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Deep Learning Architectures for Anomaly Detection in Precision Agriculture: A Systematic Review
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Keywords
Precision Agriculture
Deep Learning
Anomaly Detection
Convolutional Neural Networks
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Abstract
Precision agriculture has emerged as a transformative approach to optimizing crop yield, resource utilization, and environmental sustainability. Among emerging technologies, deep learning-based anomaly detection has gained prominence due to its ability to identify irregularities in complex agricultural datasets, including crop health, soil conditions, and sensor-generated data. This paper presents a systematic review of contemporary deep learning architectures applied to anomaly detection in precision agriculture. The review synthesizes studies spanning convolutional neural networks, recurrent neural networks, graph-based models, and hybrid architectures, emphasizing technical mechanisms, real-world applicability, and performance metrics. Comparative analyses reveal current strengths, limitations, and gaps in model interpretability, scalability, and integration with Internet-of-Things (IoT) frameworks. Findings indicate that while deep learning significantly enhances detection accuracy and decision-making efficiency, challenges remain in data heterogeneity, computational requirements, and domain-specific adaptation. This study consolidates theoretical foundations and empirical evidence, providing a research roadmap for deploying robust, high-performing anomaly detection systems in precision agriculture.
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