Enhancing tomato leaf disease detection with contrast-limited adaptive histogram equalization and image blending for improved machine learning accuracy

This study examines the application of Contrast Limited Adaptive Histogram Equalization (CLAHE), an advanced version of Adaptive Histogram Equalization (AHE), and image blending as a preprocessing technique for tomato leaf images to improve the accuracy of machine learning models in agricultural applications, particularly in the context of disease detection. We implemented CLAHE to normalize the contrast in tomato images and the image blending technique, thereby enhancing the visibility of key features critical for accurate analysis. The experimental results demonstrate a significant increase in the accuracy of the machine learning algorithms, with improvements of up to 4.02% compared to baseline models using standard unprocessed images. When compared to existing methods that rely solely on traditional image enhancement techniques, the CLAHE method does not involve image blending. CLAHE, combined with image blending, showed superior performance in highlighting disease symptoms, thereby leading to more accurate predictions. These findings highlight the crucial role of effective disease detection in tomato crops, as timely identification of health issues can lead to more informed management decisions and enhanced yield. By facilitating higher accuracy rates in disease detection, this research underscores the importance of advanced image preprocessing methods in developing robust machine learning solutions, ultimately enhancing decision-making processes in crop management and improving production efficiency. In this research, we conduct numerous experiments on various machine learning algorithms to identify and evaluate the algorithm that performs best in predicting diseases in tomatoes based on the provided image of a tomato leaf.