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Research Paper | Agriculture | Volume 15 Issue 6, June 2026 | Pages: 211 - 216 | India
Crop Recommendation Using Machine Learning
Abstract: The use of intelligent systems in agriculture has grown quite a bit lately, especially since farmers are dealing with more and more pressure from climate variability, less land availability, and higher productivity expectations all at once. One of the developments that looks particularly promising is the crop recommendation system, it uses machine learning to help farmers figure out what to cultivate, based on their soil nutrient makeup and the surrounding climatic situation. In this area, many methods have been explored for this reason, such as Random Forest, Support Vector Machine, Decision Tree, k-Nearest Neighbor, Artificial Neural Networks, XGBoost, LightGBM , and each tends to show different levels of performance. Still, a lot of existing systems don?t fully meet the needs that matter. In the literature you often see things like imbalanced datasets, incomplete preprocessing steps, features that are basically redundant, and weak generalization when the model is moved to new regions. To address these kinds of shortcomings, this study presents a hybrid framework that merges three complementary approaches: Synthetic Minority Oversampling Technique (SMOTE) to handle class imbalance, Random Forest for feature selection (or relevant attribute picking), and XGBoost for the real crop classification activity. A review of 27 studies was conducted to see what performs well, what fails, and where the research gaps actually sit. The input set for the proposed system consists of Nitrogen (N), Phosphorus (P), Potassium (K), soil pH, temperature, humidity, and rainfall. The overall aim is to create a crop recommendation framework that is not only accurate, but also usable in practice and scalable enough to support precision agriculture in the real world.
Keywords: Crop Recommendation, Precision Agriculture, Machine Learning, kNN, Decision Tree, Random Forest, Agricultural Decision Support System
How to Cite?: Yadwinder Singh, Dr. Amrit Kaur, "Crop Recommendation Using Machine Learning", Volume 15 Issue 6, June 2026, International Journal of Science and Research (IJSR), Pages: 211-216, https://www.ijsr.net/getabstract.php?paperid=SR26603222613, DOI: https://dx.dx.doi.org/10.21275/SR26603222613