NeuroRF FarmSense: IoT-fueled precision agriculture transformed for superior crop care

In light of the ongoing global hunger crisis, it is imperative to improve food production in accordance with Sustainable Development Goal 2.0, which aims to eliminate hunger while promoting sustainable agricultural practices. This research presents a novel Internet of Things (IoT)-driven crop management system, NeuroRF FarmSense, specifically designed for precision agriculture. By utilizing soil sensors and a robust IoT framework, this system enables effective data collection across vast and remote agricultural areas. The study utilizes an extensive Crop Recommendation Dataset obtained from Kaggle, which includes 2,200 entries and seven critical attributes essential for crop selection: phosphorus, humidity, potassium, temperature, nitrogen, pH, and rainfall. This dataset provides a detailed methodology for crop recommendations, revealing more than 22 alternative crops based on varying characteristics. For agricultural forecasting, the NeuroRF FarmSense system employs the NeuroRF Classifier, which integrates neural networks (NN) with the Random Forest Classifier, achieving an unprecedented accuracy of 99.82%, exceeding prior records. This integrative approach harnesses the advantages of NN’s ReLU activation and dropout regularization alongside the robustness of RF. By utilizing NN predictions as input features for RF training and refining RF through grid search with cross-validation, the ensemble model produces highly precise predictions, facilitating strategic crop cultivation for optimal yields across diverse environmental conditions. This innovative methodology signifies a strong solution for classification challenges in precision agriculture. By merging IoT technology with machine learning algorithms, smart farming is poised to enter a transformative phase, providing a scalable response to the pressing issues of global food security. This research aspires to advance precision agriculture in harmony with global sustainability objectives.