Crop Prediction and Soil Nutrient Monitoring System Using Hybrid Deep Learning: A CNN-LSTM-Attention Framework
TL;DR
A novel hybrid deep learning framework integrating Convolutional Neural Networks, Long Short-Term Memory, and self-attention mechanisms for accurate crop prediction and real-time soil nutrient analysis is presented, significantly outperforming conventional machine learning approaches.
Abstract
Precision agriculture has emerged as a critical paradigm for sustainable food production, necessitating intelligent systems for crop yield prediction and soil nutrient monitoring. This pa-per presents a novel hybrid deep learning framework integrating Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and self-attention mechanisms for accurate crop prediction and real-time soil nutrient analysis. The proposed system employs Internet of Things (IoT) sensors for continuous monitoring of soil parameters including nitrogen (N), phosphorus (P), potassium (K), pH, moisture content, temperature, and electrical conductivity. A comprehensive dataset comprising 12,500 agricultural samples from diverse agroclimatic zones was utilized for model training and validation. The hybrid CNN-LSTM-Attention architecture achieves a prediction accuracy of 96.8% for crop classification and a mean absolute percentage error (MAPE) of 4.2% for yield prediction, significantly outperforming conventional machine learning approaches. Experimental results demonstrate that the proposed framework reduces prediction error by 23.5% compared to standalone LSTM models and 31.2% compared to traditional Random Forest classifiers. The sys-tem provides actionable recommendations for nutrient management, contributing to optimized fertilizer application and enhanced agricultural sustainability. The integration of edge computing with cloud-based analytics enables real-time decision support for farmers, achieving latency under 500 milliseconds for prediction queries.