Enhancing Plant Disease Classification through Manual CNN Hyperparameter Tuning

Abstract

Diagnosing plant diseases is a challenging task due to the complex nature of plants and the visual similarities among different species. Timely identification and classification of these diseases are crucial to prevent their spread in crops. Convolutional Neural Networks (CNN) have emerged as an advanced technology for image identification in this domain. This study explores deep neural networks and machine learning techniques to diagnose plant diseases using images of affected plants, with a specific emphasis on developing a CNN model and highlighting the importance of hyperparameters for precise results. The research involves processes such as image preprocessing, feature extraction, and classification, along with a manual exploration of diverse hyperparameter settings to evaluate the performance of the proposed CNN model trained on an openly accessible dataset. The study compares customized CNN models for the classification of plant diseases, demonstrating the feasibility of disease classification and automatic identification through machine learning-based approaches. It specifically presents a CNN model and traditional machine learning methodologies for categorizing diseases in apple and maize leaves, utilizing a dataset comprising 7023 images divided into 8 categories. The evaluation criteria indicate that the CNN achieves an impressive accuracy of approximately 98.02%.