Deep Learning-Driven Precision Agriculture: Enhancing Crop Recommendation and Soil Analysis through IoT Sensor Data

Abstract

This research introduces an innovative approach to precision agriculture, focusing on empowering farmers through advanced technologies. The study utilizes a fusion of cutting-edge deep learning algorithms, including CNNs, CapsNets, GRUs, LSTMs, and an AdaBoostClassifier integrated with GRU, to optimize crop recommendations and soil analysis. By analyzing a comprehensive dataset from IoT sensors that measure critical agricultural parameters like soil moisture, temperature, pH levels, and nutrient content, the research uncovers intricate relationships within these factors. The integration of spatial features, complex soil patterns, and capturing temporal dynamics achieves a remarkable 99% accuracy in crop recommendation. This integration of deep learning techniques with IoT sensor data offers farmers an advanced framework for decision-making based on soil conditions, enhancing agricultural productivity and promoting sustainable farming practices. The research represents a significant advancement in precision agriculture, bridging advanced science with traditional farming wisdom for the benefit of farmers and global agriculture.