Evidence map›Paper›PMID 41853757›Full record

ArticleACS ES&T engineering2026

Modular, On-Site Solutions with Lightweight Anomaly Detection for Sustainable Nutrient Management in Agriculture.

Abigail R Cohen, Yuming Sun, Zhihao Qin, Harsh S Muriki, Zihao Xiao, Yeonju Lee, Matthew Housley, Andrew F Sharkey, Rhuanito Soranz Ferrarezi, Jing Li and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in ACS ES&T engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Abigail R CohenSchool of Civil and Environmental Engineering, Georgia Institute of Technology, North Avenue, Atlanta, Georgia 30332, United States.
Yuming SunSchool of Civil and Environmental Engineering, Georgia Institute of Technology, North Avenue, Atlanta, Georgia 30332, United States.
Zhihao QinSchool of Electrical and Computer Engineering, Georgia Institute of Technology, North Avenue, Atlanta, Georgia 30332, United States.
Harsh S MurikiSchool of Interactive Computing, Georgia Institute of Technology, North Avenue, Atlanta, Georgia 30332, United States.
Zihao XiaoSchool of Computational Science and Engineering, Georgia Institute of Technology, North Avenue, Atlanta, Georgia 30332, United States.
Yeonju LeeSchool of Industrial and Systems Engineering, Georgia Institute of Technology, North Avenue, Atlanta, Georgia 30332, United States.
Matthew HousleyDepartment of Horticulture, University of Georgia, Athens, Georgia 30602, United States.
Andrew F SharkeySchool of Civil and Environmental Engineering, Georgia Institute of Technology, North Avenue, Atlanta, Georgia 30332, United States.
Rhuanito Soranz FerrareziDepartment of Horticulture, University of Georgia, Athens, Georgia 30602, United States.
Jing LiSchool of Industrial and Systems Engineering, Georgia Institute of Technology, North Avenue, Atlanta, Georgia 30332, United States.
Lu GanSchool of Aerospace Engineering, Georgia Institute of Technology, North Avenue, Atlanta, Georgia 30332, United States.
Yongsheng ChenSchool of Civil and Environmental Engineering, Georgia Institute of Technology, North Avenue, Atlanta, Georgia 30332, United States.ORCID https://orcid.org/0000-0002-9519-2302

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 (

Indexed as

AI sustainabilitycomputer visionmultispectral imagingnondestructive plant phenotypingvision transformer

Identifiers

PMID41853757
PMCPMC12993859

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.