ReviewSensors (Basel, Switzerland)2022
Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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
40 citing papers in PubMed, 220 citations in OpenAlex.
- Beyond the Visual Spectrum: From RGB-Based Learning to Hyperspectral Intelligence for Plant Disease Detection-Challenges and Opportunities.Sensors (Basel, Switzerland) · 2026Review
- Handheld hyperspectral imaging dataset of annual sowthistle and little mallow under abiotic stress for machine learning.Data in brief · 2026Article
- The Influence of Pre-Harvest Application ofFoods (Basel, Switzerland) · 2026Article
- Enhancing crop disease recognition framework via vision-language model with cross-attention and gated fusion.Scientific reports · 2026Article
- Adaptive fuzzy deep learning with multimodal sensor fusion for enhanced plant disease detection.Scientific reports · 2026Article
- Advancements and Emerging Trends in the Detection of Sweet Potato Diseases: A Comprehensive Review.ACS omega · 2026Review
- Estimating Rice Cropping Area and Analyzing Land Use and Land Cover Changes in Jiangsu Province Using Multispectral Satellite Imagery.Plants (Basel, Switzerland) · 2026Article
- A Survey of Crop Disease Recognition Methods Based on Spectral and RGB Images.Journal of imaging · 2026Review
- Beyond QTL and GWAS: how deep learning, graph models, and multi-omics are reshaping plant genomic prediction analysis.Frontiers in genetics · 2026Review
- VNIR Hyperspectral Signatures for Early Detection and Machine-Learning Classification of Wheat Diseases.Plants (Basel, Switzerland) · 2025Article
- Cross-Crop Transferability of Machine Learning Models for Early Stem Rust Detection in Wheat and Barley Using Hyperspectral Imaging.Plants (Basel, Switzerland) · 2025Article
- Analysis of Carbon Density Influencing Factors and Ecological Effects of Green Space Planning in Dongjiakou Port Area.Plants (Basel, Switzerland) · 2025Article
- Cross-Modal Data Fusion via Vision-Language Model for Crop Disease Recognition.Sensors (Basel, Switzerland) · 2025Article
- Detection of Apple Leaf Diseases Based on LightYOLO-AppleLeafDx.Plants (Basel, Switzerland) · 2025Article
- A review of deep learning architectures for plant disease detection.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
- Advanced hyperspectral image processing and machine learning approaches for early detection of wheat stem rust.Frontiers in plant science · 2025Article
- Article
- Early surveillance of rice bakanae disease using deep learning and hyperspectral imaging.aBIOTECH · 2024Article
- A Microscope Setup and Methodology for Capturing Hyperspectral and RGB Histopathological Imaging Databases.Sensors (Basel, Switzerland) · 2024Article
- Early diagnosis of Cladosporium fulvum in greenhouse tomato plants based on visible/near-infrared (VIS/NIR) and near-infrared (NIR) data fusion.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
The development of hyperspectral remote sensing equipment, in recent years, has provided plant protection professionals with a new mechanism for assessing the phytosanitary state of crops. Semantically rich data coming from hyperspectral sensors are a prerequisite for the timely and rational implementation of plant protection measures. This review presents modern advances in early plant disease detection based on hyperspectral remote sensing. The review identifies current gaps in the methodologies of experiments. A further direction for experimental methodological development is indicated. A comparative study of the existing results is performed and a systematic table of different plants' disease detection by hyperspectral remote sensing is presented, including important wave bands and sensor model information.
Indexed as
Identifiers
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.