Abstract
This paper describes the machine learning strategies to the crop yield prediction in agriculture. Agriculture is the important occupation in India. The demand for crop production increases more for future generations, so prediction of crop yield becomes more important. The research was conducted on Telangana state to predict the crop yield. The main purpose of the study is to apply K-nearest neighbor and support vector machine algorithms for prediction of jowar yield. These machine learning (ML) algorithms are implemented in MATLAB and the results are verified. The parameters used in the dataset are soil moisture, humidity, temperature, and rainfall. From the comparison of algorithms, the best accurate model for prediction of crop yield is suggested. Support vector machine model gave the better accuracy comparatively.
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Pavani, S., Augusta Sophy Beulet, P. (2022). Prediction of Jowar Crop Yield Using K-Nearest Neighbor and Support Vector Machine Algorithms. In: Sivasubramanian, A., Shastry, P.N., Hong, P.C. (eds) Futuristic Communication and Network Technologies. VICFCNT 2020. Lecture Notes in Electrical Engineering, vol 792. Springer, Singapore. https://doi.org/10.1007/978-981-16-4625-6_49
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DOI: https://doi.org/10.1007/978-981-16-4625-6_49
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