative process of responsible innovation, there is a lack of commitment to, or reporting of, the steps taken in technology development in the agriculture industry.

In the following sections we discuss how the four key components mentioned above can be operationalized to guide technology development in agriculture 11 , outlining key research needs. The examples referenced herein alongside the guidance from Stilgoe et al. 8 and Eastwood et al. 11 provide a good overview of techniques that can be used to apply responsible innovation principles.

Anticipation

With the objective of minimizing negative, unintended outcomes 8 , ‘anticipation’ involves identifying, predicting and exploring the potential short- and long-term consequences of future innovation across society and is therefore essential for the responsible development of autonomous robots.

Very little empirical anticipatory work for autonomous robots in farming has included a variety of stakeholders in the process, though a recent paper by Legun and Burch 12 begins to describe a process of co-design in the context of robotic apple orchards in New Zealand. Empirical studies have otherwise been limited to the narrow use of foresight exercises in the form of technology use and acceptance surveys and farmer 13 or public opinion surveys 14 using online questionnaires and short interviews. Foresight is also used to elucidate future benefits and challenges associated with combining a technology with other methods, such as the Delphi technique (which relies on anonymous rounds of voting) 15 . Other anticipatory processes include ‘horizon scanning’ (scanning data sources to detect early developments 16 ) and ‘socio-literary techniques’ (using science fiction as a tool to encourage dialogue about technology futures 17 , possibly through ‘Ag-Tech movie nights’ 18 ). A typical methodology in robotics and human–robotic interaction are ‘Wizard of Oz’ studies 19 , where autonomy is ‘fake’; robots are usually remote-controlled, anticipating the abilities they may have once fully implemented. Video studies are also often employed 20 , where participants are presented with recordings of robot behaviour and assess it from a third-person perspective.

One further method to consider is backcasting, which involves building an (ideal) future scenario, and working backwards to identify the steps needed to get to there. This is done in anticipatory governance approaches, for example. A key area for future research will be to use different anticipatory methods with diverse stakeholders specifically on the subject of autonomous robots in agriculture. Those included in the process of anticipation should be those directly affected by the adoption of robotics, including farmers, farm workers and consumers of food produced in that way. Including such a wide range of stakeholders will create a number of practical challenges related to power inequality (for example, farm managers versus farm workers) and language barriers (for example, migrant farm workers) and these will need to be managed sensitively by trained facilitators.

Reflexivity

Reflexivity entails “holding a mirror up to one’s own activities, commitments and assumptions, being aware of the limits of knowledge, and being mindful that a particular framing of an issue may not be universally held” 8 . Constant analysis and critique of one’s work among peers is an embedded practice of rigorous science. However, scientists and engineers typically carry out reflexivity and other responsible innovation practices behind closed doors, in the lab, and do not recognize these processes as reflexivity in responsible innovation terms 21 . Opening these conversations up to the public and acknowledging and listening to other actors can improve the quality of reflexivity.

Reflexivity in the realm of autonomous robots in agriculture has mostly come from the user-centred design process. Work to date in this space has recognized that robotic systems interacting with humans need to undergo an iterative development approach 22 , bringing together subjective user experience with actual system logs. After including stakeholders and seeking their information requirements and preferences for autonomous robots through surveys 23 , workshops 24 and field experiments 25 , 26 , 27 , designers have altered prototypes and design paths to ensure that the robots work for the user. Yet, this is narrow reflexivity; it involves developers tweaking design based on user feedback, rather than conducting a fundamental analysis of the assumptions and values underlying the proposed solution or questioning if agricultural robotics is really the path we want to take as a society. We rarely carry out a deeper form of reflexivity, possibly missing alternative solutions.

The development of and engagement with best practice guidelines, codes of conduct and international standards is another form of reflexivity that can guide industry to conduct innovation in a responsible manner, although it is not always clear whether they continue to serve the purpose of reflexivity once adopted. In Australia, a code of practice for agricultural mobile field machinery with autonomous functions is currently under development to help guide safe working procedures in the field; this code of practice is intended to hold some legal weight. International standards for the use of autonomous robots such as ISO 10218 provide norms for worker safety when collaborating with robots in a structured, industrial environment. In ISO 10218, safety aspects such as tactile and pressure sensors, safe maximum speed, proximity sensors, human detection cameras, and emergency stop are described to ensure the safety of human–robot collaboration. Other relevant international standards include: ISO 18497 (design principles for safety with highly automated agricultural machines — operations of robots in-field are not covered); ISO 17757 (for use of autonomous machinery in mining); and ISO/SAE DIS 21434, currently under development (for cybersecurity in road vehicles). The agricultural industry can glean insights from these standards, however there is a necessity to further develop agriculture-specific standards and codes of practice that account for human–robot collaboration in flexible, unstructured environments such as in the field. Understanding how this might be done effectively, bringing together relevant stakeholders, is an important future area for research.

Concepts of inclusion are frequently limited to the ‘consideration’ of how stakeholders may be impacted or react to innovation by a limited group of experts 28 . Genuine inclusion should involve the participation of a full range of stakeholders. If we do not pursue methods for the substantive inclusion of a full range of actors, not just the usual suspects, and do not give due attention to power inequalities between stakeholders throughout the participatory process, then we risk reinforcing unequal participation under the guise of inclusivity. It may appear that increased participation from the start is time-consuming and resource-intensive, but user-centred design can prevent problems further down the line.

Within the development of autonomous robots in agriculture, inclusion has mostly taken the form of consultation and sometimes collaboration, involving feedback from farmers and farm workers on the technical side of robot development. Simulation experiments 29 , 30 and field-based workshops 23 have allowed farmers and farm workers to test the usability of a technology. Researchers have used task scenarios, observations, and participant feedback to feed into prototype development. The social sciences have developed a number of participatory methods that allow substantive inclusion, such as citizen juries and deliberative workshops, and a greater selection of these should be brought to bear for inclusion surrounding autonomous agriculture 31 .

Stakeholders identified in the PAS 440 Responsible Innovation framework developed for InnovateUK 10 include co-developers; markets, customers and end-users; regulators and standards bodies; NGOs representing civil society stakeholders; and individual citizens likely to be affected. Beyond the usual suspects, it is important to engage with harder-to-reach stakeholders. Schillo and Robinson 28 discuss the importance of engaging with historically marginalized groups. In the case of autonomous agriculture, this could involve small farmers (who may be pushed out of the industry by larger farmers with more capacity to adopt and adapt), organic farmers (whose farming strategy may be more difficult to align with autonomous robots focused on precision fertilization 32 ), as well as farm workers (who could lose jobs as they are replaced by robots). Blok 33 argues that stakeholder inclusion and participation can typically become reductive as it focuses on the cognitive approach to understanding the perspectives of stakeholders in a self-serving ‘immunization strategy’, where the goal is to convince others, prevent criticism and portray the company as having good intentions. We should ultimately ensure that we are undertaking substantive, rather than tokenistic inclusion.

The involvement of stakeholders should not be restricted to the exploration of consequences in terms of economic opportunity or technology acceptance, but include wider implications and society’s ‘grand challenges’. To date there are limited examples of this work: Pfeiffer et al. 14 explored public opinion of digital farming technology through surveys and spontaneous associations; Kester et al. 13 surveyed farmers’ views of the future of automation on topics such as perceived value, applications and expectations; and Baxter et al. 26 asked fruit pickers questions regarding the impact of autonomous robots on their job security.

Responsiveness

Identifying potential consequences, reflecting on underlying assumptions, values and problem-solving processes, and including stakeholders in the innovation process can only lead to responsible innovation if newly gained insights are acted on. Actors should be reactive to new knowledge and ensure development is iterative. This could be in the form of adapting R&D projects or early design prototypes based on feedback from stakeholders. Other actions that result from new information could include adjusting business models, altering control or access to software, amending workers contracts and working conditions 3 , or refraining from developing a certain robot altogether if it is not desired by society.

Responsiveness is also important within institutional structures, which should respond promptly to new information, in policy, law and regulatory environment. Regulation can restrict innovation (for example, genetically modified crops in Europe), efficiency and competitive advantage, however legal structures will be important to ensure protection for users of autonomous robots and for clarifying the liability framework. Hence, regulation can act as both a barrier to and an enabler of adoption. Basu et al. 5 describe the current legal frameworks, regulations and standards that are relevant to the development of autonomous robots in agriculture, as well as the gaps in areas such as data protection law, ethics of robot autonomy and artificial intelligence. Similarly to how the European Union embedded ‘privacy by design’ into its General Data Protection Regulation, others are calling for ‘equality by design’ in artificial intelligence regulation to safeguard against bias and discrimination that may inadvertently be engrained in technology and machine learning 34 . There are examples of ‘technological redlining’ as well as technological limitations of measurement such as unequal object detection or lower quality heart rate measurement for those with darker skin 34 . A lack of transparency with algorithms, machine learning and artificial intelligence — the ‘black box problem’ — can lead to bias and discrimination issues within machine learning becoming further entrenched and replicated. Regulatory oversight of equality by design 34 is key to ensure that programmers address any bias and discrimination that may be produced in algorithms, ultimately ensuring that technology treats users fairly.

Addressing the social, legal and ethical implications of autonomous robots is arguably a greater challenge than the development of the technology itself. More research is needed to ensure that anticipation, reflexivity and inclusion efforts are turned into responsive action on the ground. As highlighted in this paper, most empirical work for the development of autonomous agriculture has been focused on the technical aspects of robot operation with some level of inclusion and reflexivity to ensure improvement of technical performance. Little published work has gone beyond this to use methods that allow for substantive inclusion and deeper reflexivity on the subject. Yet, if society decides that autonomous robotics for farming is the way to go, then practising responsible innovation in their development is vitally important to prevent future controversy, implementation delays and negative consequences. Ultimately, the success or failure of autonomous robots in agriculture will not rest on the limits of our technical enterprise, but on our ability to involve society, learn from it and respond appropriately.

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Acknowledgements

This paper was developed from the Robot Highways project funded by InnovateUK as part of the ISCF TFP Science and Technology into Practice: Demonstration call (grant number 51367).

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Rose, D.C., Lyon, J., de Boon, A. et al. Responsible development of autonomous robotics in agriculture. Nat Food 2 , 306–309 (2021). https://doi.org/10.1038/s43016-021-00287-9

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Robotics for Smart Farming

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Robotics in agriculture explores the potential of robotics and artificial intelligence to revolutionize the way farming is done. It looks at the possibilities for automation in crop production and livestock farming, as well as the implications for farming and rural communities. It examines the ways in which robotics could reduce costs, increase yields, and improve safety and sustainability. It also considers the potential risks and drawbacks associated with the use of robotics and AI in agriculture, such as the potential for job losses and the vulnerability of robotic systems to cyberattack. This Research Topic (Robotics for Smart Farming) aims to highlight the latest research in robotic technologies relevant to agriculture and farming processes. It will focus on agricultural robotics covering different fields of robotics, intelligent perception, manipulation, control, path planning, machine learning, and the applications of robotic and control systems in agriculture. The goal of this Research Topic is to explore the potential of robotics for smart farming and to bring together the latest developments in the field of robotics for agriculture and food production. We aim to provide a comprehensive overview of the current state of research and applications in this field, and to identify the challenges, opportunities and future trends in robotics for smart farming. We also aim to promote collaboration between researchers and practitioners, and to provide a platform for exchanging ideas and experiences. The scope of this Research Topic is to review the latest developments in the field of robotics for smart farming. We invite original research papers, review articles, and technical notes on topics related to the following, but not limited to: • Robotics and UAVs in Smart Farming • Robotics for crop production, harvesting, and post-harvest processing • Autonomous navigation and control of agricultural robots • Machine learning and artificial intelligence for agricultural robotics • Deep learning and reinforcement learning for agricultural robotics • Robotic Applications in Agriculture for Land Preparation before Planting • Robotic Applications in Agriculture for Sowing and Planting • Robotic Applications in Agriculture for Plant Treatment • Robotics for Yield Estimation and Phenotyping • Robotic Applications in Agriculture for Harvesting • Robotic Systems for Food Production • Robotic Livestock Farming • Robotic Fish Farming • Robotic Crop Plantation and Weeding • Robotic Harvesting • Robotic Crop Sensing and Monitoring • Robotic Disease Detection • Robotics in Precision Agriculture • Robotics in Food Processing • Social and ethical implications of robotics in agriculture

Keywords : Robotics, Smart Farming, Autonomous Navigation, Sensor Technologies, Machine Learning

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Mobile robotics in smart farming: current trends and applications

Darío fernando yépez-ponce.

1 Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, Valencia, Spain

2 Facultad de Ingeniería en Ciencias Aplicadas, Universidad Técnica del Norte, Ibarra, Ecuador

José Vicente Salcedo

Paúl d. rosero-montalvo.

3 Computer Science Department, IT University of Copenhagen, Copenhagen, Denmark

Javier Sanchis

In recent years, precision agriculture and smart farming have been deployed by leaps and bounds as arable land has become increasingly scarce. According to the Food and Agriculture Organization (FAO), by the year 2050, farming in the world should grow by about one-third above current levels. Therefore, farmers have intensively used fertilizers to promote crop growth and yields, which has adversely affected the nutritional improvement of foodstuffs. To address challenges related to productivity, environmental impact, food safety, crop losses, and sustainability, mobile robots in agriculture have proliferated, integrating mainly path planning and crop information gathering processes. Current agricultural robotic systems are large in size and cost because they use a computer as a server and mobile robots as clients. This article reviews the use of mobile robotics in farming to reduce costs, reduce environmental impact, and optimize harvests. The current status of mobile robotics, the technologies employed, the algorithms applied, and the relevant results obtained in smart farming are established. Finally, challenges to be faced in new smart farming techniques are also presented: environmental conditions, implementation costs, technical requirements, process automation, connectivity, and processing potential. As part of the contributions of this article, it was possible to conclude that the leading technologies for the implementation of smart farming are as follows: the Internet of Things (IoT), mobile robotics, artificial intelligence, artificial vision, multi-objective control, and big data. One technological solution that could be implemented is developing a fully autonomous, low-cost agricultural mobile robotic system that does not depend on a server.

1. Introduction

In recent years, the global population has increased unprecedentedly, leading to significant changes in food demand (Dhumale and Bhaskar, 2021 ). As we move into the future, it is expected that the demand for food will continue to rise, driven by factors such as population growth, urbanization, and changing dietary preferences. In addition, the effects of climate change have also impacted food demand and supply, creating new challenges for the food industry (Dutta et al., 2021 ). In Springmann et al. ( 2018 ), it is mentioned that by 2050, the food chain might increase production by 50%. Besides, the FAO shows that the world population will reach approximately 10 billion by that year (Ahmed et al., 2018 ). This population increase affects the environmental conditions, which changes the harvesting process forcing farmers to use fertilizers and pesticides (Shafi et al., 2019 ). The residuals of those chemical pollutants contaminate water (Rajeshwari et al., 2021 ). Another concern is the nutritional outcome that offers food since the previous statement that the environmental condition worsens, creating floods and droughts. Therefore, humans are not receiving enough nutrients to be healthy by eating processed food, requiring pills and supplements (Mostari et al., 2021 ). The Intergovernmental Panel on Climate Change (IPCC) warns that global warming reduces the nutritional value of crops due to the intensive use of fertilizers to boost crop yields; they also predict that in the incoming years, people might suffer from zinc deficiency, causing even their psychological and cognitive disorders (Ryan et al., 2021 ).

Technology in the food production industry is a significant challenge that impedes progress and innovation in this critical sector. With the rapidly growing global population and increasing demand for food, it has become imperative to adopt technological advancements to improve food production and distribution (Ferrag et al., 2021 ). However, in many parts of the world, particularly in developing countries, technology in food production still needs to be improved, resulting in low productivity, high food losses, and reduced efficiency. Given that a big part of food production is from developing countries, exists a lack of advanced agricultural technologies (Khan et al., 2021 ). They face significant financial constraints and limited access to modern technologies, which can impede their ability to improve their food production processes. This concern also extends to the education and training of the workforce, who may not have the knowledge and skills to operate and maintain technological tools and equipment effectively (Xuan, 2021 ).

To mitigate the concerns mentioned above about food supply, FAO proposes four bullet points to guarantee food quality in the incoming years, which they closely related to the use of technology since information plays a fundamental role in ensuring the economic and sustainability impacts of new cutting-edge techniques in the food production process (Mooney, 2020 ).

Implementing emerging technologies in agriculture is often called smart farming, which aims to improve productivity, efficiency, and sustainability (Raj et al., 2021 ). In Belhadi et al. ( 2021 ), mention that smart farming might use trend technologies such as robotics, artificial intelligence, and the IoT. Therefore, these devices can gather data from crops to extract intrinsic knowledge from plants to improve agricultural decision-making and reduce environmental impact (Megeto et al., 2021 ). However, the full exploitation of the potential of smart farming presents several challenges and technical, socio-economic, and administrative constraints (Mengoli et al., 2021 ). Works such as Ahmed et al. ( 2016 ), Jawad et al. ( 2017 ), Bermeo-Almeida et al. ( 2018 ), Kamilaris and Prenafeta-Boldu ( 2018 ), and Rahmadian and Widyartono ( 2020 ) present broad approaches to smart farming and trend technologies without focusing only on robots. These studies do not include a detailed discussion of the tools and techniques used to develop the different mobile systems and their level of maturity. It is relevant to discuss the use of mobile robotics in smart farming from different perspectives and describe their corresponding nuances.

This article stands out from others of a similar nature because it offers a broad overview of the challenges and opportunities presented by precision agriculture and robotic farming. The article focuses on the use of robotics and precision agriculture in agriculture 4.0 and provides a detailed description of the many types of agricultural robots used, as well as the techniques and hardware used for their operation and monitoring. Additionally, the article highlights the areas where literature is least developed and suggests potential solutions to address these challenges. Future trends in precision agriculture and robotics are also discussed, including the use of multi-objective control algorithms and artificial intelligence in low-cost mobile robots for planning the best path while accounting for energy efficiency, soil type, and obstacles, as well as for evaluating and managing pests and diseases that affect crops.

This work aims to present an overview of mobile robotics implemented for agricultural production related to smart farming techniques. The main contribution of this work is showing the existing frameworks, tools, and applications where robots are currently used. Also, it presents shortcomings in smart farming applications, which might provide future trends in robots. The rest of the manuscript is structured as follows: Section 2 gives the smart farming background and provides a detailed overview of the leading mobile robots with existing technologies. Section 3 presents the discussion highlighting the technical and socio-economic obstacles to successfully integrating mobile robotics in agriculture. Section 4 presents the future trends related to mobile robotics in agriculture. Finally, Section 5 presents the conclusions.

2. Research methodology

A systematic literature review (SLR) was performed to manage the diverse knowledge and identify research related to the raised topic (Ahmed et al., 2016 ), especially to investigate the status of mobile robotics in precision agriculture. In particular, we searched for papers on “mobile robotics” with the term “agriculture 4.0” in the title, abstract or keywords. Prior to the SLR, a review protocol was defined to ensure a transparent, high quality and comprehensive research process (Page et al., 2021 ) including three steps: formulating the research questions, defining the search strategy, and specifying the inclusion and exclusion criteria. The preferred reporting approach for systematic reviews and meta-analyses (PRISMA) was used to conduct the SLR.

2.1. Review protocol

Before starting the bibliographic analysis, a review protocol was defined to identify, evaluate and interpret the relevant results of the research topic (see Table 1 ). The first step was to formulate research questions to identify the studies published on the subject of interest from different approaches. The appropriate keywords were then identified order to formulate search strings to obtain relevant information using four databases: IEEE Xplore, Web of Science, Scopus, and ScienceDirect. To refine the search results, inclusion and exclusion criteria were defined to evaluate the content of the publications and used as a preliminary filter of the metadata sources and limit the scope of the research.

Review protocol for SLR.

After performing the SLR, 69 research articles were obtained on the proposed topic. After the PRISMA selection and eligibility steps with the help of the Mendeley bibliographic reference manager, similar files were identified and eliminated, leaving a total 65 research papers, as can be seen in Figure 1 .

Three-steps evaluation of literature search process (PRISMA).

2.2. Trends in agriculture

The distribution of the 65 articles by year, about 38% of the most recent scientific papers were published in 2021, reflecting the considerable progress of agriculture in the context of mobile robotics, although the pace can still be considered slow compared to other domains such as healthcare, the manufacturing, the mining, the automation, the energy, among others (Araújo et al., 2021 ).

Figure 2 gives the breakdown of publications on the five most common activities carried out by agriculture 4.0 and the type of mobile robot employed. The multiple tasks in the field category include activities such as row recognition and tracking, obstacle detection and avoidance, and information gathering and reporting in both outdoor and greenhouse agriculture.

Mobile robotics activities in agriculture.

According to the International Federation of Robotics (IFR), the top five service robot applications for professional use sold during 2019 and 2020 are: transportation and logistics, professional cleaning, medical robotics, hospitality, and agriculture (International Federation of Robotics, 2021 ). Figure 3 gives the percentage of robots employed in each of these areas.

Percentage of top five applications of service robots in 2020.

3. Background and related works

Smart farming is a technique that uses advanced technology to optimize yield and efficiency in agricultural production. In Lohchab et al. ( 2018 ), explored the application of IoT technologies in smart agriculture. Subsequently, in a 2020 review article, Sharma et al. ( 2020 ) focused on the use of artificial intelligence and machine learning in smart agriculture. Furthermore, in a 2021 review article, Ratnaparkhi et al. ( 2020 ) discussed the implementation of sensor technologies and Geographic Information Systems (GIS) for smart agriculture. Finally, in a recent 2022 review article, Botta et al. ( 2022 ) examined the integration of robotics and automation in smart agriculture. Some topics that very little has been addressed in smart agriculture are: the integration of smart agriculture with the circular economy and environmental sustainability, the development and application of artificial intelligence and machine learning technologies in pest and disease identification and management, increased focus on optimizing water use and irrigation management in response to climate change and limited water availability, improved connectivity and interoperability of systems to facilitate large-scale adoption and implementation, and the development of specific low-cost solutions for small farms and rural communities in developing countries to improve food security and reduce rural poverty.

3.1. Smart farming

Smart farming is based on the information provided by sensors placed on an agricultural field (Ahmed et al., 2016 ); Machine Learning (ML) models could learn patterns to support the farmers' decision-making (Mammarella et al., 2020 ; Shorewala et al., 2021 ). These sensors joined with a microcontroller sending data constantly, are considered part of the IoT. Besides, data might be processed in big servers allocated in the cloud (cloud computing). However, IoT devices are often a rigid solution since they are placed in a single location. Therefore, Autonomous Robotic Systems (ARS) can walk around crops taking data from the whole farm and providing accurate information (Ozdogan et al., 2017 ; Kamilaris and Prenafeta-Boldu, 2018 ). This combination of sensors, data analysis, and robots provides farmers with a smart farming application with diverse tools to address challenges related to productivity, environmental impact, food safety, crop losses, and sustainability. The objectives of smart farming are to increase crop yields, minimize costs, and improve product quality through using a modern system (Araújo et al., 2021 ). In the last years, with technological evolution, different types of sensors have been developed that make it possible to collect data in almost any location, allowing real-time monitoring of agricultural fields without wiring. Therefore, the three leading technologies that contribute significantly to this field are as follows:

• Drones: These are small flying robots commonly used for crop monitoring, food infrastructure inspection, supply chain monitoring, and food safety surveillance (Costa et al., 2021 ).
• Autonomous tractors: These are generally Unmanned Ground Vehicles (UGV) incorporating sensors and actuators that enable crop monitoring, irrigation, harvesting, and disease control (Lisbinski et al., 2020 ).
• Software for decision making: These are platforms where data acquired by drones and/or UGV sensors are visualized and analyzed. They generally provide information on weather, soil, crop yields, and other factors relevant to agricultural production to improve decision-making (Ojeda-Beltran, 2022 ).

3.2. Mobile robotics in agriculture

The emerging field of agricultural mobile robotics is UGV and UAV (Prakash et al., 2020 ). The main applications of mobile robotics in farming are:

• Identify the state of the crop and corresponding application of chemical products, fumigation, or harvesting, as required by the fruit or plant.
• Mobile handling through collaborative arms (harvesting, fruit handling).
• Collection and conversion of helpful information for the farmer.
• Selective application of pesticides and avoidance of food waste.

UGV and UAV have limited available power. Therefore, their design and control optimization is paramount for their application in smart farming. Therefore, research on the cooperation between UGV and UAV is being carried out to cover large agricultural areas. These autonomous robots are intelligent machines capable of performing tasks, making decisions, and acting in real-time with a high degree of autonomy (Rahmadian and Widyartono, 2020 ). Interest in mobile robotics in agriculture has grown considerably in the last few years due to its ability to automate tasks such as planting, irrigation, fertilization, spraying, environmental monitoring, disease detection, harvesting, and weed and pest control (Araújo et al., 2021 ). Furthermore, mobile robotics in smart farming uses a combination of emerging technologies to improve the productivity and quality of agricultural products (Bechar and Vigneault, 2016 ).

UGV are robots that control can be remote (controlled by a human operator through an interface) or fully autonomous (operated without the need for a human controller based on AI technologies) (Araújo et al., 2021 ). The main components of UGV are locomotive, manipulator and supervisory control systems, sensors for navigation, and communication links for information exchange between devices. The main locomotion systems used are wheels, tracks, or legs. To properly operate UGV in the field, they must meet size, maneuverability, efficiency, human-friendly interface, and safety requirements. Table 2 summarizes the diverse range of UGVs designed for agricultural operations.

Different types of UGV in agriculture 4.0.

The main issue of mobile robotics in agricultural fields is to perform multiple tasks (obstacle avoidance, tracking, path planning, crop data collection, disease detection, among others) autonomously with reduced hardware for low-cost robots that can be acquired and implemented by farmers. Most UGV presented above have a wheeled locomotion system, offering easy construction and control. Some UGV incorporate low-cost computer vision systems, i.e., using conventional cameras. UGV might employ heuristic algorithms still in the conceptual or prototyping phase. Due to the limitations of UGV and to cover larger areas and less time, in the last years, the UGV-UAV collaboration has been developed (Khanna et al., 2015 ). The UGV operates in the areas selected by the UAV, which also cooperates in the generation of 3D maps of the environment with centimeter accuracy; however, merging the maps generated by UAV and UGV in an agricultural climate is a complex task since the generated maps present inaccuracies and scale errors due to local inconsistencies, missing data, occlusions, and global deformations (Gawel et al., 2017 ; Potena et al., 2019 ). Table 3 reviews some collaborations between UGV and UAV in smart farming.

Collaborations between UGV and UAV in smart farming.

Most collaborative systems between UAV and UGV are in the conceptual (simulation) phase.

3.3. Multi-objective control in smart farming

Agricultural systems use and produce energy in the form of bioenergy and play a vital role in the global economy and food security. Modern agricultural systems might therefore consider economic, energy, and environmental factors simultaneously (Banasik et al., 2017 ). Multi-objective control is an important tool in smart farming to simultaneously run and optimize multiple objectives, such as productivity, water use efficiency, product quality, and economic profitability. Some cases of multi-objective control in smart farming are presented in Table 4 , which shows their primary function, control techniques, and the hardware deployed. However, there are few studies since this topic is new in smart farming applications with mobile robots. Furthermore, path planning is an essential application of smart agriculture that focuses on optimizing routes and movements of agricultural machinery to improve efficiency and reduce production costs (Nazarahari et al., 2019 ).

Multi-objective control in agriculture 4.0.

Another application of multi-objective control in path planning is the optimization of fertilization and pesticide application in crops. According to a study by Zhao et al. ( 2023 ), multi-objective control can optimize the routing of pesticide and fertilizer application machinery to reduce the number of inputs used and improve application efficiency. In addition, multi-target control can also improve product quality and reduce environmental pollution by accurately applying crop inputs.

Finally, a study proposes a Residual-like Soft Actor-Critic (R-SAC) algorithm for agricultural scenarios to realize safe obstacle avoidance and intelligent path planning of robots. The study also proposes an offline expert experience pre-training method to improve the training efficiency of reinforcement learning. Experiments verify that this method has stable performance in static and dynamic obstacle environments and is superior to other reinforcement learning algorithms (Yang et al., 2022 ).

4. Discussion

With the information mentioned above about mobile robots in smart farming, this section aims to show the future steps in this research field related to its challenges. Given the new UGV and UAV trends in Table 2 , the multi-objective control has yet to be widely explored in smart farming applications. It might be due to its complex setup and the expensive computational resources needed. However, multi-objective applications might be doable in incoming robots with the increasing microcontrollers and microprocessor development. Conversely, IoT devices that collect data from farms are extensively deployed in several applications. However, there are new concerns about their confidentiality and the risk that data is exposed when traveling by communication channels (Pylianidis et al., 2021 ).

Smart farming needs final devices with robust systems working in harsh conditions in outdoor scenarios. However, several works have shown prototypes with their tentative functionalities. Building robots may need several debugging rounds to solve issues with the hardware and software. Consequently, since the robot links people and plants, farmers, considered experts in smart farming, must work closely with the robot's developer. However, the variety of plant and crop species makes it challenging to develop a multi-task robot (Selmani et al., 2019 ).

The main challenges and future research for deploying smart farming are presented. The present study sought to articulate mobile robotics with smart farming. Looking at Table 4 , it can be seen that multi-objective control has not been significantly explored in smart farming. One of the reasons could be that applying advanced technologies with complex operations can be costly. Hence, the development of these technologies in smart farming should increase in the coming years. Also, the IoT is widely deployed in agriculture for crop monitoring and tracking. Therefore, it can be said that IoT is a research trend within smart farming. However, only a few studies have considered data security and reliability, scalability, and interoperability when developing a smart farming system (Pylianidis et al., 2021 ).

The results presented also show that most of the use cases are in the prototype phase. One possible reason could be that smart farming links people, animals, and plants making it more difficult than creating systems for non-living things. Another reason could be that the technology is due to the transdisciplinarity of this field, and therefore for the development of intelligent systems, farmers should be familiar with these technologies. Finally, the variety of plant and crop species makes implementing technology in agricultural fields complex (Selmani et al., 2019 ). The results also show that most systems developed are for free-range farms. In addition, it is also evident that research is limited to soil management, fruit detection, and crop quality management. With this, it is corroborated that work must be done on research and development of systems that guarantee the deployment of smart farming at affordable costs. The natural complexity of agricultural fields presents a number of obstacles that prevent the full integration of mobile robotics in smart farming. Therefore, from the analysis, blockages at the technical and socio-economic levels have been identified and classified.

4.1. Technical roadblocks

• Interoperability . To establish effective communication between heterogeneous devices, they need to be interconnected, and interoperable (Aydin and Aydin, 2020 ).
• Dataquality . Lack of decentralized systems impedes the deployment of smart farming (Liu et al., 2022 ).
• Hardware . A suitable casing must be constructed that is robust and durable enough to withstand actual field conditions (Villa-Henriksen et al., 2020 ).
• Power sources . A proper energy-saving scheme is necessary as instant battery replacement is complicated. A possible solution to optimize power consumption is using low-power hardware and proper communications management (Jawad et al., 2017 ).
• Wireless architectures . Wireless communication networks and technologies offer several advantages in terms of low cost, wide area coverage, network flexibility, and high scalability (Brinis and Saidane, 2016 ).
• Security . The nature of agricultural fields leads to risks to data privacy, integrity, and availability (Chen et al., 2017 ).
• User interface . Most graphical user interfaces are designed so that only experts can use them (Del Cerro et al., 2021 ).

4.2. Socio-economic roadblocks

• Costs . Costs associated with adopting robotic technologies and systems are the biggest drawback to deploying smart farming (Sinha and Dhanalakshmi, 2022 ).
• Return on investment . When implementing new technologies, farmers are concerned about the payback time and the difficulties in assessing the benefits (Miranda et al., 2019 ).
• Gap between farmers and researchers . Farmer involvement is paramount to the success of smart farming. Farmers face many problems during the production process that technology could solve (Bacco et al., 2019 ).

Finally, in Charatsari et al. ( 2022 ) discusses the importance of responsibility in the process of technological innovation in the agrifood industry. It highlights the need to consider not only technical aspects but also social implications and societal values when introducing innovative technologies. The authors argue that the perception of responsible innovation is limited in various industrial sectors, making it challenging to implement responsible innovation approaches. The complexity of responsible innovation in the agrifood industry requires addressing the multiple scales and levels of interaction between actors and the constant evolution of agrifood systems. Therefore, the article emphasizes the need to adopt responsible innovation practices that consider the social, ethical, and environmental implications of technological innovations in the industry.

5. Future trends

The upcoming initiatives related to using robots represent significant improvements in smart farming. Government initiatives, public-private sectors, and research work in this field might contribute to establishing the right conditions to add new hardware to crops. However, there are some challenges to consider when developing mobile robots in agriculture such as: navigation on uneven terrain (loose soil and unpredictable obstacles) without damaging plants or compromising their own safety, energy efficiency so that they can operate for long periods of time avoiding constant human intervention, crop manipulation, integration with farm management systems and adaptability to different crops and conditions.

For instance, a robotic system can be developed for smart farming, starting from a basic architecture with few components and simple functionality that allows the gradual addition of features and functionality to create a complex system. Future trends in smart farming involve using multi-objective control algorithms and artificial intelligence in low-cost mobile robots to plan the best trajectory considering energy efficiency, soil type, and obstacles while monitoring crop growth and assessing and controlling crop pests and diseases. To ensure good connectivity and live transmission of crop data, 5G technology needs to be widely explored. 5G technology minimizes internet costs and increases information management by remotely performing accurate inspections of agricultural fields (Abbasi et al., 2021 ). Finally, blockchain, combined with IoT and other technologies, should be applied to address the challenge of information privacy and security (Bermeo-Almeida et al., 2018 ).

As seen in the tables in the previous sections, most of the UGV have a computer, which increases the cost of this type of robots. Table 5 shows several state-of-the-art boards that could deploy smart farming at affordable prices for farmers.

Boards for agriculture 4.0.

Finally, in Figure 4 , we can see the future of agriculture, for which a correct 5G network deployment and path planning/tracking is essential. Artificial intelligence, machine learning, machine vision, IoT, and cloud computing are needed in each of the activities carried out in agricultural fields.

Future of agriculture.

6. Conclusions

Growing concerns about global food security have accelerated the need to incorporate mobile robots in agriculture. The scientific community and researchers are integrating disruptive technologies into conventional agricultural systems to increase crop quality and yields, minimize costs, and reduce waste generation. This article analyzes the current state and challenges of smart farming. Considering the impact of farming on climate change and healthy food production, it is vital to provide the agricultural sector with low-cost, functional mobile robots. Research questions were posed and answered regarding the use of mobile robotics in agriculture, the technologies, methods, and tools used in agricultural fields, and the main challenges of multi-target control in this area. Several conclusions were drawn, such as the integration of scalable mobile robots incorporating efficient systems. It should be noted that most cases address a specific problem and are in the prototype phase.

From the SLR conducted, it was identified that research on the following topics is limited:

• The implementation of digital twins for robot-based production lines
• Ingenious software project management while narrowing the impact aspect.
• Blockchain in agriculture.
• Context-aware wireless sensor network suitable for precision agriculture.
• Internet of Things (IoT) for smart precision agriculture and farming in rural areas.
• Semantic and syntactic interoperability for agricultural open-data platforms in the context of IoT using crop-specific trait.
• Multi-objective path planner for an agricultural mobile robot in a virtual and real greenhouse environment.
• Closing loops in agricultural supply chains using multi-objective optimization.
• New control approaches for trajectory tracking and motion planning of unmanned tracked robots.

These areas require further research to improve the efficiency and effectiveness of precision agriculture. Likewise, the information gathered in this article makes it clear that the emerging fields of research are:

• Autonomous navigation . Planning, tracking of trajectories, and task planning should be considered in this area.
• Energy efficiency . Good navigation autonomy is not the only thing that must be taken into account, but also the design and all components that make up the mobile robot since its size and cost directly influence the deployment of smart farming.
• Communication . Due to the number of devices involved in smart farming, middleware that improves communication between field devices and the station is important to ensure the reliability and security of information.

The interdependence of these challenges means that a practical solution must be sought with a suitable compromise between the theoretically optimal path that facilitates information exchange and overall system energy optimization. Moreover, the following questions must be considered: the kinematic and dynamic design of the mobile robot, the terrain traversability, the computational complexity of the various algorithms to ensure real-time performance, the use of sensors and low-energy control boards, and the sending and receiving of information. It also identifies the leading technical and socioeconomic obstacles that must be overcome to deploy smart farming successfully. We can see leaps and bounds being made in this area, but there is still a long way to go to mitigate the impact of farming on the environment in the coming years. Finally, one of the areas to be investigated is multi-objective heuristic optimization for autonomous navigation, communication, and energy efficiency of mobile robots.

Finally, numerous international political organizations play a crucial role in spreading awareness of the technologies involved in precision agriculture and advocating for their successful implementation. These organizations are:

• The FAO promotes the use of advanced agricultural technologies through programs and projects, providing technical assistance, training, and resource access for farmers.
• The European Union (EU) supports agricultural modernization and the adoption of innovative technologies in the industry through its Agricultural Common Policy (ACP). Additionally, the UE funds research and development projects in precision agriculture, agricultural robotics, and digital solutions to increase efficiency and sustainability.
• The Department of Agriculture (USDA) of the United States places emphasis on the adoption of cutting-edge agricultural technologies. The USDA supports the implementation of precise agriculture systems, the integration of sensors and IoT devices into agricultural operations, and the promotion of digitalization in the industry through its funding and grant program.
• The focus of AGRA is to encourage the use of contemporary agricultural technologies across the African continent. AGRA works in close partnership with governments, regional organizations, and the private sector to increase access to and availability of improved seeds, fertilizers, and digital farming technologies that boost agricultural productivity and sustainability.
• The World Economic Forum (WEF) has established initiatives and projects to advance precision agriculture. The WEF brings together many actors-including political leaders, business executives, and members of civil society-through its platform “Shaping the Future of Food Security and Agriculture” to develop innovative and collaborative solutions that foster the digital transformation of agriculture.

These political organizations play a crucial role in the spread of advanced agricultural technologies, and they are actively working to promote the adoption of “agriculture 4.0” on a global scale with the aim of enhancing the efficiency, productivity, and sustainability of the agricultural sector.

Author contributions

JVS supervised this project. PR-M and JS contributed in this project. DY-P made the first version of the article under the guidelines of the other authors; likewise, he made the corrections to the observations made by the reviewers and shared them with the other authors for their respective review and subsequent approval. All authors contributed to the article and approved the submitted version.

Funding Statement

This work was supported by Generalitat Valenciana regional government through project CIAICO/2021/064.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Agricultural Robotics and ML

Agriculture, ml-powered agricultural robotics.

Edge Impulse machine learning models are helping a UK-based robotics startup record crop trials while providing precise irrigation levels to help quantify the best-growing conditions for the crops.

One research and development project I have been working on over the last eight months with a longtime friend is an agricultural robot called MARV-bot intended to support crop trials.

Crop trials are intensive operations, as the growth of the crop needs to be recorded accurately along with providing known irrigation levels to help quantify the best growing conditions for the crops.

Crop trials are also often performed in remote locations, which makes for difficulty in access for the trials team. These issues combined make crop trials a costly and intensive operation.

Like many industries, agriculture is seeing a robotic revolution. Agricultural robots are helping farmers pick crop and remove weeds. Dedicated trials robots can not only provide irrigation and spraying, but are able to accurately identify and analyze each of the individual crops as well.

Due to the remote location, agricultural robots need to operate with constrained power budgets. As navigation and communication are critical systems, this limits the power available for the embedded systems deployed on it. The remote locations also means agricultural robots need to leverage modern communication infrastructure. This infrastructure enabled near-real-time telemetry and analysis results to be communicated with the operations center. Depending on the deployment location this could be either terrestrial or satellite 5G. Of course, GNSS is also used to provide an accurate location of the robot and can be used to tag the location of the crops as well.

The trials robot Adiuvo have been supporting is developed by Robotic System Ltd. The robot is designed to straddle a standard trails patch. As such, most of the robot is located above the crops enabling the robot to not only spray the crops but also to image the crops from above. The robot has two modes spraying where it travels at the required rate of 3 MPH and imaging where the robot can image and analyze the crops at a slower speed taking time to ensure accuracy. The currently version one of the robot exits, which can be seen in the video.

The image system is designed to be added onto the trials robot and provide illumination and image capture and processing. Key elements of the imaging and analysis mode is the ability for several parallel embedded systems to identify and image the plants and perform any necessary analysis. The images can then be stored locally and the analyzed data can be sent back over the communications link.

As this trial’s robot development is a prototype currently in development, off-the-shelf development boards are critical to enable the development of the imaging system in a reasonable timescale and risk.

There is a range of boards that I had been considering for this application; however, Edge Impulse recently sent me a Sony Spresense board, which not only has a built-in GPS antenna but is able to connect to a 5MP camera. It’s very compact, which will allow the deployment of multiple systems without challenging the power budget.

Coupled with the recently announced support for the Sony Spresense by Edge Impulse, I thought it would make a good development board to use for initial testing.

We can use the camera to capture images of the crops, while the GPS can be used to identify the location of the crop in question. Thanks to Edge Impulse, we are also able to run ML algorithms on the Sony Spresense to identify the crop correctly or to detect if there is weed or other unauthorized crop growing.

With that in mind I set out to develop a ML application demonstration for use on the trials robot, which can classify the types of crops. Getting started with this is straightforward. For this demonstration, I wanted to focus on three different types of crops. For this example, I selected wheat (there are plenty of wheat fields growing by me) maize and sugar can. In the actual deployment, I envisage the ML algorithm being switched out depending on the crop in question, another reason why the ability to easily generate ML algorithms is critical to supporting potential future users.

To get started with this I needed a dataset of the different crops, which I obtained from Kaggle. Importing this dataset into Edge Impulse, I was then able to define and train a machine learning solution that can run on the Sony Spresense to classify the crops in question.

We can test out the algorithm using the test data in the live classification or capture images directly from the Sony Spresense using the Edge Impulse daemon.

What is nice about the Edge Impulse solution is the provision of a C++ library, which can be added to in the development to complete the final algorithm.

We still have a little way to go developing version two of the robot but it is in development. This project has shown the benefit edge ML can have in a real world application. If you are interested in creating a similar solution, I have written a step-by-step instructions on how to do this. They are available here , while the video below shows a walkthrough of how to create the project using the Edge Impulse Studio.

I am really looking forward to being able to try out the developed algorithm on the robot as we move towards testing in the field! - Adam Taylor

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Agricultural Robots – Present and Future Applications (Videos Included)

Kumba is an AI Analyst at Emerj, covering financial services and healthcare AI trends. She has performed research through the National Institutes of Health (NIH), is an honors graduate of Rensselaer Polytechnic Institute and a Master’s candidate in Biotechnology at Johns Hopkins University.

Artificial intelligence is gaining traction in the agricultural industry and is steadily being integrated in robotics developed for this sector. As automated technologies penetrate the market, we aim to answer the important questions that business leaders are asking today:

• Which types of AI applications are currently available in the agricultural robotics market?
• How are agricultural companies using these technologies to stay ahead of the competition?  
• What innovations have the potential to change the industry over the next decade?

In this article, we explore current and “near future” examples of agricultural robots (often called “agribots” or “ag robots”). Based on our research, most current ag robotics applications fit into the following sub-categories:

• Drones (Primarily Survailance)
• Precision Weed Control

Crop Harvesting

• Planting and Seeding

Below we have selected 8 brief examples across these four sub-categories. Each provides a snapshot of how AI and robotics are converging within the agriculture industry. We’ve broken down the examples below into “current applications” (those that seem to be in use already), and “emerging applications” (those which show promise but seem farther from a robust business ROI).

Current Agribot Applications

Blue river technology – weed control.

Herbicide resistance has become a primary concern for stakeholders in the agricultural industry. The increasing use of herbicides has contributed to herbicide resistance which has been documented in 250 species of weeds .  The Weed Science Society of America recently concluded that herbicide resistant weeds have been responsible for approximately $43 billion worth of financial losses for American farmers.

In response to the continued challenges that weeds are presenting to farmers, Blue River Technology has launched a weed spraying machine. The See & Spray robot has been marketed as a safer solution to herbicide resistance with claims that it significantly reduces the the crops’ exposure to chemicals.

The video below explores the robot’s functions in more detail:

In September 2017, noted agricultural company John Deere announced its acquisition of Blue River Technology for a reported $305 million.  

Harvest CROO Robotics – Crop Harvesting

The U.S. Bureau of Labor Statistics has recently reported an anticipated loss of six percent of the agricultural workforce nationwide. In response to this trend, Harvest CROO Robotics has introduced a strawberry picking machine. In a 24-hour time span, the robot is reportedly capable of picking strawberries across eight acres of land.

With a reported 40 percent of nationwide farm costs going to wages and other labor costs, companies engaged in efforts to implement AI into agriculture are on the rise. The video below shows the Harvest CROO Robot in action:

The company claims that the machine’s general workload capacity is comparable to thirty human farm workers. Based on the U.S. 2016 median annual wage for one agricultural worker , 30 workers would amount to approximately $676,200 in annual costs. Harvest CROO does not publish the cost of its robot on its website. However, if we consider the price tag of a competing strawberry picking robot at $50,000 , it is plausible that the Harvest CROO robot could provide cost savings.

Agribotix – Drones

Colorado-based Agribotix reportedly takes agricultural data captured by drones and conducts analyses using cloud-based software to help clients increase crop yields and profits. According to the Agribotix website, the company claims that it has experience with more than 44 crops and its clientele base spans over 45 countries. The one minute video below provides a demonstration of a drone in action:

In one case study , Agribotix claims its technology helped a soybean grower prevent damage to its crops from weeds and avoided a 13 percent crop loss. The reported ROI amounted to $7,222.00, which included savings from crop loss avoidance and a precise herbicide application.

In May 2017, Agribotix announced its involvement in a partnership with The Climate Corporation to expand the Corporation’s suite of digital tools by integrating the ability to capture high resolution aerial images.

For example, as depicted in the image below from the company’s website, these aerial images provide a contrasting view of the terrain, highlighting which areas are healthy and which require attention. Theoretically, the information derived from these images would allow more efficient budgeting and planning of farming and harvesting procedures.

(We cover a few additional companies and use-cases for agricultural drones in our “ Industrial Drone Applications ” and “ Machine Vision for Satellite Imagery ” articles, respectively.)

Emerging and Future Applications

Vision robotics – planting and seeding.

Vision Robotics ’ technology reportedly integrates algorithms with sensor technology to bring automation to lettuce farming and vineyards. Specifically, computer vision allows robots to generate 3D maps and models of areas of interest and then to complete various tasks within those parameters.

For example, “thinning” is a process in farming where seeds are adequately spaced apart during planting to allow for optimal crop growth. It can also be a time-consuming process. Vision Robotics’ automates the lettuce thinning and results are shown in the example image below:

Future initiatives appear to include the development of precision weed removal technology using herbacids and the company is actively seeking strategic partners. Vision Robotics has not published an anticipated timeline for this effort.

In a 2017 survey conducted by the The Produce Marketing Association, apples and strawberries ranked 2nd and 4th place among the top 20 fruits and vegetable sold in the U.S.  

Shibiya Seiki – Strawberry Harvesting

In 2013, Japanese company Shibiya Seiki made headlines with the announcement of a $50,000 robot reportedly capable of picking strawberries. Results from a research paper published by the company reported that the machine showed “potential for practical use” – and is clearly not in use with businesses today. It has been estimated that it takes a human 10 to 15 minutes to pick a quart of ripe strawberries , and – judging by the video below – this robot seems rather far from beating human performance in the near term:

The greenhouse setup for Shibiya Seiki’s research was customized, complete with a hanging strawberry garden setup with a particular density of fruit and a particular set of distances between plant beds. While the machine likely would be of little use in a normal (read: un-instrumented) berry farm setting, this application may help generate ideas about the super-efficient farm setups of the future (optimized for machine performance, not human performance).

The robot was developed in collaboration with Japan’s National Agriculture Food Research Organization (NARO). NARO’s current research efforts appear to include the development of fully-automated farms . It is unclear exactly when the organization expects to realize its ambitious effort.

Octinion – Strawberry Harvesting

R&D company Octinion ’s strawberry harvesting robot is slated to officially debut in 2018. In comparison to Shibiya Seiki’s strawberry picking robot, Octinion emphasizes the ability of its robot to prevent damage to crops. The prototype was reportedly developed two years prior as demonstrated in the video below:

Like the Shibiya Seiki strawberry robot in the previous example, Octinion’s application seems to operate within a special farm layout with hanging fruit and neat rows at a specific picking level. We might imagine that fruit at various levels (or in different layouts) might be more challenging for the machine.

According to its website, Octinion claims its autonomous robot is developed with 3D computer vision which gives it the ability to only pick strawberries that it won’t bruise; this equates to picking an estimated 70 percent of all ripe strawberries.

The company reports that human labor results in ~16 kg of strawberries picked and sorted per hour (with about 56 strawberries per kg), equating to about one strawberry every four seconds. Comparatively, Octonion claims that its robot “has the capacity to pick one strawberry every three seconds.” Based on the video of the current robot (above), this speed seems rather exaggerated, and it seems obvious that many more iterations and improvements will be required before this machine reaches human skill in picking and sorting.

Octinion does not presently feature any case studies or real-world applications of it’s technologies on it’s website, nor is there evidence of current paying customers (though this information might be kept secret).

FF Robotics – Apple Harvesting

Historically, apple harvesting has been conducted manually. In 2012, an estimated 4.2 million apples were picked in the U.S. Israel-based FF Robotics has reportedly been working to perfect an automated apple-picking robot as demonstrated in the 2.5 minute video below:

While the company showcases instances of media coverage on its website, it is unclear what clients have invested in the technology to date.

Abundant Robotics – Apple Harvesting

California-based competitor Abundant Robotics is reportedly aiming to bring its commercial apple harvester to market by 2018 . Co-founders Dan Steere and Curt Salisbury explain their vacuum-inspired technology in the video below:

In May 2017, Venture Capitalist firm GV (also known as Google Ventures) was reportedly a lead investor in a $10 million Series A investment in Abundant Robotics. However, it is difficult to grasp an idea of the experience of Abundant Robotics clients. The company’s website does not appear to provide client testimonials or case studies.

Concluding Thoughts on Agricultural Robots

Agricultural robots or agribots are changing the look, feel and pace of traditional farming practices. Crop harvesting is poised to significantly impact the agricultural sector over the next decade. Essentially, where consumer demand and labour requirements are the greatest, automation will prove most useful. For example, roughly 320,000 acres of apples are grown in the U.S. annually and each acre requires between 250 and 350 man-hours per acre .

However, it is important to keep in mind that there will be a learning curve as these technologies improve in their sensitivity and operation capacity. The industry appears to be inching towards large-scale efforts, so robot developers will need to keep this growing trend in mind.

Drone technology has reportedly demonstrated some promising results as in the case of Agribotix. In comparison to other robotic categories where ROI is scarce, the company’s openness to publish hard figures may reflect the sustainability of the this technology (we regularly publish reputable case studies of successful AI implementations in business, and we’ve found such published case studies to be relatively rare in the nascent world of agricultural robots).

Advances in drone research will be critical for maintaining safety considerations. Agriculture authorities and investors alike will require proof effectiveness and safety.  We can anticipate more drone applications to attempt to come to market in the coming decade.

Barring adverse weather, drones seem to have the easiest chance of becoming a commonplace agricultural application because – unlike their grounded counterparts – they don’t need to navigate the unpredictable and messy world of sold and product.

Grounded machines require dexterity in interacting with plants safely, while drones can survey a domain and record image data without requiring the same kind of significant progress needed for other robotics applications (case in point: the dexterity and discernment needed to pick strawberries at human speed is a much harder problem than simply flying over crops). We suspect that drones will be among the first widely adopted agricultural robots in the near term.

While fully autonomous robots are a goal of many players in the agribot space, this should not overshadow the important role that humans will play in quality management. The evolving roles of robots and human laborers may potentially usher in new job roles that are more technical in nature.

AI agribots are poised to present new opportunities for the agricultural sector and we anticipate increased implementation across multiple areas of the industry.

Header image credit: Popular Mechanics

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A Survey of Robotic Harvesting Systems and Enabling Technologies

• Review Paper
• Open access
• Published: 27 January 2023
• Volume 107 , article number  21 , ( 2023 )

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• Leonidas Droukas 1 ,
• Zoe Doulgeri 1 ,
• Nikolaos L. Tsakiridis 1 ,
• Dimitra Triantafyllou 2 ,
• Ioannis Kleitsiotis 2 ,
• Ioannis Mariolis 2 ,
• Dimitrios Giakoumis 2 ,
• Dimitrios Tzovaras 2 ,
• Dimitrios Kateris 3 &
• Dionysis Bochtis 3  

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This paper presents a comprehensive review of ground agricultural robotic systems and applications with special focus on harvesting that span research and commercial products and results, as well as their enabling technologies. The majority of literature concerns the development of crop detection, field navigation via vision and their related challenges. Health monitoring, yield estimation, water status inspection, seed planting and weed removal are frequently encountered tasks. Regarding robotic harvesting, apples, strawberries, tomatoes and sweet peppers are mainly the crops considered in publications, research projects and commercial products. The reported harvesting agricultural robotic solutions, typically consist of a mobile platform, a single robotic arm/manipulator and various navigation/vision systems. This paper reviews reported development of specific functionalities and hardware, typically required by an operating agricultural robot harvester; they include (a) vision systems, (b) motion planning/navigation methodologies (for the robotic platform and/or arm), (c) Human-Robot-Interaction (HRI) strategies with 3D visualization, (d) system operation planning & grasping strategies and (e) robotic end-effector/gripper design. Clearly, automated agriculture and specifically autonomous harvesting via robotic systems is a research area that remains wide open, offering several challenges where new contributions can be made.

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The Vision-Based Target Recognition, Localization, and Control for Harvesting Robots: A Review

Jingfan Liu & Zhaobing Liu

Harvesting Robots for Smart Agriculture

Automation in Agriculture

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Open access funding provided by HEAL-Link Greece. This research received funding from the European Community’s Framework Programme Horizon 2020 under grant agreement No 871704, project BACCHUS.

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Conceptualization - Leonidas Droukas, Zoe Doulgeri; Literature search - Leonidas Droukas, Nikolaos L. Tsakiridis, Dimitra Triantafyllou, Ioannis Kleitsiotis and Dimitrios Kateris; Writing/original draft preparation - Leonidas Droukas, Nikolaos L. Tsakiridis, Dimitra Triantafyllou, Ioannis Kleitsiotis and Dimitrios Kateris; Writing/review and editing/critical revision - Zoe Doulgeri, Ioannis Mariolis, Dimitrios Giakoumis, Dimitrios Tzovaras and Dionysis Bochtis.

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Droukas, L., Doulgeri, Z., Tsakiridis, N.L. et al. A Survey of Robotic Harvesting Systems and Enabling Technologies. J Intell Robot Syst 107 , 21 (2023). https://doi.org/10.1007/s10846-022-01793-z

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DOI : https://doi.org/10.1007/s10846-022-01793-z

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