Abstract
Agriculture expansion is critical to the economic prosperity of any country. Agriculture employs more than 60% of the Indian population, either directly or indirectly. Nowadays, monitoring the crop is the challenging task in the world. In this article, data has been collected from various sensors to propose an IoT-assisted hybrid machine learning approach for obtaining an effective crop monitoring system. Crop monitoring system here means predicting as well as detecting diseases of crops. This study is about leveraging existing data and applying regression analysis, SVM, and decision tree to predict crop diseases in diverse crops such as rice, ragi, gram, potato, and onion. Among the applied methods, SVM outperforms regression, DT methods. The training and testing accuracy of Gram has 96.29% and 95.67%, respectively.
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Kumar Apat, S., Mishra, J., Srujan Raju, K., Padhy, N. (2023). IoT-Assisted Crop Monitoring Using Machine Learning Algorithms for Smart Farming. In: Kumar, R., Pattnaik, P.K., R. S. Tavares, J.M. (eds) Next Generation of Internet of Things. Lecture Notes in Networks and Systems, vol 445. Springer, Singapore. https://doi.org/10.1007/978-981-19-1412-6_1
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