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Assessing Agricultural Land and Determining Crop Suitability for Optimum Yield using Machine Learning Approach

KOIRANGA, Abdullahi Hammanadama Dr. Yusuf Musa Malgwi HAMIDU, Mohammed

Abstract

Machine Learning has developed rapidly and increasingly becoming significant in agricultural domain as there is need of efficient logical practice for detecting new and valuable information in agricultural domain. Agriculture serves as the primary economic driver and major employment provider for the majority of the population in Nigeria. However, the sector is vulnerable to challenges stemming from inadequate agronomic practices. This study built an Agricultural land assessment model that predict optimum yield using logistic regression model and Support Vector Machine (SVM) classifiers using Python and VScode. Comparing Logistic Regression and Support Vector Machine (SVM) for classifying crops based on their nutrient content and yield, both algorithms exhibit high overall accuracy of around 87%. However, SVM stands out due to its consistently high precision, recall, and F1-scores across various crops. This indicates that SVM provides a more balanced and robust performance, vital for accurate agricultural predictions. The study also introduces a novel application of machine learning techniques to the field of agriculture and bridges the gap between traditional agricultural practices and modern data analytics, fostering innovation and improving agricultural efficiency and sustainability. The study can further enhance its impact on agricultural productivity and sustainability by Incorporate Internet of Things (IoT) devices to collect real-time data on soil moisture, temperature, and other environmental factors, promote improving the accuracy of crop suitability predictions. Also to extend the model to include a wider variety of crops and explore its applicability to different climatic and geographical regions, ensuring broader relevance and utility

Keywords

AssessingAgricultural LandCrop SuitabilityOptimum YieldMachine Learning

References

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