轻量异常检测助力农业营养实时管理，降低资源浪费。
Modular, On-Site Solutions with Lightweight Anomaly Detection for Sustainable Nutrient Management in Agriculture
- 分层模块化流程结合自编码器实现早期异常预警。
- 9天内对低肥组检测率达73%，能耗低于氮肥浪费能耗。
- 可部署于边缘设备，适合资源受限的智慧农业场景。
高效营养管理对作物生长和可持续资源利用（如氮、能源）至关重要。现有方法分析耗时，难以实现实时优化；成像虽能快速表型，但计算成本高，难在资源受限环境下部署。本研究提出一种灵活分层的异常检测与状态估计管道，涵盖从效率到精度的全谱系方法。基于三组施肥处理（T1-100%，T2-50%，T3-25%）的营养耗竭实验与多光谱成像（MSI），采用自编码器（AE）构建早期预警系统。进一步对比两种不同复杂度的状态估计模块：基于植被指数（VI）的随机森林（RF）与全图深度学习的视觉变换器（ViT）。结果表明，该方法实现高效率异常检测（T3样本9天后净检测率73%），能耗显著低于浪费氮肥所含能量；在磷、钙含量估计上，ViT表现更优（R² 0.61 vs. 0.58，0.48 vs. 0.35），但能耗更高。该模块化流程为边缘诊断和农业可持续实践提供了可行路径。
原文摘要 · Abstract (English)
Efficient nutrient management is critical for crop growth and sustainable resource consumption (e.g., nitrogen, energy). Current approaches require lengthy analyses, preventing real-time optimization; similarly, imaging facilitates rapid phenotyping but can be computationally intensive, preventing deployment under resource constraints. This study proposes a flexible, tiered pipeline for anomaly detection and status estimation (fresh weight, dry mass, and tissue nutrients), including a comprehensive energy analysis of approaches that span the efficiency-accuracy spectrum. Using a nutrient depletion experiment with three treatments (T1-100%, T2-50%, and T3-25% fertilizer strength) and multispectral imaging (MSI), we developed a hierarchical pipeline using an autoencoder (AE) for early warning. Further, we compared two status estimation modules of different complexity for more detailed analysis: vegetation index (VI) features with machine learning (Random Forest, RF) and raw whole-image deep learning (Vision Transformer, ViT). Results demonstrated high-efficiency anomaly detection (73% net detection of T3 samples 9 days after transplanting) at substantially lower energy than embodied energy in wasted nitrogen. The state estimation modules show trade-offs, with ViT outperforming RF on phosphorus and calcium estimation (R2 0.61 vs. 0.58, 0.48 vs. 0.35) at higher energy cost. With our modular pipeline, this work opens opportunities for edge diagnostics and practical opportunities for agricultural sustainability.
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