Implementation of Convolutional Neural Networks for Automated Disease Detection in Cucumber Crops

Andrea Menco Tovar, Edwin Puertas, Juan Carlos Martinez-Santos

Abstract: This study presents an automated system for detecting diseases in cucumber crops using Convolutional Neural Networks (CNN). Given the importance of agriculture and the challenges crops face due to pests and diseases, a vision-based approach is proposed to improve the early and accurate identification of diseases in cucumbers. Three CNN architectures were evaluated: Xception, VGG16, and ResNet50, using a balanced dataset of images of healthy and diseased cucumber leaves. The Xception model showed the best performance with an accuracy of 93.45% and a loss of 0.4842, surpassing the other models. Image preprocessing and transfer learning were key to achieving these results. Despite the good results, challenges were identified in accurately classifying some images, suggesting areas for future improvement. This system provides a valuable tool for farmers, enabling early detection and rapid decision-making to control diseases, which can significantly improve crop quality and yield. Future research could integrate this system with mobile technologies and drones for more efficient real-time monitoring.