International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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An Extensive Review of AI Methods for Estimating Crop Yield

Author(s) Deepesh Agarwal, Dharamdas Kumar
Country India
Abstract This research aims to investigate the use of Artificial Intelligence (AI) with datasets in precision agriculture, focusing specifically on crop yield estimates. Improvements in remote sensing technologies, especially high-resolution multispectral image datasets, have revolutionized agricultural surveillance. These developments have produced significant insights regarding plant wellness, soil moisture content, and growth patterns of plants. Artificial intelligence systems, which include Machine Learning (ML) as well as Deep Learning (DL) models, can now accurately predict agricultural output by using Vegetation Indices (VIs) obtained from these photos. This study analyzes research from the last five years that uses datasets as well as artificial intelligence methodologies to predict yields for commodities like wheat, maize, and rice. Multiple artificial intelligence strategies are analyzed, including random forest models, support vector machine algorithms , neural networks using convolution (CNNs), and ensemble methods, all of which boost yield forecasts. The analysis underscores a notable disparity in methodological uniformity, as researchers use disparate VIs and AI algorithms for analogous commodities, leading to varied results. This study emphasizes the need of comprehensive comparisons and the use of uniform procedures in future research. The work underscores the vital significance of datasets as well as artificial intelligence in advancing precision agriculture, offering essential insights that future research focused on enhancing both sustainability and effectiveness in crop management using advanced predictive modeling.
Keywords Crop prediction, smart farming, AI, SVM, CNN.
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-11-06
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.62521
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E-ISSN 2582-2160
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