Design of Machine Learning Solutions to Post-Harvest Classification of Vegetal Species

This paper presents a machine learning approach to automatically classifying post-harvest vegetal species. Color images of vegetal species were applied to convolutional neural networks (CNNs) and support vector machine (SVM) classifiers. We focused on okra as the target vegetal species and classifie...

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Bibliographic Details
Published in:AgriEngineering 2023-06, Vol.5 (2), p.1005-1019
Main Authors: Diop, Papa Moussa, Oshiro, Naoki, Nakamura, Morikazu, Takamoto, Jin, Nakamura, Yuji
Format: Article
Language:English
Subjects:
Accuracy
Agriculture
Algorithms
Artificial neural networks
Classification
Color imagery
color space
convolutional neural network (CNN)
Crop diseases
Datasets
Design
Farmers
Harvest
Image processing
Learning algorithms
Machine learning
Neural networks
Okra
Plant diseases
Saturation (color)
Species
support vector machine (SVM)
Support vector machines
vegetal classification
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Summary:This paper presents a machine learning approach to automatically classifying post-harvest vegetal species. Color images of vegetal species were applied to convolutional neural networks (CNNs) and support vector machine (SVM) classifiers. We focused on okra as the target vegetal species and classified it into two quality types. However, our approach could also be applied to other species. The machine learning solution consists of several components, and each design process and its combinations are essential for classification quality. Therefore, we carefully investigated their effects on classification accuracy. Through our experimental evaluation, we confirmed the following: (1) in color space selection, HLG (hue, lightness, and green) and HSL (hue, saturation, and lightness) are essential for vegetal species; (2) suitable preprocessing techniques are required owing to the complexity of the data and noise load; and (3) the diversity extension of learning image data by mixing different datasets obtained under different conditions is quite effective in reducing the overfitting possibility. The results of this study will assist AI practitioners in the design and development of post-harvest classifications based on machine learning.
ISSN:2624-7402
2624-7402
DOI:10.3390/agriengineering5020063