Enhanced Agricultural Decision-Making: Machine Learning Approaches for Crop Prediction and Analysis in India

Sandeep Gupta; Abu Bakar Abdul Hamid; Tadiwa Elisha Nyamasvisva; Nitin Tyagi; Vishal Jain; Ng Khai Mun; Danish Ather

doi:10.15575/join.v10i2.1610

Authors

Sandeep Gupta Kuala Lumpur University of Science & Technology, Unipark Suria, Jalan Ikram-Uniten, Kajang, Selangor, Kuala Lumpur and Department of Computer Science & Engineering, SSCSE,Sharda University, Plot no. 32, 34 KP –III, Greater Noida 201301(U.P.), India
Abu Bakar Abdul Hamid Kuala Lumpur University of Science & Technology, Unipark Suria, Jalan Ikram-Uniten, Kajang, Selangor, Kuala Lumpur, Malaysia
Tadiwa Elisha Nyamasvisva Faculty of Engineering Science and Technology, Kuala Lumpur University of Science & Technology, Unipark Suria, Jalan Ikram-Uniten, Kajang, Selangor, Kuala Lumpur, Malaysia
Nitin Tyagi Department of CSE and Allied Branches, Accurate Institute of Management and Technology, Greater Noida, U. P.-201306, India
Vishal Jain Kuala Lumpur University of Science & Technology, Unipark Suria, Jalan Ikram-Uniten, Kajang, Selangor, Kuala Lumpur and Department of Computer Science & Engineering, School of Engineering & Technology, Vivekananda Institute of Professional Studies-Technical Campus, New Delhi, India
Ng Khai Mun Kuala Lumpur University of Science & Technology, Unipark Suria, Jalan Ikram-Uniten, Kajang, Selangor, Kuala Lumpur, Malaysia
Danish Ather Amity University, Tashkent City, Street Labzak, Building-70, 100028, Uzbekistan

DOI:

https://doi.org/10.15575/join.v10i2.1610

Keywords:

Agriculture, Crop Prediction, India, Machine Learning, Precision Farming

Abstract

This paper addresses the critical aspects of agriculture in the Indian economy and the challenges faced by this sector, including soil quality decline, unpredictable weather, and the need for efficient decision-making. It presents machine learning as a transformative approach for improved agricultural decision-making, enabling enhanced crop prediction and productivity. Machine learning (ML) algorithms are shown to effectively analyze vast datasets to generate predictive models that aid in crop selection optimization, disease outbreak prediction, and market fluctuation anticipation, thus leading to increased yields and profitability. Focusing on crop prediction, the paper discusses models leveraging historical data and advanced algorithms to forecast crop yields. Additionally, the application of machine learning in precision farming, such as optimizing fertilizer application, is explored. The paper uses a mixed-method approach on a dataset encompassing various crops and environmental parameters. In this paper the various techniques such as K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Decision Tree (DT) and Random Forest (RF) algorithms have been employed to demonstrate the utility of ML in the agricultural fields. The KNN at the value of K=4 and SVM with polynomial kernel resulted the accuracy of 0.982 and 0.989 respectively. Whereas DT and RT gave the results in terms of accuracy of 0.987 and 0.970 respectively. Overall, it can be said that all these techniques used in the present work showed the better accuracy for agricultural sustainability.

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