An Ultra Lightweight Interpretable Convolution-Vision Transformer Fusion Model for Plant Disease Identification: ConViTX
Poornima Singh Thakur, Shubhangi Chaturvedi, Ayan Seal, Pritee Khanna, Tanuja Sheorey, Aparajita Ojha
Abstract
Plant diseases have been detrimental for the agriculture industry, as they cause substantial crop loss globally. To overcome this, IoT and AI-based smart agriculture solutions are being deployed for plant disease detection. However, a diverse range of crops and their diseases pose enormous challenges to these methods. Additionally, limited generalizability and the black-box nature of existing deep learning models, together with the scarcity of in-field datasets, are the main bottlenecks in developing efficient and acceptable solutions for large-scale applications. In the present work, a lightweight model 'ConViTX' is proposed for plant disease classification that demonstrates improved generalizability and explainability. The compact architecture of ConViTX uses a fusion of convolutional neural networks and vision transformers to simultaneously capture local and global features. Remarkably, ConViTX outperforms nine state-of-the-art deep learning methods on four publicly available datasets and a self-collected in-field maize dataset. Furthermore, the model demonstrates explainable prediction through Gradient Weighted Class Activation Maps and Locally Interpretable Model-Agnostic Evaluations. ConViTX attains 98.8% accuracy on the maize dataset and 61.42% on drone camera-captured raw images. With only 0.7 million parameters and 0.647 billion operations per second, the proposed model has the potential for deployment on resource-constrained precision agriculture setups.