Classification of Cocoa Pod Maturity Using Similarity Tools on an Image Database: Comparison of Feature Extractors and Color Spaces

Kacoutchy Jean Ayikpa (), Diarra Mamadou, Pierre Gouton and Kablan Jérôme Adou
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Kacoutchy Jean Ayikpa: Laboratoire Imagerie et Vision Artificielle (ImVia), Université Bourgogne Franche-Comté, 21000 Dijon, France
Diarra Mamadou: Laboratoire de Mécanique et Information (LaMI), Université Felix Houphouët-Boigny, Abidjan 22 BP 801, Côte d’Ivoire
Pierre Gouton: Laboratoire Imagerie et Vision Artificielle (ImVia), Université Bourgogne Franche-Comté, 21000 Dijon, France
Kablan Jérôme Adou: Laboratoire de Mécanique et Information (LaMI), Université Felix Houphouët-Boigny, Abidjan 22 BP 801, Côte d’Ivoire

Data, 2023, vol. 8, issue 6, 1-24

Abstract: Côte d’Ivoire, the world’s largest cocoa producer, faces the challenge of quality production. Immature or overripe pods cannot produce quality cocoa beans, resulting in losses and an unprofitable harvest. To help farmer cooperatives determine the maturity of cocoa pods in time, our study evaluates the use of automation tools based on similarity measures. Although standard techniques, such as visual inspection and weighing, are commonly used to identify the maturity of cocoa pods, the use of automation tools based on similarity measures can improve the efficiency and accuracy of this process. We set up a database of cocoa pod images and used two feature extractors: one based on convolutional neural networks (CNN), in particular, MobileNet, and the other based on texture analysis using a gray-level co-occurrence matrix (GLCM). We evaluated the impact of different color spaces and feature extraction methods on our database. We used mathematical similarity measurement tools, such as the Euclidean distance, correlation distance, and chi-square distance, to classify cocoa pod images. Our experiments showed that the chi-square distance measurement offered the best accuracy, with a score of 99.61%, when we used GLCM as a feature extractor and the Lab color space. Using automation tools based on similarity measures can improve the efficiency and accuracy of cocoa pod maturity determination. The results of our experiments prove that the chi-square distance is the most appropriate measure of similarity for this task.

Keywords: cocoa maturity; cocoa pod; color space; GLCM; CNN; similarity measure (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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