Open Access

ARTICLE

AI Method for Improving Crop Yield Prediction Accuracy Using ANN

T. Sivaranjani^1,*, S. P. Vimal²

1 Department of Electrical and Electronics Engineering, V.S.B College of Engineering Technical Campus, Coimbatore, 642109, India
2 Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, Coimbatore, 641022, India

* Corresponding Author: T. Sivaranjani. Email:

Computer Systems Science and Engineering 2023, 47(1), 153-170. https://doi.org/10.32604/csse.2023.036724

Received 10 October 2022; Accepted 06 January 2023; Issue published 26 May 2023

Abstract

Crop Yield Prediction (CYP) is critical to world food production. Food safety is a top priority for policymakers. They rely on reliable CYP to make import and export decisions that must be fulfilled before launching an agricultural business. Crop Yield (CY) is a complex variable influenced by multiple factors, including genotype, environment, and their interactions. CYP is a significant agrarian issue. However, CYP is the main task due to many composite factors, such as climatic conditions and soil characteristics. Machine Learning (ML) is a powerful tool for supporting CYP decisions, including decision support on which crops to grow in a specific season. Generally, Artificial Neural Networks (ANN) are usually used to predict the behaviour of complex non-linear models. As a result, this research paper attempts to determine the correlations between climatic variables, soil nutrients, and CY with the available data. In ANN, three methods, Levenberg-Marquardt (LM), Bayesian regularisation (BR), and scaled conjugate gradient (SCG), are used to train the neural network (NN) model and then compared to determine prediction accuracy. The performance measures of the training, as declared above, such as Mean Squared Error (MSE) and correlation coefficient (R), were determined to assess the ANN models that had been built. The experimental study proves that LM training algorithms are better, while BR and SCG have minimal performance.

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Keywords

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