Identification and Classification of Major Wheat Diseases Insights from Convolutional Neural Network Analysis

The cultivation of wheat, as a fundamental cereal crop, is confronted with substantial challenges posed by a range of diseases, which have a profound influence on agricultural output and the overall stability of food supply. This study introduces a new methodology that utilizes Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) to achieve precise diagnosis and classification of wheat illnesses by analyzing leaf photos. The research starts with a rigorous phase of gathering data, performing preprocessing tasks, and extracting features. This approach aims to improve the dataset's quality and facilitate the construction of successful models. The methodology integrates a hybrid framework that combines CNN architecture with SVM algorithms, resulting in a shown superiority in illness prediction when compared to conventional approaches. The CNN architecture, which consists of several convolutional layers and a fully connected layer, demonstrated exceptional performance in extracting complex information from pictures of wheat leaves. This capability significantly contributed to the accurate diagnosis of diseases affecting the plants.