Weakly supervised learning for fine-grained plant disease classification : a hybrid approach

Abstract

Agricultural productivity is significantly impacted by plant diseases, resulting in economic losses and risks to food safety. The accurate detection of plant diseases with minimal supervision is considered crucial for the resolution of these issues, rather than having sole reliance placed on traditional methods such as manual inspections. Promising solutions for the automation of disease classification have been presented by recent advancements in computer vision, particularly those achieved through deep learning techniques. However, the requirement of large, annotated datasets is often associated with these methods, and such datasets can be both expensive and labour- intensive to obtain. This dissertation aims to design and evaluate a hybrid deep learning model capable of achieving high fine-grained plant disease classification accuracy under weak supervision with limited labelling effort. In order to increase the accuracy of fine-grained plan disease classification problems under weak supervision, a novel hybrid architecture combining ResNet50 and Vision Transformer is described. A series of experiments was executed utilizing diverse parallel hybrid configurations, and their performance was systematically compared against that of the standalone ResNet50 and Vision Transformer models. Distinct feature fusion strategies, including addition, concatenation, and weighted combinations, were employed by each model to combine the intermediate feature maps. The models were trained and assessed using the Crop Pest and Disease Detection (CCMT) dataset, and accuracies of 84.53%, 84.56%, and 86.26% were yielded, respectively. These performance metrics were exceeded in comparison to the individual models, with an accuracy of 63.56% achieved by ResNet50 and 81.88% attained by the Vision Transformer. The efficacy of the proposed hybrid architecture for fine-grained classification in scenarios characterized by minimal supervision is underscored by the results. This study provides a practical pathway toward scalable, cost-effective plant disease detection systems for real-world agricultural deployments. The intrinsic limits of current classification frameworks are successfully addressed, and this research significantly advances the field of fine-grained classification under limited supervision.