Comparative Analysis of Fungal Infections Classification in Apple Leaves Using CNN and CNN with GLCM Features

Around 85% of the world's leading plant diseases are caused by fungal organism. These fungi possess a massive threat to food security and good harvest. Hence, Accurate and timely fungal infections classification is crucial. This paper explores the potential of deep learning architectures such as Convolutional Neural Networks (CNNs) for fungal infection classification using apple leaf images. We investigate the impact of incorporating Gray Level Co- occurrence Matrix (GLCM) features, which capture image texture, into the CNN architecture. By comparing the performance of the pure CNN and the CNN incorporating GLCM features. Based on the results, we gained the result of 82% accuracy and 81 % f1-score on pure CNN., meanwhile 49% accuracy and 47% f1-score on CNN supporting GLCM.