Exploring of Enhanced Sustainable Agriculture for Leaf Disease Detection Using Machine Learning

Abstract

Plants play a vital role in providing food on a global scale. Several environmental factors contribute to the occurrence of plant leaf diseases, leading to substantial reductions in crop yields. Nevertheless, the process of manually detecting plant leaf diseases is both time-consuming and detection to errors. However, despite these applications, several gaps in plant leaf disease research still need to be addressed for efficient disease detection. This study presents an exploring of enhanced sustainable agriculture for leaf disease detection using machine learning. This proposed system uses tomato leaf disease dataset from Kaggle. The dataset undergoes data pre-processing, image splitting, and data augmentation to enhance its detection. The discriminative attributes from leaf images are used to extract and selection of features. The extracted features are used to train and test tomato leaves classification into their respective disease categories by using machine learning classifier. The potential of machine learning as a sustainable tool for precision agriculture demonstrates efficiency and accuracy for identification of tomato leaf diseases. The results show proposed model can effectively support farmers in early disease diagnosis. It reduces dependence on manual inspection in promoting data-driven agricultural practices.