Classification of Bitter gourd leaf disease using deep learning architecture ResNet50 - Artika, Artika; Al Maki, Wikky Fawwaz (Universitas Ahmad Dahlan, 2025)
The primary goal of this research is to develop a feasible and efficient method for identifying the disease and to advocate for an appropriate system that provides an early and cost-effective solution to this problem. Due to their superior computational capabilities and accuracy, computer vision and machine learning methods and techniques have garnered significant attention in recent years for classifying various leaf diseases. As a result, Resnet50 and Resnet101 were proposed in this study for the classification of bitter gourd disease. The 2490 images of bitter gourd leaves are classified into three categories: Healthy leaf, Fusarium Wilt leaf, and Yellow Mosaic leaf. The proposed ResNet50 architecture accomplished 98% accuracy with the Adam optimizer. The ResNet101 architecture achieves an average accuracy of 94% with the Adam optimizer. As a result, the proposed model can differentiate between healthy and diseased bitter gourd leaves. This research contributes to the development of methods for detecting bitter melon leaf disease using computer vision and machine learning, achieving high accuracy and supporting automatic disease diagnosis. The results can help farmers quickly and cost-effectively detect diseases early, thereby increasing agricultural productivity.