Authors : Annapurna K Salunke; Prajakta V. Kale; Geetanjali R. Kulkarni

Volume/Issue : Volume 9 - 2024, Issue 6 - June

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Google Scholar : https://tinyurl.com/msty886d

Scribd : https://tinyurl.com/y4xjvrb6

DOI : https://doi.org/10.5281/zenodo.14854520

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Abstract : Precision agriculture aims to optimize crop yield and sustainability by leveraging advanced technologies. This study investigates the application of machine learning for recommending suitable crops based on geographical factors, including soil properties, climate conditions, and topographical features. By integrating these diverse datasets, the proposed machine learning model aims to enhance the accuracy of crop recommendations. Experimental results demonstrate the model's effectiveness in capturing complex interactions between the factors, leading to improved agricultural decision- making compared to traditional methods.

Keywords : Precision Agriculture, Machine Learning, Crop Recommendation, Geographical Factors, Soil Properties, Climate Conditions, Topographical Features, Data Integration, Agricultural Decision-Making.

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7. Crop Recommender System Using Machine Learning Approach SHILPA MANGESH PANDE1 , DR. PREM KUMAR RAMESH2 , ANMOL3 , B.R AISHWARYA4 , KARUNA ROHILLA5 , KUMAR SHAURYA6 1 Associate Professor, Department of Information Science and Engineering 1 Research Scholar VTU-CMRIT-CSE Research Centre 2 Professor, Department of Computer Science and Engineering 3,4,5,6Student, Department of Computer Science and Engineering 1,2,3,4,5,6CMR Institute of Technology, Bengaluru, India and affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India EMAIL: [email protected] , [email protected] , [email protected] , [email protected] , [email protected] , [email protected]