Wei Cheng, Shan Yifeng, Zhen MengZhe
Chongqing Business Vocational College, Chongqing, China.
Ningbo University of Finance and Economics, School of Basic Education, Ningbo, Zhejiang, China.
Front Plant Sci. 2025 May 14;16:1576756. doi: 10.3389/fpls.2025.1576756. eCollection 2025.
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.
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.
Experimental evaluations demonstrate the effectiveness of our approach in detecting anomalies and improving decision-making in precision agriculture.
This framework sets a new standard for sustainable and data-driven crop protection strategies.
精准农业依靠先进技术来优化作物保护和资源利用，确保可持续和高效的耕作方式。异常检测在识别和解决可能对产量产生负面影响的异常情况（如害虫爆发、疾病传播或养分缺乏）方面发挥着关键作用。传统方法难以应对从不同来源收集的农业数据的复杂性和变异性。
为应对这些挑战，我们提出了一个新颖的框架，该框架将集成多模态智能农业网络（IMSFNet）与自适应资源优化策略（AROS）相结合。IMSFNet采用多模态数据融合和时空建模，通过利用来自无人机、卫星、地面传感器和气象站的数据，对作物健康状况和产量异常进行准确预测。AROS基于实时环境反馈和多目标优化动态优化资源分配，平衡产量最大化、成本效益和环境可持续性。
实验评估证明了我们的方法在检测异常和改善精准农业决策方面的有效性。
该框架为可持续和数据驱动的作物保护策略树立了新的标准。
