Cauliflower Disease Identification Using Attention-Based Lightweight Deep Transfer Learning

Cauliflower is a winter crop which is known for health benefits and form a major part of the diet. During cultivation, cauliflower plants are at time affected by certain diseases like downy mildew, black rot, and bacterial spot rot, which may severely impact the production. Manual monitoring of the cultivation becomes infeasible, thereby necessitating disease recognition of cauliflower images by exploring machine/deep learning paradigms. This paper presents a novel approach for identifying diseases in cauliflower using an attention-based lightweight deep transfer learning model. Leveraging the VegNet dataset, the proposed system is evaluated and scores an accuracy of 98.04% thereby outperforming the accuracies achieved by some recent standard methods.