GIS-Enhanced Crop Yield Modeling with Machine Learning

Venkatesh, S.D. and Chitra, K. and Harilakshami, V.M. (2024) GIS-Enhanced Crop Yield Modeling with Machine Learning. Journal of Innovation and Technology, 2024 (37). pp. 1-7. ISSN 2805-5179

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Abstract

India, with its vast population and agrarian society, faces challenges in agricultural practices. Many farmers continue to grow the same crops repeatedly without experimenting with new varieties. To address these issues, we have developed a system using machine learning algorithms aimed at helping farmers. Our system recommends the most suitable crops for specific lands based on soil content and weather conditions. It also provides information on the appropriate type and number of fertilizers and the necessary seeds for cultivation. By using our system, farmers can diversify their crops, potentially increase their profit margins, and reduce soil pollution.

Item Type:	Article
Uncontrolled Keywords:	Classification Algorithms, Decision Tree, KNN, Machine Learning in Agriculture
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General)
Depositing User:	Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited:	04 Dec 2024 09:46
Last Modified:	04 Dec 2024 09:46
URI:	http://eprints.intimal.edu.my/id/eprint/2082

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