Dragon fruits stem disease classification using deep learning

Abstract

Dragon Fruits Stem Disease Classification is a critical task in agriculture to ensure the health and productivity of dragon fruit crops. Utilizing deep learning techniques, this study aims to accurately classify various stem diseases affecting dragon fruits, including Fresh, Maroon Spot, Sun Burn, and Wilting. We collected and labeled a diverse dataset of dragon fruit stem images from different farms and applied image processing and augmentation techniques to enhance the dataset. Several Convolutional Neural Network (CNN) architectures were implemented and evaluated, including Xception, VGG19, a custom CNN, InceptionV3, and MobileNetV2. The results demonstrated that MobileNetV2 achieved the highest accuracy of 96.17%, followed by Xception with 92.17%, and InceptionV3 with 91.50%. The custom CNN and VGG19 achieved accuracies of 85.75% and 84.83%, respectively. The high accuracy of MobileNetV2 highlights its potential for real-world deployment in resource-constrained environments. Our methodology provides a robust framework for dragon fruit stem disease classification, ensuring precise disease identification and supporting agricultural management practices.