Selective Harvesting Robotics: Current Research, Trends, and Future Directions

Kootstra, Gert; Wang, Xin; Blok, Pieter M.; Hemming, J.; Henten, Eldert van

doi:10.1007/s43154-020-00034-1

Cited by 139 publications

(82 citation statements)

References 45 publications

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“…On the other side there is an abundance of literature describing advances in open-field agricultural robotics (e.g. Bechar & Vigneault, 2017 ; Fountas et al, 2020 ; Kootstra et al, 2021 ; Mahmud et al, 2020 ; Oberti & Shapiro, 2016 ), but studies combining design processes geared towards diversified agriculture settings with automated tool specifications are noticeably lacking (Rose et al, 2021 ).…”

Section: Historical and Conceptual Backgroundmentioning

confidence: 99%

Automating Agroecology: How to Design a Farming Robot Without a Monocultural Mindset?

Ditzler

Driessen

2022

J Agric Environ Ethics

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Robots are widely expected—and pushed—to transform open-field agriculture, but these visions remain wedded to optimizing monocultural farming systems. Meanwhile there is little pull for automation from ecology-based, diversified farming realms. Noting this gap, we here explore the potential for robots to foster an agroecological approach to crop production. The research was situated in The Netherlands within the case of pixel cropping, a nascent farming method in which multiple food and service crops are planted together in diverse assemblages employing agroecological practices such as intercropping and biological pest control. Around this case we engaged with a variety of specialists in discussion groups, workshops, and design challenges to explore the potential of field robots to meet the multifaceted demands of highly diverse agroecological cropping systems. This generated a spectrum of imaginations for how automated tools might—or might not—be appropriately used, ranging from fully automated visions, to collaborative scenarios, to fully analogue prototypes. We found that automating agroecological cropping systems requires finding ways to imbue the ethos of agroecology into designed tools, thereby seeking to overcome tensions between production aims and other forms of social and ecological care. We conclude that a rethinking of automation is necessary for agroecological contexts: not as a blueprint for replacing humans, but making room for analogue and hybrid forms of agricultural work. These findings highlight a need for design processes which include a diversity of actors, involve iterative design cycles, and incorporate feedback between designers, practitioners, tools, and cropping systems.

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Section: Historical and Conceptual Backgroundmentioning

confidence: 99%

Automating Agroecology: How to Design a Farming Robot Without a Monocultural Mindset?

Ditzler

Driessen

2022

J Agric Environ Ethics

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“…Vinken built is a robot that can see underground. It emits an electric signal which identifies the vegetables [5]. A tomato harvesting system in the form of a mobile robot is capable of harvesting with manual navigation [6].…”

Section: Research Methods 21 Literature Reviewmentioning

confidence: 99%

Agricultural harvesting using integrated robot system

Raja¹,

B²,

Nagaraj³

et al. 2022

IJEECS

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In today's competitive world, robot designs are developed to simplify and improve quality wherever necessary. The rise in technology and modernization has led people from the unskilled sector to shift to the skilled sector. The agricultural sector's solution for harvesting fruits and vegetables is manual labor and a few other agro bots that are expensive and have various limitations when it comes to harvesting. Although robots present may achieve harvesting, the affordability of such designs may not be possible by small and medium-scale producers. The integrated robot system is designed to solve this problem, and when compared with the existing manual methods, this seems to be the most cost-effective, efficient, and viable solution. The robot uses deep learning for image detection, and the object is acquired using robotic manipulators. The robot uses a Cartesian and articulated configuration to perform the picking action. In the end, the robot is operated where carrots and cantaloupes were harvested. The data of the harvested crops are used to arrive at the conclusion of the robot's accuracy.

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“…From horse to tractor, robots, and intelligent vehicles, a revolutionary era has come for agriculture and food industry, from rudimentary to high efficiency of agriculture with the introduction of mechanization, innovative technologies, computerized analysis, and decision, improving farming activities and crop productivity [2]. Revolutionizing machines, often called "agribots," are now used in agriculture for all kinds of activities, namely, soil preparation, seed sowing, weed and pest treatment, irrigation, fertilization, and ultimately, grain and fruit harvesting, minimizing effort and energy cost [2,[44][45][46][47]. As a whole crop management, agricultural drones can be used starting from soil treatment (herbicide), going to sowing step, plant treatment (pesticide), and physiological control and observation, and ending with harvest time determination [2,36,[48][49][50][51].…”

Section: Artificial Intelligence Technology and Application To Improve Agriculture And Food Industriesmentioning

confidence: 99%

Artificial Intelligence to Improve the Food and Agriculture Sector

Ayed

Hanana

2021

Journal of Food Quality

143

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The world population is expected to reach over 9 billion by 2050, which will require an increase in agricultural and food production by 70% to fit the need, a serious challenge for the agri-food industry. Such requirement, in a context of resources scarcity, climate change, COVID-19 pandemic, and very harsh socioeconomic conjecture, is difficult to fulfill without the intervention of computational tools and forecasting strategy. Hereby, we report the importance of artificial intelligence and machine learning as a predictive multidisciplinary approach integration to improve the food and agriculture sector, yet with some limitations that should be considered by stakeholders.

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Selective Harvesting Robotics: Current Research, Trends, and Future Directions

Cited by 139 publications

References 45 publications

Automating Agroecology: How to Design a Farming Robot Without a Monocultural Mindset?

Automating Agroecology: How to Design a Farming Robot Without a Monocultural Mindset?

Agricultural harvesting using integrated robot system

Artificial Intelligence to Improve the Food and Agriculture Sector

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