Time and Space Efficient Multi-Model Convolution Vision Transformer for Tomato Disease Detection from Leaf Images with Varied Backgrounds

A consumption of 46.9 million tons of processed tomatoes was reported in 2022 which is merely 20% of the total consumption. An increase of 3.3% in consumption is predicted from 2024 to 2032. Tomatoes are also rich in iron, potassium, antioxidant lycopene, vitamins A, C and K which are important for preventing cancer, and maintaining blood pressure and glucose levels. Thus, tomatoes are globally important due to their widespread usage and nutritional value. To face the high demand for tomatoes, it is mandatory to investigate the causes of crop loss and minimize them. Diseases are one of the major causes that adversely affect crop yield and degrade the quality of the tomato fruit. This leads to financial losses and affects the livelihood of farmers. Therefore, automatic disease detection at any stage of the tomato plant is a critical issue. Deep learning models introduced in the literature show promising results, but the models are difficult to implement on handheld devices such as mobile phones due to high computational costs and a large number of parameters. Also, most of the models proposed so far work efficiently for images with plain backgrounds where a clear demarcation exists between the background and leaf region. Moreover, the existing techniques lack in recognizing multiple diseases on the same leaf. To address these concerns, we introduce a customized deep learning-based convolution vision transformer model. The model achieves an accuracy of 93.51% for classifying tomato leaf images with plain as well as complex backgrounds into 13 categories. It requires a space storage of merely 5.8 MB which is 98.93%, 98.33%, and 92.64% less than state-of-the-art visual geometry group, vision transformers, and convolution vision transformer models, respectively. Its training time of 44 min is 51.12%, 74.12%, and 57.7% lower than the above-mentioned models. Thus, it can be deployed on (Internet of Things) IoT-enabled devices, drones, or mobile devices to assist farmers in the real-time monitoring of tomato crops. The periodic monitoring promotes timely action to prevent the spread of diseases and reduce crop loss.