Machine Vision Algorithm for Robots to Harvest Strawberries in Tabletop Culture Greenhouses

Rajendra, Peter; Kondo, Naoshi; Ninomiya, Kazunori; Kamata, Junzo; Kurita, Mitsutaka; Shiigi, Tomowo; Hayashi, Shigehiko; Yoshida, Hirotaka; Kohno, Yasushi

doi:10.1016/s1881-8366(09)80023-2

Cited by 42 publications

(28 citation statements)

References 1 publication

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“…These solutions are not appropriate for iceberg lettuce. Color cues as used in (Berenstein et al, ; Cubero, Alegre, Aleixos, & Blasco, ; Foglia & Reina, ) are less useful because the lettuces appear to be a “sea of green.” Depth cues, as used in Kusumam et al () and Rajendra et al () also provide limited information because the plants and their leaves overlap and the heads are often hidden.…”

Section: State Of the Artmentioning

confidence: 99%

A field‐tested robotic harvesting system for iceberg lettuce

Birrell

Hughes

Cai

et al. 2019

Journal of Field Robotics

131

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Agriculture provides an unique opportunity for the development of robotic systems; robots must be developed which can operate in harsh conditions and in highly uncertain and unknown environments. One particular challenge is performing manipulation for autonomous robotic harvesting. This paper describes recent and current work to automate the harvesting of iceberg lettuce. Unlike many other produce, iceberg is challenging to harvest as the crop is easily damaged by handling and is very hard to detect visually. A platform called Vegebot has been developed to enable the iterative development and field testing of the solution, which comprises of a vision system, custom end effector and software. To address the harvesting challenges posed by iceberg lettuce a bespoke vision and learning system has been developed which uses two integrated convolutional neural networks to achieve classification and localization. A custom end effector has been developed to allow damage free harvesting. To allow this end effector to achieve repeatable and consistent harvesting, a control method using force feedback allows detection of the ground. The system has been tested in the field, with experimental evidence gained which demonstrates the success of the vision system to localize and classify the lettuce, and the full integrated system to harvest lettuce. This study demonstrates how existing state‐of‐the art vision approaches can be applied to agricultural robotics, and mechanical systems can be developed which leverage the environmental constraints imposed in such environments.

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Section: State Of the Artmentioning

confidence: 99%

A field‐tested robotic harvesting system for iceberg lettuce

Birrell

Hughes

Cai

et al. 2019

Journal of Field Robotics

131

View full text Add to dashboard Cite

show abstract

“…Several research projects have examined the sensing technologies to facilitate strawberry in-season management. Arima et al (2001Arima et al ( , 2003; Cui et al (2013); Dimeas et al (2014); Feng et al (2012aFeng et al ( , 2012b; Hayashi et al (2009Hayashi et al ( , 2010aHayashi et al ( , 2010bHayashi et al ( , 2012Hayashi et al ( , 2013Hayashi et al ( , 2014a; Kim et al (2008); Kondo et al (1998Kondo et al ( , 2005; Leonard et al (2013); Nagasaki et al (2013); Rajendra et al (2009Rajendra et al ( , 2011; Saenz et al (2013); Takeshita et al (2010); Tarrio et al (2006); Yamamoto et al (2008Yamamoto et al ( , 2009Yamamoto et al ( , 2014; Xie and Zhang (2006) [a] "W," "D," "H," "TR," "S," and "P" are used to represent "weed control," "disease detection," "harvesting," "transporting," "sorting," and "packing" in the purpose category, respectively. "F" and "G" are used to indicate the working environment of robots as either "field" or "greenhouse."…”

Section: In-season Managementmentioning

confidence: 99%

“…Though the majority of strawberry harvesting is still conducted manually in open field production, much research and product development has occurred on strawberry harvesting robotics, especially for greenhouse production. Various strawberry harvesting robots developed thus far follow a similar workflow, detecting mature fruits, locating desired fruit position, approaching each one, suctioning and pulling off the fruit or locating its peduncle followed by a hold and cut (Kondo et al, 2005;Yamamoto et al, 2009;Hayashi et al, 2010aHayashi et al, , 2010bHayashi et al, , 2012Rajendra et al, 2009Rajendra et al, , 2011Feng et al, 2012aFeng et al, , 2012b. Whether or not a strawberry is mature can be decided by calculating the ratio of the area covered by red pixels over non-red pixels in the strawberry image.…”

Section: Harvestingmentioning

confidence: 99%

Review of Robotic Technology for Strawberry Production

2016

Appl. Eng. Agric

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With an increasing world population in need of food and a limited amount of land for cultivation, higher efficiency in agricultural production, especially fruits and vegetables, is increasingly required. The success of agricultural production in the marketplace depends on its quality and cost. The cost of labor for crop production, harvesting, and postharvesting operations is a major portion of the overall production cost, especially for specialty crops such as strawberry. As a result, a multitude of automation technologies involving semi-autonomous and autonomous robots have been utilized, with an aim of minimizing labor costs and operation time to achieve a considerable improvement in farming efficiency and economic performance. Research and technologies for weed control, harvesting, hauling, sorting, grading, and/or packing have been generally reviewed for fruits and vegetables, yet no review has been conducted thus far specifically for robotic technology being used in strawberry production. In this article, studies on strawberry robotics and their associated automation technologies are reviewed in terms of mechanical subsystems (e.g., traveling unit, handling unit, storage unit) and electronic subsystems (e.g., sensors, computer, communication, and control). Additionally, robotic technologies being used in different stages in strawberry production operations are reviewed. The robot designs for strawberry management are also categorized in terms of purpose and environment.

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“…Apart from these basic characteristics, many researchers are engaged with developing different approaches to recognize fruits in natural backgrounds. Research work on detection of different fruits and vegetables such as apple [40][41][42][43], cherry fruit [44], cucumber [45,46], orange [47,48], tomato [49][50][51], strawberry [52,53], melon [54,55] and sweet pepper [12,[56][57][58] (Figure 4) has been undertaken. .…”

Section: Color Camera Recognition Systemmentioning

confidence: 99%

Deliberation on Design Strategies of Automatic Harvesting Systems: A Survey

Bachche

2015

Robotics

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In Asia, decreasing farmer and labor populations due to various factors is a serious problem that leads to increases in labor costs, higher harvesting input energy consumption and less resource utilization. To solve these problems, researchers are engaged in providing long term and low-tech alternatives in terms of mechanization and automation of agriculture by way of efficient, low cost and easy to use solutions. This paper reviews various design strategies in recognition and picking systems, as well as developments in fruit harvesting robots during the past 30 years in several countries. The main objectives of this paper are to gather all information on fruit harvesting robots; focus on the technical developments so far achieved in picking devices; highlight the problems still to be solved; and discuss the future prospects of fruit harvesting robots.

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Machine Vision Algorithm for Robots to Harvest Strawberries in Tabletop Culture Greenhouses

Cited by 42 publications

References 1 publication

A field‐tested robotic harvesting system for iceberg lettuce

A field‐tested robotic harvesting system for iceberg lettuce

Review of Robotic Technology for Strawberry Production

Deliberation on Design Strategies of Automatic Harvesting Systems: A Survey

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