Cultivating Prosperity: A Fusion of IoT Data with Machine Learning and Deep Learning for Precision Crop Recommendations

The selection of crops in precision agriculture poses challenges, affecting food security and economic stability due to factors like sub-optimal yields and resource wastage stemming from variables such as soil composition and climate. These challenges underscore the importance of holistic solutions. Utilizing an set of sensors, data collection becomes an indispensable cornerstone for ongoing research. This data gathering consti-tutes a pivotal element in facilitating exhaustive analysis and supporting informed decision-making within the agricultural domain. Addressing the problem at hand, the paper proposes a multilayered approach that combines homogeneous and heterogeneous ensemble learning models, alongside integrating deep learning architectures. It introduces powerful ensemble learning strategies such as RandomForestClassifier, GradientBoostingClassifier, XGBClassifier, Support Vector Classifier (SVC), MLPClassifier, Logistic Regression, and a variety of other models. Through this ensemble approach, it leverages the strengths of these models to enhance the accuracy and robustness of crop recommendations. Additionally, the study delves into the realm of deep learning, employing Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and RNN-LSTM models, which are finely tuned to capture subtle data patterns essential for accurate crop yield prediction. Our study carefully evaluates the performance of these models through rigorous cross-validation and fusion techniques, highlighting their transformative potential to revolutionize crop recommendation practices. By providing data-driven insights to farmers, we aim to pave the way for sustainable agriculture and improved food security in an ever-evolving agricultural landscape.