Modular, On-Site Solutions with Lightweight Anomaly Detection for Sustainable Nutrient Management in Agriculture
Modular, On-Site Solutions with Lightweight Anomaly Detection for Sustainable Nutrient Management in Agriculture Abigail R Cohen
ACS ES T Eng. 2026 Feb 24;6(3):1089-1105. doi: 10.1021/acsestengg.5c00635. eCollection 2026 Mar 13.
ABSTRACT
Efficient nutrient management is critical for crop growth and sustainable resource consumption (e.g., nitrogen and 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, we developed a hierarchical pipeline using an autoencoder for early warning. Further, we compared two status estimation modules of different complexity for more detailed analysis: vegetation index 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 (R 2 0.61 vs 0.58, 0.48 vs 0.35) at higher energy cost. With our modular pipeline, this work opens up opportunities for edge diagnostics and practical opportunities for agricultural sustainability.
PMID:41853757 | PMC:PMC12993859 | DOI:10.1021/acsestengg.5c00635
Efficient nutrient management is critical for crop growth and sustainable resource consumption (e.g., nitrogen and 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… [#item_author]
