Volume XLII-2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 463-470, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-463-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 463-470, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-463-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 May 2018

30 May 2018

CLASSIFICATION OF STRAWBERRY FRUIT SHAPE BY MACHINE LEARNING

T. Ishikawa1, A. Hayashi2, S. Nagamatsu3, Y. Kyutoku1, I. Dan1, T. Wada3, K. Oku3, Y. Saeki3, T. Uto3, T. Tanabata2, S. Isobe2, and N. Kochi1,2 T. Ishikawa et al.
  • 1Faculty of science and Engineering, Chuo University, 1-13-27, Kasuga, Bunkyo-ku, Tokyo, 112-8551, Japan
  • 2Department of Frontier Research Plant Genomics and Genetics, Kazusa DNA Research Institute, 2-6-7, Kazusa-Kamatari, Kisarazu, Chiba, 292-0818, Japan
  • 3Agro-Environment Sciences and Biotechnology Div., Fukuoka Agriculture and Forestry Research Center, 587, Yoshiki, Chikushino, Fukuoka, 818-8549, Japan

Keywords: Shape Recognition, Classification, Measurement, Chain code, Machine learning, Random Forest, Agriculture

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.