IIARD International Institute of Academic Research and Development

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY (IJCSMT )

E-ISSN 2545-5699
P-ISSN 2695-1924
VOL. 10 NO. 6 2024
DOI: 10.56201/ijcsmt.v10.no6.2024.pg119.132

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Machine Learning Approach Leveraging Crop Datasets for Optimizing Crop Yield Production

Bulus Bali, Zakawa N. Ngida, Isacha Habila

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Abstract

Crop yield prediction based on environmental, soil, water, and weather parameters has become a vital area of research, addressing the growing need for sustainable agricultural practices and food security. This study adopts a machine learning approach to optimize crop yield production within the agricultural landscape of Michika Local Government Area, Adamawa State. Applying extensive crop datasets, machine learning techniques are utilized to analyze, interpret, and uncover critical factors and patterns influencing crop yields. The main objective is to develop a robust predictive model that empowers farmers and stakeholders with actionable insights, to enhance agricultural productivity. The results demonstrate a substantial improvement in yield predicting accuracy through machine learning-based methods compared to traditional approaches. ANN with the lowest RMSE (3136.8), the lowest MAE (2502.2), and higher R² (0.073255), indicates the most accurate predictions. These findings underscore the transformative potential of artificial intelligence in advancing precision agriculture, enabling resource-efficient farming, and bolstering food security. This study also highlights avenues for future research, including optimizing resource allocation strategies, identifying resilient crop varieties, predicting and mitigating crop diseases, and mapping soil suitability for diverse crops. Such efforts would further drive the adoption of smart agricultural systems, enhancing productivity and sustainability while supporting the transition to climate-resilient farming practices.

keywords:

Crop datasets; Machine Learning; Predictive modeling; Crop yield; Weather

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