Recognize and classify illnesses on tomato leaves using EfficientNet's transfer learning approach with different size dataset

This study focuses on the remarkable progress made by the agricultural sector in utilizing image processing techniques for early detection and classification of leaf plant diseases. Timely identification of diseases is crucial, but it often poses a challenge for the human eye to discern subtle differences. To address this issue, the researchers propose a novel approach that employs EfficientNet, a deep learning model, to accurately recognize various diseases affecting tomato plant leaves. Transfer learning is applied to three different datasets comprising 3000, 8000, and 10,000 images of diseased tomato leaves. The experimental results demonstrate impressive overall accuracies of 97.3%, 99.2%, and 99.5% when using 3000, 8000, and 10,000 images, respectively, for the detection of common tomato plant diseases. We also compare our proposed method with ANN, CNN, Random Forest and LightGBM. This research underscores the effectiveness of image processing and deep learning techniques in achieving precise and efficient detection of tomato leaf diseases. It significantly contributes to the advancement of precision agriculture and enhanced crop management practices.