ReviewBiosensors2020
Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning.
Review in Biosensors, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of multi-mode analytics for enhanced plant stress evaluation.Frontiers in plant science · 2025Pooled it
- Hyperspectral imaging-based early stress and disease detection for sustainable crop management.Planta · 2026Review
- Reagent-Free Prediction of Free-Ammonia Toxicity in Algal Systems Using Chlorophyll Fluorescence Transients and Interpretable Sparse Regression.Environmental science & technology · 2026Article
- Plant stress early detection through a low-cost multispectral device: Toward safer and more sustainable agricultural practices.iScience · 2026Article
- Advancements and Emerging Trends in the Detection of Sweet Potato Diseases: A Comprehensive Review.ACS omega · 2026Review
- Artificial Intelligence (AI) in Detection of Abiotic Stress in Plants: A Review.Sensors (Basel, Switzerland) · 2026Review
- Low-Temperature Stress-Induced Changes in Cucumber Plants-A Near-Infrared Spectroscopy and Aquaphotomics Approach for Investigation.Sensors (Basel, Switzerland) · 2025Article
- Integrating Artificial Intelligence and Biotechnology to Enhance Cold Stress Resilience in Legumes.Plants (Basel, Switzerland) · 2025Review
- A machine-learning-powered spectral-dominant multimodal soft wearable system for long-term and early-stage diagnosis of plant stresses.Science advances · 2025Article
- Priming thermotolerance: unlocking heat resilience for climate-smart crops.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Review
- Physiological Response of Olive Trees UnderPlants (Basel, Switzerland) · 2025Article
- A comprehensive review of crop stress detection: destructive, non-destructive, and ML-based approaches.Frontiers in plant science · 2025Review
- Plant stress detection using multimodal imaging and machine learning: from leaf spectra to smartphone applications.Frontiers in plant science · 2025Article
- Portable solutions for plant pathogen diagnostics: development, usage, and future potential.Frontiers in microbiology · 2025Review
- Sensing Technologies for Outdoor/Indoor Farming.Biosensors · 2024Review
- Automated image registration of RGB, hyperspectral and chlorophyll fluorescence imaging data.Plant methods · 2024Article
- Investigation of Using Hyperspectral Vegetation Indices to Assess Brassica Downy Mildew.Sensors (Basel, Switzerland) · 2024Article
- Affordable Phenotyping at the Edge for High-Throughput Detection of Hypersensitive Reaction Involving Cotyledon Loss.Plant phenomics (Washington, D.C.) · 2024Article
- Explainable light-weight deep learning pipeline for improved drought stress identification.Frontiers in plant science · 2024Article
- A Prototype Method for the Detection and Recognition of Pigments in the Environment Based on Optical Property Simulation.Plants (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plant stresses have been monitored using the imaging or spectrometry of plant leaves in the visible (red-green-blue or RGB), near-infrared (NIR), infrared (IR), and ultraviolet (UV) wavebands, often augmented by fluorescence imaging or fluorescence spectrometry. Imaging at multiple specific wavelengths (multi-spectral imaging) or across a wide range of wavelengths (hyperspectral imaging) can provide exceptional information on plant stress and subsequent diseases. Digital cameras, thermal cameras, and optical filters have become available at a low cost in recent years, while hyperspectral cameras have become increasingly more compact and portable. Furthermore, smartphone cameras have dramatically improved in quality, making them a viable option for rapid, on-site stress detection. Due to these developments in imaging technology, plant stresses can be monitored more easily using handheld and field-deployable methods. Recent advances in machine learning algorithms have allowed for images and spectra to be analyzed and classified in a fully automated and reproducible manner, without the need for complicated image or spectrum analysis methods. This review will highlight recent advances in portable (including smartphone-based) detection methods for biotic and abiotic stresses, discuss data processing and machine learning techniques that can produce results for stress identification and classification, and suggest future directions towards the successful translation of these methods into practical use.
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What OpenQuestion holds
Registered trials
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.