A Season-Aware Ensemble Bagged Machine Learning Approach for Crop Recommendation Based on user- Defined Month Input

Agricultural productivity is significantly impacted by a number of factors, including seasonal patterns, the quality of the soil, and the climate. Within the scope of this study, we proposed a crop recommender system that takes into account the seasons by employing a collection of machine learning techniques that are tailored to accept monthly inputs according to the requirements of the user. The user-provided month (1–12) is dynamically mapped to its associated season (in this case, winter, spring, summer, or autumn), and the system then combines this seasonal data with environmental elements in order to make recommendations on the top three crops that are best suited for that particular time period. In order to create a bagged ensemble model, the fundamental methodology involves training and testing a number of different supervised learning models. These models include Decision Tree, Random Forest, Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Deep Neural Networks, and a custom ensemble voting classifier that makes use of XGBoost and AdaBoost. In order to determine the effectiveness of these models, they were evaluated on a curated dataset of agricultural data using performance indicators such as accuracy, F1 score, and area under the curve (AUC). The results of the experiments demonstrate that ensemble models, such as the bagged combination of XGBoost and AdaBoost, perform more effectively than single classifiers, resulting in higher precision in crop recommendation. The incorporation of seasonal context enhances the flexibility of models and synchronizes agricultural planning with the cycles of nature. Using this technology, the real-world application of ensemble bagging methods in precision agriculture is demonstrated, hence providing support for agricultural strategies that are both data-intensive and environmentally sustainable.