AI-Powered Precision in Diagnosing Tomato Leaf Diseases

Abstract

Correct detection of plant diseases is critical for enhancing crop yield and quality. Conventional methods, such as visual inspection and microscopic analysis, are typically labor-intensive, subjective, and vulnerable to human error, making them infeasible for extensive monitoring. In this study, we propose a novel technique to detect tomato leaf diseases effectively and efficiently through a pipeline of four stages. First, image enhancement techniques deal with problems of illumination and noise to recover the visual details as clearly and accurately as possible. Subsequently, regions of interest (ROIs), containing possible symptoms of a disease, are accurately captured. The ROIs are then fed into K-means clustering, which can separate the leaf sections based on health and disease, allowing the diagnosis of multiple diseases. After that, a hybrid feature extraction approach taking advantage of three methods is proposed. A discrete wavelet transform (DWT) extracts hidden and abstract textures in the diseased zones by breaking down the pixel values of the images to various frequency ranges. Through spatial relation analysis of pixels, the gray level co-occurrence matrix (GLCM) is extremely valuable in delivering texture patterns in correlation with specific ailments. Principal component analysis (PCA) is a technique for dimensionality reduction, feature selection, and redundancy elimination. We collected 9014 samples from publicly available repositories; this dataset allows us to have a diverse and representative collection of tomato leaf images. The study addresses four main diseases: curl virus, bacterial spot, late blight, and Septoria spot. To rigorously evaluate the model, the dataset is split into 70%, 10%, and 20% as training, validation, and testing subsets, respectively. The proposed technique was able to achieve a fantastic accuracy of 99.97%, higher than current approaches. The high precision achieved emphasizes the promising implications of incorporating DWT, PCA, GLCM, and ANN techniques in an automated system for plant diseases, offering a powerful solution for farmers in managing crop health efficiently.