Automatic Cauliflower Disease Detection Using Fine-Tuning Transfer Learning Approach

Plants are a major food source worldwide, and to provide a healthy crop yield, they must be protected from diseases. However, checking each plant to detect and classify every type of disease can be time-consuming and would require enormous expert manual labor. These difficulties can be solved using deep learning techniques and algorithms. It can check diseased crops and even categorize the type of disease at a very early stage to prevent its further spread to other crops. This paper proposed a deep-learning approach to detect and classify cauliflower diseases. Several deep learning architectures were experimented on our selected dataset VegNet, a novel dataset containing 656 cauliflower images categorized into four classes: downy mildew, black rot, bacterial spot rot, and healthy. We analyzed the results conducted, and the best test accuracy reached was 99.25% with an F1-Score of 0.993 by NASNetMobile architecture, outperforming many other neural networks and displaying the model’s efficiency for plant disease detection.