NAAS Journal

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P-ISSN: 2618-060X, E-ISSN: 2618-0618   |   NAAS: 5.20

2025, Vol. 8, Special Issue 1

Integration of machine learning and remote sensing in crop yield prediction: A review


Anil Kumar, Indraveer Singh, Mohit Kashyap, Ashutosh Kumar, Ningombam Bijaya Devi, Shreya Singh, Shaina Sharma and Rahul Pradhan

Increasing demand for accurate crop yield predictions in agriculture has been fueled by technological innovation. Machine Learning (ML) and Remote Sensing (RS) have become leading tools for precision and scalability in predictions. This review discusses the present state of the integration of ML and RS, bringing out methodologies, datasets, applications, and challenges in predicting crop yields. It has thus proven to show considerable promise for agricultural decision-making, resource use optimization, and improvement in food security through synergies between ML algorithms and RS data. The future trends and potential advancement are also discussed below.
Pages : 549-562 | 107 Views | 61 Downloads
How to cite this article:
Anil Kumar, Indraveer Singh, Mohit Kashyap, Ashutosh Kumar, Ningombam Bijaya Devi, Shreya Singh, Shaina Sharma, Rahul Pradhan. Integration of machine learning and remote sensing in crop yield prediction: A review. Int J Res Agron 2025;8(1S):549-562. DOI: 10.33545/2618060X.2025.v8.i1Sh.2496
Call for book chapter