Enhanced Rice Leaf DISEASE Detection and Categorization Using Hybrid Convolutional Vision Transformer (CVT) Model with Spatial Attention
Keywords:
Agriculture, Convolutional Neural Network, Hybrid CVT, Rice leaf disease, Vision TransformerAbstract
Diagnosis and classification of disease in rice leaf are essential in countering diseases in crops and enabling agricultural sustainability. Conventional methods that mostly involve manual checks are constrained by the intensity of labor, inconsistency and sensitivity to errors. The proposed paper presents a Hybrid Convolutional Vision Transformer (CVT) with Spatial Attention (SA) to improve the accuracy of detection and reliability of the classification in rice leaves. The suggested CVT architecture combines a Convolutional Neural Network (CNN), which serves as the basis of the first features extraction with a Vision Transformer (ViT) in the advanced feature representations. Convolutional Neural Network learns the most critical texture and shape, whereas Vision Transformer learns the attention over patches of an image and effectively learns the complicated spatial relationship required to recognize disease-specific features under a wide variety of field conditions. In addition, SA module further optimizes the model by giving more weight to diseased areas to eliminate interference by non-leafbackground areas. Examples with rice leafdataset show that the hybrid CVT with SA model reaches an average feature extraction error of over 98.12, a 98.56, and 98.26 feature extraction error and classification error respectively on dataset 1 and dataset 2, respectively, and utilizing multiple rice leaf classes, as opposed to baseline CNN and ViT models. In the decision making process, Spatial Attention uses heat maps to come up with a significant location that increases the interpretation of the model. This CVT hybridized system offers an extensible platform of rice leaf disease detection and classification that can be added into security in agriculture, like drone cameras to investigate remote fields. The model demonstrated is better performing than any other existing approach.
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