Development of a Stationary Robotic Strawberry Harvester with a Picking Mechanism that Approaches the Target Fruit from Below

Abstract

A stationary robotic strawberry harvester, combinable with a movable bench system, was developed. Initially, the features of strawberries, such as the visibility of fruit, the picking force required, and spectral reflectance were investigated. Subsequently, a machine-vision system was constructed, comprising units to measure position and coloration respectively. Considering the visibility of strawberry fruit, these units were optimally located to detect mature fruit and estimate their coloration. White, red, and green light-emitting diodes were installed for more accurate coloration measurement. The average error was 5.4%, and its standard deviation was 10.8%. We developed an end-effector with three functions: 1) to prevent the fruit adjoining the target fruit from being touched during the approach, 2) to force adjoining fruit away from the target fruit during the picking motion, and 3) to remove peduncles from harvested fruit, as done manually. The performance test confirmed that the stationary robotic harvester outperformed the conventional robots, namely, the harvester achieved a stable high harvesting success rate of 67.1%, and was able to successfully remove the peduncles from 88.0% of the harvested fruit.

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

“…When the 3D position of a target is obtained, its coordinates can be further utilized to instruct the movements of the manipulation. 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, ).…”

“…Strawberry production is heavily reliant on human labor, especially for harvesting (Xiong, Peng, Grimstad, From, & Isler, ). It was reported that 25% of all working hours in Japan are consumed by harvesting operations (Yamamoto, Hayashi, Yoshida, & Kobayashi, ). Strawberry producers in the Western world, particularly the United Kingdom and United States, are similarly concerned about the future availability of labor for picking, as well as about inflation in the cost of labor.…”

“…Noncontact picking might be an acceptable solution to avoid damage. Second, strawberries are small in size and tend to grow in clusters, which makes it difficult to identify and pick individual strawberries (Xiong et al, ; Yamamoto et al, ). Picking in clusters with dense obstacles is one of the main challenges for strawberry harvesting (Xiong et al, ; Yamamoto et al, ) as well as for many other crop harvesting systems, such as tomato harvesting (Yaguchi, Nagahama, Hasegawa, & Inaba, ) and sweet pepper robot (Bac et al, ).…”

Towards Automated Strawberry Harvesting: Identifying the Picking Point

“…However doing this may easily damage strawberries, since the fruit is generally softer than pineapple. Another method for picking fruit is for a robot hand, or a special cup, to hold the fruit, as described in [18]. In [18] a stationary robotic strawberry harvester was introduced, with the aim of dealing with strawberry plants which grow on a table-top or a shelf.…”

“…Another method for picking fruit is for a robot hand, or a special cup, to hold the fruit, as described in [18]. In [18] a stationary robotic strawberry harvester was introduced, with the aim of dealing with strawberry plants which grow on a table-top or a shelf. In this work, a camera detected the maturity of the fruit from the side of the bench, while another camera located the position of strawberry from below.…”