Abstract
Loss of tomato crops due to diseases such as early blight, leaf mold, and late blight is a point of apprehension for the farmers as well as the food industry. The traditional methods lack in early disease detection, require more time, have high cost, and need expertise in plant pathology. Therefore, there is a strong need for automation of early disease detection in tomato crops. Various research groups worked on the detection and classification of diseases in tomato crops, but the early disease detection is underexplored. Also, there is a huge scope for develo** preprocessing techniques based on the type of dataset. Moreover, there is a requirement of develo** handy tools for assisting farmers in predicting the diseases at an early stage and estimating crop loss. In this manuscript, we conduct a survey of deep learning techniques employed for disease detection and classification. We discuss the architectures employed or developed, performance reported, and advantages of each approach. We also highlight the limitations of works proposed in the literature and identify the research gaps for future works.
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Gangwar, A., Dhaka, V.S., Rani, G. (2023). Role of Deep Learning Techniques in Early Disease Detection in Tomato Crop. In: Kumar, S., Hiranwal, S., Purohit, S., Prasad, M. (eds) Proceedings of International Conference on Communication and Computational Technologies. ICCCT 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-3485-0_35
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