Classification of Strawberry Fruit Shape by Machine Learning

Ishikawa, Tsubasa; Hayashi, Atsushi; Nagamatsu, Shiro; Kyutoku, Yasushi; Dan, Ippeita; Wada, Takahiro; Oku, Koichiro; Saeki, Yasutake; Uto, Tomoyuki; Tanabata, Takanari; Isobe, Sachiko; Kochi, Nobuo

doi:10.5194/isprs-archives-xlii-2-463-2018

Cited by 46 publications

(42 citation statements)

References 20 publications

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“…We report for the first time a robust method to measure strawberry shape uniformity and apply this technique to generate genetic markers for uniformity traits. Several studies have attempted to quantify strawberry fruit shape using 2D images with neural networks 48 , 3D imaging 49 and by machine learning 15 . However, none of these studies investigated berry uniformity.…”

Section: Discussionmentioning

confidence: 99%

Defining strawberry shape uniformity using 3D imaging and genetic mapping

Li¹,

Cockerton²,

Johnson³

et al. 2020

Hortic Res

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Strawberry shape uniformity is a complex trait, influenced by multiple genetic and environmental components. To complicate matters further, the phenotypic assessment of strawberry uniformity is confounded by the difficulty of quantifying geometric parameters 'by eye' and variation between assessors. An in-depth genetic analysis of strawberry uniformity has not been undertaken to date, due to the lack of accurate and objective data. Nonetheless, uniformity remains one of the most important fruit quality selection criteria for the development of a new variety. In this study, a 3D-imaging approach was developed to characterise berry shape uniformity. We show that circularity of the maximum circumference had the closest predictive relationship with the manual uniformity score. Combining five or six automated metrics provided the best predictive model, indicating that human assessment of uniformity is highly complex. Furthermore, visual assessment of strawberry fruit quality in a multi-parental QTL mapping population has allowed the identification of genetic components controlling uniformity. A "regular shape" QTL was identified and found to be associated with three uniformity metrics. The QTL was present across a wide array of germplasm, indicating a potential candidate for marker-assisted breeding, while the potential to implement genomic selection is explored. A greater understanding of berry uniformity has been achieved through the study of the relative impact of automated metrics on human perceived uniformity. Furthermore, the comprehensive definition of strawberry shape uniformity using 3D imaging tools has allowed precision phenotyping, which has improved the accuracy of trait quantification and unlocked the ability to accurately select for uniform berries.

show abstract

Section: Discussionmentioning

confidence: 99%

Defining strawberry shape uniformity using 3D imaging and genetic mapping

Li¹,

Cockerton²,

Johnson³

et al. 2020

Hortic Res

View full text Add to dashboard Cite

show abstract

“…We report for the first time a robust method to measure strawberry uniformity and apply this technique to generate genetic markers for uniformity traits. Several studies have attempted to quantify strawberry fruit shape using 2D images with neural networks 44 , 3D imaging 45 and by machine learning 15 . However, none of these studies investigated berry uniformity.…”

Section: Discussionmentioning

confidence: 99%

Defining Strawberry Uniformity using 3D Imaging and Genetic Mapping

Cockerton²,

Johnson³

et al. 2020

Preprint

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Strawberry uniformity is a complex trait, influenced by multiple genetic and environmental components. To complicate matters further, the phenotypic assessment of strawberry uniformity is confounded by the difficulty of quantifying geometric parameters ‘by eye’ and variation between assessors. An in-depth genetic analysis of strawberry uniformity has not been undertaken to date, due to the lack of accurate and objective data. Nonetheless, uniformity remains one of the most important fruit quality selection criteria for the development of a new variety. In this study, a 3D-imaging approach was developed to characterise berry uniformity. We show that circularity of the maximum circumference had the closest predictive relationship with the manual uniformity score. Combining five or six automated metrics provided the best predictive model, indicating that human assessment of uniformity is highly complex. Furthermore, visual assessment of strawberry fruit quality in a multi-parental QTL mapping population has allowed the identification of genetic components controlling uniformity. A “regular shape” QTL was identified and found to be associated with three uniformity metrics. The QTL was present across a wide array of germplasm, indicating a strong candidate for marker-assisted breeding. A greater understanding of berry uniformity has been achieved through the study of the relative impact of automated metrics on human perceived uniformity. Furthermore, the comprehensive definition of strawberry uniformity using 3D imaging tools has allowed precision phenotyping, which has improved the accuracy of trait quantification. This tool has allowed us to illustrate the use of advanced image analysis towards the breeding of greater uniformity in strawberry.

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“…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. Other researchers have explored feature learning methods to analyze strawberry fruit shapes (Ishikawa et al, ). Recently, extensive work used deep learning as an approach for fruit detection.…”

Section: Related Workmentioning

confidence: 99%

“…Other researchers have explored feature learning methods to analyze strawberry fruit shapes (Ishikawa et al, 2018). Recently, extensive work used deep learning as an approach for fruit detection.…”

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

289

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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Classification of Strawberry Fruit Shape by Machine Learning

Cited by 46 publications

References 20 publications

Defining strawberry shape uniformity using 3D imaging and genetic mapping

Defining strawberry shape uniformity using 3D imaging and genetic mapping

Defining Strawberry Uniformity using 3D Imaging and Genetic Mapping

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

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