Deep Learning-Based Detection of Early Blight in Potato Leaves Using CNN Architectures

Abstract

Early blight, caused by the fungus Alternaria solani, is a prevalent disease in potato crops that severely impacts yield and quality. Traditional detection methods are time-consuming, require expert knowledge, and depend on laboratory facilities. This study aims to develop an efficient and automated approach for detecting early blight in potato leaves using deep learning techniques. A deep learning-based software solution was created, utilizing a convolutional neural network (CNN) trained on a large, annotated dataset of potato leaf images showing various disease symptoms. Five widely used CNN architectures (ResNet, NasNet, MobileNet, VGG16, InceptionNet) were implemented and compared within a consistent MATLAB environment. The comparative analysis revealed differences in model performance, offering valuable insights into the suitability of each architecture for real-time disease detection on different devices. The study demonstrates that CNN-based models can effectively and automatically detect early blight in potato leaves, with certain architectures offering better adaptability and accuracy for practical, field-level applications.

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