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Crop Yield Prediction Using Machine Learning Approaches on a Wide Spectrum

S. Vinson Joshua1, A. Selwin Mich Priyadharson1, Raju Kannadasan2, Arfat Ahmad Khan3, Worawat Lawanont3,*, Faizan Ahmed Khan4, Ateeq Ur Rehman5, Muhammad Junaid Ali6

1 Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, 600062, India
2 Department of Electrical and Electronics Engineering, Sri Venkateswara College of Engineering, Sriperumbudur, 602117, India
3 Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand
4 University of Central Punjab, Lahore, 54000, Pakistan
5 Government College University, Lahore, 54000, Pakistan
6 Virtual University of Pakistan, Islamabad Campus, 45550, Pakistan

* Corresponding Author: Worawat Lawanont. Email: email

Computers, Materials & Continua 2022, 72(3), 5663-5679.


The exponential growth of population in developing countries like India should focus on innovative technologies in the Agricultural process to meet the future crisis. One of the vital tasks is the crop yield prediction at its early stage; because it forms one of the most challenging tasks in precision agriculture as it demands a deep understanding of the growth pattern with the highly nonlinear parameters. Environmental parameters like rainfall, temperature, humidity, and management practices like fertilizers, pesticides, irrigation are very dynamic in approach and vary from field to field. In the proposed work, the data were collected from paddy fields of 28 districts in wide spectrum of Tamilnadu over a period of 18 years. The Statistical model Multi Linear Regression was used as a benchmark for crop yield prediction, which yielded an accuracy of 82% owing to its wide ranging input data. Therefore, machine learning models are developed to obtain improved accuracy, namely Back Propagation Neural Network (BPNN), Support Vector Machine, and General Regression Neural Networks with the given data set. Results show that GRNN has greater accuracy of 97% (R² = 0.97) with a normalized mean square error (NMSE) of 0.03. Hence GRNN can be used for crop yield prediction in diversified geographical fields.


Cite This Article

APA Style
Joshua, S.V., Priyadharson, A.S.M., Kannadasan, R., Khan, A.A., Lawanont, W. et al. (2022). Crop yield prediction using machine learning approaches on a wide spectrum. Computers, Materials & Continua, 72(3), 5663-5679.
Vancouver Style
Joshua SV, Priyadharson ASM, Kannadasan R, Khan AA, Lawanont W, Khan FA, et al. Crop yield prediction using machine learning approaches on a wide spectrum. Comput Mater Contin. 2022;72(3):5663-5679
IEEE Style
S.V. Joshua et al., "Crop Yield Prediction Using Machine Learning Approaches on a Wide Spectrum," Comput. Mater. Contin., vol. 72, no. 3, pp. 5663-5679. 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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