Authors : Shital Sapkal; Priya Taware; Dr. J.S. Rangole; Harshal Borate

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/5ha5yjud

DOI : https://doi.org/10.38124/ijisrt/25may536

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Agricultural productivity has a key role in ensuring food availability, promoting economic resilience, and supporting the long-term well-being of rural communities. This work focuses on harnessing machine learning to better crop production prediction by assessing critical agricultural characteristics like precipitation, temperature, humidity, pH, and nutritional levels of the soil. The expanding population and associated growth in food demand provide considerable challenges to agricultural productivity, demanding creative technology solutions to optimize land management and boost crop yield.The proposed approach leverages machine learning models trained on agricultural datasets, including a Kaggle dataset for crop recommendations. Numerous classification methods are used, including K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Extra Tree Classifier (ETC), and boost predicted accuracy. By incorporating machine learning into farming methods, the system intends to improve judgment in relation to irrigation, fertilization, and crop choice. This research emphasizes the potential of AI-driven solutions to solve important concerns in agriculture, including soil degradation, water shortages, and insect control. The results illustrate the ability of machine learning to boost agricultural efficiency, decrease risks, enhance food security, and encourage sustainable farming methods.

Keywords : Crop Yield Prediction, Artificial Intelligence, Machine Learning, Agriculture, Review.

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