Machine Learning Techniques for Crop Yield Forecasting in Semi-Arid (3A) Zone, Rajasthan (India)

Suresh Kumar Sharma^1,3*, Durga Prasad Sharma² and Kiran Gaur³

¹MSRDC- Maharishi Arvind Institute of Science and Management, Jaipur under RTU Kota, Rajasthan. India. and Sri Karan Narendra Agriculture University, Jobner, Rajasthan, India.

²MSRDC-MAISM (RTU), Research Centre Jaipur and AMUIT, MOEFDRE under UNDP, Ethiopia.

³Sri Karan Narendra Agriculture University, Jobner, Rajasthan, India.

Corresponding Author E-mail: suresh.cs@sknau.ac.in

DOI : http://dx.doi.org/10.12944/CARJ.11.3.19

Abstract:

Economic growth and prosperity of a nation are inextricably linked to the agricultural sector. In the compass of agriculture, climate and other environmental changes are one of the main challenges. The present study attempts to predict crop yield for the Jaipur district which is an important region in the semi-arid eastern plain of Rajasthan (India).  Machine learning (ML) techniques are used in forecasting and developing practical solutions for numerous challenges such as climate change with other environmental factors. Crop yield prediction is the process of predicting yield using historical data through meteorological parameters and past yield records. This paper used the agrometeorological time-series data from the year 1991 to 2020 for optimal yield forecasting. There have been numerous attempts to improve crop yield prediction by employing machine learning techniques. However, in this study, fusing the intelligence of reinforcement with deep learning, we got a comprehensive framework for mapping raw data to crop prediction values, allowing an optimal estimation of crop yields with higher accuracy. Upon comparative analysis of numerous ML algorithms, Random Forest is found the best-performing algorithm with an accuracy of 92.3% using supervised machine learning methods. With an accuracy of 92.3%, the proposed Random Forest-based model outperforms other techniques that are currently being used to predict crop yields. The study predictions could significantly help in choosing the best cropping pattern and planning for action accordingly. The results provide the best ways to solve environmental and agricultural problems in this semi-arid region of the specified Rajasthan state facing climate change issues.

Keywords:

Forecasting; Deep Learning; Machine Learning; Logistic Regression; Random Forest

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