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Agricultural Crop Recommendations Based on Productivity and Season
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Author(s): A. V. Senthil Kumar (Hindusthan College of Arts & Science, India), Aparna M. (Hindusthan College of Arts & Science, India), Amit Dutta (All India Council for Technical Education, India), Samrat Ray (IIMS, India), Hakikur Rahman (Presidency University, Bangladesh), Shadi R. Masadeh (Isra University, Jordan), Ismail Bin Musirin (Universiti Teknologi Mara, Malaysia), Manjunatha Rao L. (National Assessment and Accreditation Council, India), Suganya R. V. (VISTAS, India), Ravisankar Malladi (Koneru Lakshmaiah Education Foundation, India)and Uma N. Dulhare (Muffakham Jah College of Engineering and Technology, India)
Copyright: 2024
Pages: 16
Source title:
Advanced Computational Methods for Agri-Business Sustainability
Source Author(s)/Editor(s): Suchismita Satapathy (KIIT University (Deemed), India)and Kamalakanta Muduli (Papua New Guinea University of Technology, Papua New Guinea)
DOI: 10.4018/979-8-3693-3583-3.ch004
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Abstract
This chapter aims to develop an agricultural crop recommendation system leveraging the power of machine learning algorithms. The proposed system takes into account crop productivity and prevailing season as crucial factors in making appropriate crop suggestions. The authors proposed the SVM algorithm, which was trained and evaluated on a comprehensive dataset comprising historical agricultural data with diverse features such as climate variables, soil properties, and geographical factors. The data was further segmented based on seasonal patterns to provide crop recommendations tailored to specific timeframes. The models' performance was evaluated using standard metrics, and an ensemble approach was considered to enhance the system's robustness. Ultimately, the developed system offers farmers and agricultural experts a valuable tool for making informed decisions, optimizing crop selection, and increasing overall agricultural productivity
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