Multi-Task Deep Learning Framework for Anomaly Detection and Carbon Emission Prediction in Precision Agriculture

Abstract

Precision agriculture faces persistent challenges, including unpredictable weather, fluctuating carbon emissions, noisy sensor data, and security vulnerabilities, which hinder the optimization of crop yield and environmental sustainability. This study proposes a multi-task deep learning framework for precision agriculture that targets carbon emission prediction and anomaly detection using sensor and drone imagery data. Initially, the data is acquired using a testbed and stored in Interplanetary File System storage for secure transmission. The sensor-based statistical features were extracted via CoStat-Principal Component Analysis, and spatial features for drone images through the Visual Geometry Group 16 to extract vital patterns. These features are selected using Mutual Chi-squared Recursive Feature Elimination for feature refinement. Additionally, a feature fusion approach combines sensor and image data for a holistic analysis. Finally, anomaly patterns are identified through a deep autoencoder, while a feedforward neural network predicts the average carbon emission. This classified output integrates explainable artificial intelligence to provide transparent decision-making. A blockchain-based decentralized alert system is used for real-time anomaly detection. The proposed system achieves an accuracy of 99.20% in carbon emissions forecasting, 99.73% in anomaly detection, and a blockchain-triggered alerts rate of 99%, providing sustainable farming practices and a secure solution for precision agriculture.