Deep learning-based anomaly detection for precision field crop protection

Abstract

Introduction: Precision agriculture relies on advanced technologies to optimize crop protection and resource utilization, ensuring sustainable and efficient farming practices. Anomaly detection plays a critical role in identifying and addressing irregularities, such as pest outbreaks, disease spread, or nutrient deficiencies, that can negatively impact yield. Traditional methods struggle with the complexity and variability of agricultural data collected from diverse sources. Methods: To address these challenges, we propose a novel framework that integrates the Integrated Multi-Modal Smart Farming Network (IMSFNet) with the Adaptive Resource Optimization Strategy (AROS). IMSFNet employs multimodal data fusion and spatiotemporal modeling to provide accurate predictions of crop health and yield anomalies by leveraging data from UAVs, satellites, ground sensors, and weather stations. AROS dynamically optimizes resource allocation based on real-time environmental feedback and multi-objective optimization, balancing yield maximization, cost efficiency, and environmental sustainability. Results: Experimental evaluations demonstrate the effectiveness of our approach in detecting anomalies and improving decision-making in precision agriculture. Discussion: This framework sets a new standard for sustainable and data-driven crop protection strategies.