Research on Tomato Leaf Disease Classification Based on Machine Learning

Timely and accurate detection of plant diseases is an ongoing challenge in agriculture. The occurrence of tomato leaf diseases can affect the normal growth of tomatoes, leading to a reduction in quality and yield, resulting in significant economic losses. Traditional manual identification methods are time consuming and labour intensive, while traditional computer vision identification methods lack portability. Conducting research on tomato leaf disease classification based on machine learning can effectively alleviate the shortcomings of manual classification and traditional image classification methods in practical applications. It can improve the classification efficiency and accuracy, reduce the labour cost, realize the timely detection, identification and solution of diseases, reduce the misuse of pesticides, improve the tomato yield, and meet the national demand for green agriculture. Therefore, it is of great significance. Using deep learning technology to classify and identify tomato leaf diseases, we realised the classification of 10 tomato leaf diseases, while improving the classification efficiency and accuracy.