Detection and Classification of Plant Diseases Using a Textural Feature Analysis and Classification System

Global agriculture faces considerable difficulties as a result of the rise of plant diseases, which necessitate innovative solutions for early and accurate detection the aim of this research is to introduce a novel approach. to the identification of plant diseases by combining the analysis of textural features with machine learning techniques. The set of data includes high-resolution images of healthy plants and various manifestations of disease, which has been preprocessed to ensure data quality and diversity. Images are analyzed using textural features such as the Gray-Level Co-occurrence Matrix (GLCM), Gabor filters and Local Binary Patterns (LBP), and Haralick features. The dimensionality of the feature space is reduced through careful feature selection, resulting in a more efficient machine learning algorithm. There are a number of potential classification algorithms, Random Forests, Support Vector Machines (SVM), and Convolutional Neural Networks (CNN). Using the preprocessed dataset, the model is trained, and parameters are fine-tuned to maximize performance. On a separate testing set, metrics to evaluate the model's competence, Metrics like F1-score, recall, accuracy, and precision are used. This system provides farmers and stakeholders with a user-friendly interface that can be deployed in real-world agricultural scenarios. Continuous improvement is facilitated through regular updates with new data, ensuring adaptability to emerging diseases. Precision agriculture is advanced by this research, which provides an effective and scalable solution for the purpose of classifying and early identification of plant diseases, ultimately assisting in timely and informed crop management decisions.