Universal Journal of Agricultural Research Vol. 10(3), pp. 288 - 303
DOI: 10.13189/ujar.2022.100310
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A Comprehensive Review on Machine Learning Approaches for Yield Prediction Using Essential Soil Nutrients

R. Prabavathi ^*, Balika J Chelliah
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, India

ABSTRACT

Agriculture is the backbone of India's economy, as it is the most important factor in the country's socio-economic development. Because of the rapid expansion in human population, the "Green Revolution" introduced high yield variety (HYV) seeds, which increased crop productivity but degraded crop and soil quality. This is due to the use of excessive amounts of chemical fertilizers in HYV seeds, as well as the irrigation system utilized to grow these seeds. This stunts the growth of the crops, resulting in financial and productivity losses. Because of field surveys, traditional ways to crop production prediction will take longer, and contemporary agriculture will face certain obstacles. As a result, a comprehensive review of various crop key factors such as climatic factors, soil nutrients, production factors, and environmental factors is conducted using a variety of machine learning approaches such as Support Vector Machine, bayes classifier, decision tree, random forest, linear regression and Extreme Learning Machines. The accuracy measures such as root mean square error, coefficient of determination and mean absolute error are used for comparing the performance of the system. Based on the findings of the reviews, an intelligent and robust machine learning technique provides the optimum option for achieving (i) soil fertility, (ii) crop prediction, and (iii) yield prediction. The importance of soil variables and the amount of nutrients available in the soil for growing crops has been found, according to an examination of 51 peer-reviewed studies, to create qualitative yield prediction. Furthermore, the investigations will yield recommendations for future fertilizer research.

KEYWORDS
Green Revolution, Precision Agriculture, Machine Learning, Soil Factors, Yield Prediction

Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] R. Prabavathi , Balika J Chelliah , "A Comprehensive Review on Machine Learning Approaches for Yield Prediction Using Essential Soil Nutrients," Universal Journal of Agricultural Research, Vol. 10, No. 3, pp. 288 - 303, 2022. DOI: 10.13189/ujar.2022.100310.

(b). APA Format:
R. Prabavathi , Balika J Chelliah (2022). A Comprehensive Review on Machine Learning Approaches for Yield Prediction Using Essential Soil Nutrients. Universal Journal of Agricultural Research, 10(3), 288 - 303. DOI: 10.13189/ujar.2022.100310.