Abstract: The agricultural sector has become data-driven in recent years to increase agricultural yields, improve resource use, and promote sustainable agriculture. This essay describes an overview of how a Crop Recommendation System using Machine Learning was developed to assist farmers in making the optimal crop choice in relation to climatic and soil conditions. The model draws upon an open-source database of soil nutrient concentrations (nitrogen, phosphorus, potassium), temperature, humidity, pH, and rain to predict the best crop for the input conditions. Preprocessing methods were used to clean and normalize the data so that the model could be trained to be reliable. Various machine learning algorithms, such as Decision Trees, Random Forests, Support Vector Machines, and K-nearest neighbors, were trained and cross-validated to identify the most appropriate model. With heavy training, testing, and tuning for hyperparameters, the top-performing model was a Random Forest classifier, with impressive performance in accuracy, precision, recall, and F1-score measures. The system suggests crops in real-time, enabling farmers to make decisions based on weather and soil type. This increases its efficiency and decreases the overuse of fertilizers and water, thereby saving the environment. The study illustrates the potential of machine learning in transforming traditional agriculture. In order to develop a holistic system, the future can witness additional variables such as market forces, pest presence, and satellite imagery. The method discussed here is essential to realizing artificial intelligence-based sustainable agriculture.
Keywords: Crop Recommendation System, Machine Learning, Random Forest Classifier, Precision Agriculture, Sustainable Farming
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