Multi-Class CNN Models for Banana Ripeness Classification
Abstract
This study develops and evaluates Convolutional Neural Network (CNN) models for classifying banana maturity stages using images, thereby addressing a significant challenge in the banana supply chain. The banana industry represents a major agricultural sector worldwide, with Brazil exporting 56.2 thousand tons in 2023. Accurate maturity classification is essential for optimizing harvest timing, reducing postharvest losses, and extending shelf life. We utilized a public Brazilian dataset of 1,000 images of Prata Catarina banana categorized into eight ripening stages based on peel coloration standards established by the Brazilian Program for Horticulture Modernization. The images were preprocessed to a standardized 200 × 200-pixel resolution, and we evaluated the effectiveness of the data augmentation techniques, including horizontal flip, vertical flip, rotation, and zoom. Our CNN architecture consisted of five convolutional blocks with a dropout layer prior to flattening. We conducted six experiments to compare three classification scenarios (eight, five, and two ripeness classes) with and without data augmentation. Our findings demonstrate that CNN models can effectively classify banana ripeness, with performance improving significantly as classification granularity decreases. The best-performing model achieved 89.5% accuracy, 87.2% precision, and 89.6% recall when classifying bananas into two categories.
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References
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