A Comparative Analysis of Convolutional Neural Networkbased Transfer Learning Models for Plant Disease Detection (International Journal of Food and Nutritional Sciences, 2022)
Agriculture holds a significant role in the economy of many countries by providing foodsecurity, employment opportunities, and contributing to the overall economic growth of thenation. However, the spread of plant diseases can pose a significant threat to cropproduction and result in reduced yields, food scarcity, and ecological imbalances. Thecurrent manual process of diagnosing plant diseases is not only time-consuming but alsoprone to errors, relying heavily on the expertise of pathologists. To tackle this challenge,this paper presents a user-friendly and accessible machine learning-based plant diseasedetection system that uses Convolutional Neural Network (CNN) based transfer-learningmodels to detect and categorize plants as either healthy or diseased and providesinformation on the precautions for the identified disease. By comparing the performance ofthree cutting-edge CNN architectures, VGG16, ResNet50, and EfficientNetV2S, on publiclyavailable plant disease dataset, this paper aims to determine a model for detecting andclassifying various plant diseases. We found that ResNet50 achieves the highest accuracy of96.5%. This proposed system can enable timely and accurate disease detection, helpingfarmers take preventive measures and reducing the risk of crop loss.