CLASSIFICATION OF STRAWBERRY FRUIT SHAPE BY MACHINE LEARNING

Abstract

Abstract. Shape is one of the most important traits of agricultural products due to its relationships with the quality, quantity, and value of the products. For strawberries, the nine types of fruit shape were defined and classified by humans based on the sampler patterns of the nine types. In this study, we tested the classification of strawberry shapes by machine learning in order to increase the accuracy of the classification, and we introduce the concept of computerization into this field. Four types of descriptors were extracted from the digital images of strawberries: (1) the Measured Values (MVs) including the length of the contour line, the area, the fruit length and width, and the fruit width/length ratio; (2) the Ellipse Similarity Index (ESI); (3) Elliptic Fourier Descriptors (EFDs), and (4) Chain Code Subtraction (CCS). We used these descriptors for the classification test along with the random forest approach, and eight of the nine shape types were classified with combinations of MVs + CCS + EFDs. CCS is a descriptor that adds human knowledge to the chain codes, and it showed higher robustness in classification than the other descriptors. Our results suggest machine learning's high ability to classify fruit shapes accurately. We will attempt to increase the classification accuracy and apply the machine learning methods to other plant species.

Plant phenotyping relevance

イチゴ果実の形状という植物器官形質をデジタル画像から抽出し、記述子と機械学習で分類する手法が研究の中心であるため。

Code and data availability

The paper describes 2,969 strawberry fruit images from a MAGIC population and custom analysis (chain codes, SHAPE, random forest via scikit-learn), but no public deposit of the image dataset, extracted descriptors, or authors' analysis code is stated. The MAFF guideline URL is a cited external regulatory document, and

No evidence-backed public reproduction asset is currently recorded.