Towards Automated Strawberry Harvesting: Identifying the Picking Point

Huang, Zhuoling; Wane, Sam; Parsons, Simon

doi:10.1007/978-3-319-64107-2_18

Cited by 17 publications

(9 citation statements)

References 7 publications

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“…For strawberry detection, image processing based on color thresholding is a frequently applied method in research papers (Hayashi et al, ; Yamamoto et al, ), primarily due to the significant differences of color among ripe strawberries, green strawberries, and green plants. Peduncle detection is another widely researched harvesting step (Cui et al, ; Hayashi et al, ; Huang, Wane, & Parsons, ; Shiigi et al, ). Color‐based image processing methods were used to detect the strawberry first and then set a certain region above the strawberry for peduncle detection, with the accuracy influenced by the results of preprocessing and complexity of the environment.…”

Section: Related Workmentioning

confidence: 99%

“…Peduncle detection is another widely researched harvesting step (Cui et al, 2013;Hayashi et al, 2010;Huang, Wane, & Parsons, 2017;Shiigi et al, 2008). Color-based image processing methods were used to detect the strawberry first and then set a certain region above the strawberry for peduncle detection, with the accuracy influenced by the results of preprocessing and complexity of the environment.…”

Section: Fruit Identificationmentioning

confidence: 99%

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An autonomous strawberry‐harvesting robot: Design, development, integration, and field evaluation

Xiong

Grimstad

et al. 2019

Journal of Field Robotics

240

133

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This paper presents an autonomous robot capable of picking strawberries continuously in polytunnels. Robotic harvesting in cluttered and unstructured environment remains a challenge. A novel obstacle‐separation algorithm was proposed to enable the harvesting system to pick strawberries that are located in clusters. The algorithm uses the gripper to push aside surrounding leaves, strawberries, and other obstacles. We present the theoretical method to generate pushing paths based on the surrounding obstacles. In addition to manipulation, an improved vision system is more resilient to lighting variations, which was developed based on the modeling of color against light intensity. Further, a low‐cost dual‐arm system was developed with an optimized harvesting sequence that increases its efficiency and minimizes the risk of collision. Improvements were also made to the existing gripper to enable the robot to pick directly into a market punnet, thereby eliminating the need for repacking. During tests on a strawberry farm, the robots first‐attempt success rate for picking partially surrounded or isolated strawberries ranged from 50% to 97.1%, depending on the growth situations. Upon an additional attempt, the pick success rate increased to a range of 75–100%. In the field tests, the system was not able to pick a target that was entirely surrounded by obstacles. This failure was attributed to limitations in the vision system as well as insufficient dexterity in the grippers. However, the picking speed improved upon previous systems, taking just 6.1 s for manipulation operation in the one‐arm mode and 4.6 s in the two‐arm mode.

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Section: Related Workmentioning

confidence: 99%

Section: Fruit Identificationmentioning

confidence: 99%

An autonomous strawberry‐harvesting robot: Design, development, integration, and field evaluation

Xiong

Grimstad

et al. 2019

Journal of Field Robotics

240

133

View full text Add to dashboard Cite

show abstract

“…A broad overview of the grippers used, together with a detailed description of the effects when using the vacuum gripper, is presented in [ 27 ]. As the position of the strawberry stem (collection point) is difficult to detect [ 29 , 30 ], especially within the bush habitat, scissor heads require a relatively complicated vision system solution. It is also easy to cut more than one petiole and accidentally destroy the green fruit of the strawberry plant.…”

Section: Introductionmentioning

confidence: 99%

The Concept of the Constructional Solution of the Working Section of a Robot for Harvesting Strawberries

Kurpaska

Bielecki

Sobol

et al. 2021

Sensors

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Strawberry fruits are products of high commercial and consumption value, and, at the same time, they are difficult to harvest due to their very low mechanical strength and difficulties in identifying them within the bush. Therefore, robots collecting strawberries should be equipped with four subsystems: a video object detection system, a collecting arm, a unit for the reception and possible packaging of the fruit, and a traction system unit. This paper presents a concept for the design and operation of the working section of a harvester for strawberry fruit crops grown in rows or beds, in open fields, and/or under cover. In principle, the working section of the combine should meet parameters comparable with those of manually harvested strawberries (efficiency, quality of harvested fruit) and minimise contamination in the harvested product. In order to meet these requirements, in the presented design concept, it was assumed that these activities would be performed during harvesting with the natural distribution of fruits within the strawberry bush, and the operation of the working head arm maneuvering in the vicinity of the picked fruit, the fruit receiving unit, and other obstacles was developed on the basis of image analysis, initially general, and in detail in the final phase. The paper also discusses the idea of a vision system in which the algorithm used has been positively tested to identify the shapes of objects, and due to the similarity of space, it can be successfully used for the correct location of strawberry fruit.

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“…So far, several types of end effectors have been developed for harvesting strawberries, such as similar to scissors [3], knives with a suction device [8], as well as grippers with adjustable gripping force [2]. Because the position of the strawberry stem (picking point) is difficult to detect [6,7], especially those in clusters, the scissor end effectors require a relatively difficult vision system solution. It is also easy to cut more than one stem and accidentally destroy the green strawberry fruit.…”

Section: Introductionmentioning

confidence: 99%

The concept of a construction solution of a robot for harvesting strawberries

et al. 2019

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Strawberry fruit products are of high commercial and consumption value, while difficult to harvest due to their very low mechanical strength and difficulties in identifying them within the plants. Therefore, robots harvesting strawberries should connect four subsystems: vision of detection, delivery arm (manipulator), effector (harvesting head), and finally-a platform increasing the working space adapted to the size of the farm. The presented work of the conceptual working section of a combine for harvesting strawberry fruit from crops, carried out in rows or cultivation ridge, from cultivation on field and/ or under covers will meet the requirements for: work productivity, quality of harvested fruit, reduction of the amount of pollution. To requirements have been met, the developed concept of constructions adopted the principle of operation during the first phase of the harvesting (in the natural distribution of fruit within the plants of strawberries) and the working of the work arm head (based on image analysis, initially general, and in the last phase of detailed) maneuvering in surrounded by harvested fruit and machine.

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Towards Automated Strawberry Harvesting: Identifying the Picking Point

Cited by 17 publications

References 7 publications

An autonomous strawberry‐harvesting robot: Design, development, integration, and field evaluation

An autonomous strawberry‐harvesting robot: Design, development, integration, and field evaluation

The Concept of the Constructional Solution of the Working Section of a Robot for Harvesting Strawberries

The concept of a construction solution of a robot for harvesting strawberries

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