Abstract
India is leading producer and an exporter country of Mango with annual production of approximately 19 tons, accounting for 40% of the world’s total production. The production has been declined in recent years due to damage from pests and other disease-causing agents, in response to find a better solution to this problem the Prediction of Disease of Mango Fruit Crop using Machine Learning and IoT’ is an advanced alerting system. The main objective is to develop a system that can forecast the attack of diseases on Mango fruit crop using past weather data and crop production. The field sensors collected live weather data to calculate disease prediction in real time. The Random Forest Regression model was trained on past weather data and used to calculate disease outbreak probability. The model showed pretty accurate results in relation to the forecasting of the disease.
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Jawade, P.B., Chaugule, D., Patil, D., Shinde, H. (2020). Disease Prediction of Mango Crop Using Machine Learning and IoT. In: Satapathy, S.C., Raju, K.S., Shyamala, K., Krishna, D.R., Favorskaya, M.N. (eds) Advances in Decision Sciences, Image Processing, Security and Computer Vision. ICETE 2019. Learning and Analytics in Intelligent Systems, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-24322-7_33
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DOI: https://doi.org/10.1007/978-3-030-24322-7_33
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