Abstract
Agriculture has a significant impact on a nation's economic growth and development. It is important to consider the impact of plant diseases and pests on the quality of crops. Cauliflower is a major winter crop in terms of both area and production. However, some serious diseases can harm cauliflower plants if proper care is not taken, resulting in lower yields and quality. The major concern is planting disease monitoring by hand, which is extremely time-consuming and labour-intensive. Automatic disease detection using computer vision is becoming increasingly popular. Plant disease prediction methods (PDPT) are critical for combating this issue and sharing disease preventative and treatment information with farmers. Therefore, different deep learning techniques are discussed for predicting diseases in cauliflower plants. Deep learning techniques are the best approaches for solving this problem. Various approaches in deep learning techniques are discussed and the survey is taken for effective analysis of cauliflower plants with disease prediction using IoT and deep learning technique. This research analyzed several recent literatures that are used to predict and analyze diseases in different plants. This study outlines the various protocols that affect the cauliflower plants and the ways to overcome all the difficulties in the form of disease prediction. Deep learning techniques have studied pests and illnesses of cauliflower since the plant's lifecycle began.
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Meenalochini, M., Amudha, P. (2023). Cauliflower Plant Disease Prediction Using Deep Learning Techniques. In: Tripathi, A.K., Anand, D., Nagar, A.K. (eds) Proceedings of World Conference on Artificial Intelligence: Advances and Applications. WWCA 1997. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-5881-8_14
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DOI: https://doi.org/10.1007/978-981-99-5881-8_14
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