Classification of brown spot images in cocoa using artificial vision and knowledge transfer in the San Martín Region
DOI:
https://doi.org/10.55873/sns7vf68Keywords:
binary classification, computer vision, convolutional neural networks, deep learning, digital phytopathologyAbstract
Brown spot disease affects cocoa productivity and fruit quality, causing economic losses in the San Martín region. This study aimed to design a system for classifying images of cocoa fruits affected by this disease using computer vision and transfer learning under real field conditions. The research was conducted in Chazuta during 2024. A total of 3,003 images of healthy and diseased fruits were collected, segmented using Mask R-CNN, and subjected to data augmentation, resulting in a final dataset of 12,012 images. The pretrained VGG16, ResNet50, and MobileNetV2 architectures were evaluated as feature extractors and combined with support vector machines (SVM) for classification. Performance was assessed using cross-validation and statistical analyses, including Levene’s test, one-way ANOVA, and Tukey’s test. All models achieved accuracy above 98.6%; VGG16 + SVM showed the best performance, with a mean accuracy of 99.51% and statistically significant differences compared with the other models. The study concludes that the hybrid approach combining deep feature extraction and SVM classification is effective for automated cocoa brown spot detection and has potential applications in precision agriculture.
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