Research graph
References from Intelligent Monitoring of Diseases and Insect Pests in Rice and Wheat: A Review of Multimodal Data Fusion and Early Warning Systems. Local targets link to admitted publications; unresolved targets remain external evidence.
The global burden of pathogens and pests on major food crops
10.1038/s41559-018-0793-y · 2019 · External reference
Rice functional genomics: Decades’ efforts and roads ahead
10.1007/s11427-021-2024-0 · 2022 · External reference
10.3390/agronomy13071851
10.3390/agronomy13071851 · External reference
10.1145/3570991.3570994
10.1145/3570991.3570994 · External reference
Unresolved reference
External reference
10.3390/agriculture16131456
10.3390/agriculture16131456 · External reference
Unresolved reference
External reference
Advanced agricultural disease image recognition technologies: A review
2022 · External reference
Recent advances in pest and disease recognition: A comprehensive review
10.4081/jae.2025.1776 · 2025 · External reference
10.3390/rs17040698
10.3390/rs17040698 · External reference
10.3390/agronomy16111059
10.3390/agronomy16111059 · External reference
A Systematic Literature Review on Plant Disease Detection: Motivations, Classification Techniques, Datasets, Challenges, and Future Trends
10.1109/access.2023.3284760 · 2023 · External reference
Identification of crop diseases using improved convolutional neural networks
10.1049/iet-cvi.2019.0136 · 2020 · External reference
RPH-Counter: Field detection and counting of rice planthoppers using a fully convolutional network with object-level supervision
10.1016/j.compag.2024.109242 · 2024 · External reference
Deep learning based agricultural remote sensing image segmentation: A review
2026 · External reference
10.3390/rs13132486
10.3390/rs13132486 · External reference
The PRISMA 2020 statement: An updated guideline for reporting systematic reviews
10.1136/bmj.n71 · 2021 · External reference
Synthesis without meta-analysis (SWiM) in systematic reviews: Reporting guideline
10.1136/bmj.l6890 · 2020 · External reference
Plant Disease Detection by Imaging Sensors–Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping
10.1094/pdis-03-15-0340-fe · 2016 · External reference
10.3389/fpls.2023.1073346
10.3389/fpls.2023.1073346 · External reference
10.3390/agronomy12061451
10.3390/agronomy12061451 · External reference
Hyperspectral and Chlorophyll Fluorescence Imaging for Early Detection of Plant Diseases, with Special Reference to Fusarium spec. Infections on Wheat
10.3390/agriculture4010032 · 2014 · External reference
10.3390/s17112596
10.3390/s17112596 · External reference
Application of a spore detection system based on diffraction imaging to tomato gray mold
2024 · External reference
Detection of spores using polarization image features and BP neural network
2024 · External reference
Rapid detection of rice disease using microscopy image identification based on the synergistic judgment of texture and shape features and decision tree-confusion matrix method
10.1002/jsfa.9943 · 2019 · External reference
A rapid rice blast detection and identification method based on crop disease spores’ diffraction fingerprint texture
10.1002/jsfa.10383 · 2020 · External reference
A rapid detection method of early spore viability based on AC impedance measurement
10.1111/jfpe.13520 · 2020 · External reference
10.3390/foods10123011
10.3390/foods10123011 · External reference
Separation-enrichment method for airborne disease spores based on microfluidic chip
2021 · External reference
10.3390/foods11213462
10.3390/foods11213462 · External reference
10.3390/agriculture15192076
10.3390/agriculture15192076 · External reference
10.3390/s19102281
10.3390/s19102281 · External reference
An in-field automatic wheat disease diagnosis system
10.1016/j.compag.2017.09.012 · 2017 · External reference
A system for automatic rice disease detection from rice paddy images serviced via a Chatbot
10.1016/j.compag.2021.106156 · 2021 · External reference
Rapid image detection and recognition of rice false smut based on mobile smart devices with anti-light features from cloud database
10.1016/j.biosystemseng.2022.04.005 · 2022 · External reference
A lightweight model for early perception of rice diseases driven by photothermal information fusion
10.1016/j.compag.2025.110150 · 2025 · External reference
Northern Maize Leaf Blight Detection Under Complex Field Environment Based on Deep Learning
10.1109/access.2020.2973658 · 2020 · External reference
10.3390/agriculture14091471
10.3390/agriculture14091471 · External reference
Applying spectral fractal dimension index to predict the SPAD value of rice leaves under bacterial blight disease stress
10.1186/s13007-022-00898-8 · 2022 · External reference
10.3389/fpls.2022.828454
10.3389/fpls.2022.828454 · External reference
Spectral characteristics and incubation period diagnosis technology of rice blast disease based on FTIR-PAS
2025 · External reference
10.3389/fpls.2018.01195
10.3389/fpls.2018.01195 · External reference
10.3390/s22030757
10.3390/s22030757 · External reference
10.3389/fpls.2021.693521
10.3389/fpls.2021.693521 · External reference
10.3389/fpls.2022.963170
10.3389/fpls.2022.963170 · External reference
10.1371/journal.pone.0187470
10.1371/journal.pone.0187470 · External reference
10.3390/agriculture15151670
10.3390/agriculture15151670 · External reference
10.3389/fpls.2021.628575
10.3389/fpls.2021.628575 · External reference
10.3390/rs12183046
10.3390/rs12183046 · External reference
Study on prediction method of downy mildew in wine grapes based on GA-LSTM
2023 · External reference
10.3390/horticulturae11030336
10.3390/horticulturae11030336 · External reference
10.3389/fpls.2023.1150748
10.3389/fpls.2023.1150748 · External reference
Automatic detection and counting of planthoppers on white flat plate images captured by AR glasses for planthopper field survey
10.1016/j.compag.2024.108639 · 2024 · External reference
Automated Counting of Rice Planthoppers in Paddy Fields Based on Image Processing
10.1016/s2095-3119(14)60799-1 · 2014 · External reference
10.3390/agronomy15092100
10.3390/agronomy15092100 · External reference
10.3390/rs17050929
10.3390/rs17050929 · External reference
10.1109/cvpr.2019.00899
10.1109/cvpr.2019.00899 · External reference
10.3389/fpls.2016.01419
10.3389/fpls.2016.01419 · External reference
Deep feature based rice leaf disease identification using support vector machine
10.1016/j.compag.2020.105527 · 2020 · External reference
10.20944/preprints202104.0755.v1
10.20944/preprints202104.0755.v1 · External reference
10.3390/rs11070846
10.3390/rs11070846 · External reference
Optimizing wheat scab in remote sensing monitoring accuracy using interridge background elimination
2024 · External reference
Aerial Visual Perception in Smart Farming: Field Study of Wheat Yellow Rust Monitoring
10.1109/tii.2020.2979237 · 2021 · External reference
10.3390/rs11212495
10.3390/rs11212495 · External reference
Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery
10.1016/j.compag.2018.10.017 · 2018 · External reference
10.3390/rs15133301
10.3390/rs15133301 · External reference
MOS sensor array based on multi-modal data weighted composite membership optimization for rice blast detection in symptomless stage
10.1016/j.compag.2025.110153 · 2025 · External reference
Early warning of rice mildew based on gas chromatography-ion mobility spectrometry technology and chemometrics
10.1007/s11694-020-00775-9 · 2021 · External reference
A Novel Nanoscaled Chemo Dye-Based Sensor for the Identification of Volatile Organic Compounds During the Mildewing Process of Stored Wheat
10.1007/s12161-019-01617-1 · 2019 · External reference
10.3389/fpls.2022.1004427
10.3389/fpls.2022.1004427 · External reference
10.3390/s18061901
10.3390/s18061901 · External reference
Multi-scale monitoring for hazard level classification of Brown Planthopper damage in rice using hyperspectral technique
2024 · External reference
10.3390/s18030868
10.3390/s18030868 · External reference
10.3390/rs15184631
10.3390/rs15184631 · External reference
10.1371/journal.pone.0314535
10.1371/journal.pone.0314535 · External reference
10.3390/agriculture12111785
10.3390/agriculture12111785 · External reference
10.3390/su15021502
10.3390/su15021502 · External reference
10.3390/agronomy14102231
10.3390/agronomy14102231 · External reference
10.3389/fpls.2021.701038
10.3389/fpls.2021.701038 · External reference
10.3390/plants10122643
10.3390/plants10122643 · External reference
Self-supervised transformer-based pre-training method using latent semantic masking auto-encoder for pest and disease classification
10.1016/j.compag.2022.107448 · 2022 · External reference
Apple Leaf Disease Recognition and Sub-Class Categorization Based on Improved Multi-Scale Feature Fusion Network
10.1109/access.2021.3094802 · 2021 · External reference
10.3390/agronomy15092147
10.3390/agronomy15092147 · External reference
Embedded AI for Wheat Yellow Rust Infection Type Classification
10.1109/access.2023.3254430 · 2023 · External reference
10.3390/agronomy15071549
10.3390/agronomy15071549 · External reference
10.3390/agriculture15141526
10.3390/agriculture15141526 · External reference
10.3390/agronomy14112734
10.3390/agronomy14112734 · External reference
10.3390/agronomy14112660
10.3390/agronomy14112660 · External reference
From visual estimates to fully automated sensor-based measurements of plant disease severity: Status and challenges for improving accuracy
10.1186/s42483-020-00049-8 · 2020 · External reference
U-Net: Convolutional Networks for Biomedical Image Segmentation
2015 · External reference
10.1007/978-3-030-01234-2_49
10.1007/978-3-030-01234-2_49 · External reference
10.1109/iccv.2017.322
10.1109/iccv.2017.322 · External reference
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
2021 · External reference
10.3390/agronomy15102252
10.3390/agronomy15102252 · External reference
10.3390/plants13131722
10.3390/plants13131722 · External reference
3-D Deep Learning Approach for Remote Sensing Image Classification
10.1109/tgrs.2018.2818945 · 2018 · External reference
10.3390/rs18060880
10.3390/rs18060880 · External reference
Rice-Fusion: A Multimodality Data Fusion Framework for Rice Disease Diagnosis
10.1109/access.2022.3140815 · 2022 · External reference
10.3390/agriculture15151690
10.3390/agriculture15151690 · External reference
Fusion of the deep networks for rapid detection of branch-infected aeroponically cultivated mulberries using multimodal traits
2025 · External reference
10.3390/foods12030535
10.3390/foods12030535 · External reference
RustQNet: Multimodal Deep Learning for Quantitative Inversion of Wheat Stripe Rust Disease Index
10.1016/j.compag.2024.109245 · 2024 · External reference
10.3390/plants12020317
10.3390/plants12020317 · External reference
Long-term seasonal forecasting of a major migrant insect pest: The brown planthopper in the Lower Yangtze River Valley
10.1007/s10340-018-1022-9 · 2019 · External reference
10.3390/app9224846
10.3390/app9224846 · External reference
Short-Term Forecasting Models on Occurrence of Rice Leaf Roller Based on Kalman Filter Algorithm
2016 · External reference
Recent Advances in Forecasting of Rice Blast Epidemics Using Computers in Japan
1989 · External reference
Risk Assessment Models for Wheat Fusarium Head Blight Epidemics Based on Within-Season Weather Data
10.1094/phyto.2003.93.4.428 · 2003 · External reference
10.1186/s12859-019-3065-1
10.1186/s12859-019-3065-1 · External reference
10.3390/agriculture15242560
10.3390/agriculture15242560 · External reference
10.3389/fpls.2020.570381
10.3389/fpls.2020.570381 · External reference
Climate-Based Prediction of Rice Blast Disease Using Count Time Series and Machine Learning Approaches
10.3390/agriengineering6040246 · 2024 · External reference
On Calibration of Modern Neural Networks
2017 · External reference
10.3390/app15147663
10.3390/app15147663 · External reference
10.3389/fpls.2026.1826962
10.3389/fpls.2026.1826962 · External reference
10.3390/agronomy12081869
10.3390/agronomy12081869 · External reference
10.3389/fpls.2025.1668545
10.3389/fpls.2025.1668545 · External reference
10.3390/agronomy15061471
10.3390/agronomy15061471 · External reference
10.3390/s20051487
10.3390/s20051487 · External reference
A sustainable crop protection through integrated technologies: UAV-based detection, real-time pesticide mixing, and adaptive spraying
10.1038/s41598-025-19473-x · 2025 · External reference
A rapid rice blast detection and identification method based on crop disease spores’ diffraction fingerprint texture
10.1002/jsfa.10383 · ExternalCitation · doi-reference
Rapid detection of rice disease using microscopy image identification based on the synergistic judgment of texture and shape features and decision tree-confusion matrix method
10.1002/jsfa.9943 · ExternalCitation · doi-reference
10.1007/978-3-030-01234-2_49
10.1007/978-3-030-01234-2_49 · ExternalCitation · doi-reference
Long-term seasonal forecasting of a major migrant insect pest: The brown planthopper in the Lower Yangtze River Valley
10.1007/s10340-018-1022-9 · ExternalCitation · doi-reference
Rice functional genomics: Decades’ efforts and roads ahead
10.1007/s11427-021-2024-0 · ExternalCitation · doi-reference
Early warning of rice mildew based on gas chromatography-ion mobility spectrometry technology and chemometrics
10.1007/s11694-020-00775-9 · ExternalCitation · doi-reference
A Novel Nanoscaled Chemo Dye-Based Sensor for the Identification of Volatile Organic Compounds During the Mildewing Process of Stored Wheat
10.1007/s12161-019-01617-1 · ExternalCitation · doi-reference
Rapid image detection and recognition of rice false smut based on mobile smart devices with anti-light features from cloud database
10.1016/j.biosystemseng.2022.04.005 · ExternalCitation · doi-reference
An in-field automatic wheat disease diagnosis system
10.1016/j.compag.2017.09.012 · ExternalCitation · doi-reference
Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery
10.1016/j.compag.2018.10.017 · ExternalCitation · doi-reference
Deep feature based rice leaf disease identification using support vector machine
10.1016/j.compag.2020.105527 · ExternalCitation · doi-reference
A system for automatic rice disease detection from rice paddy images serviced via a Chatbot
10.1016/j.compag.2021.106156 · ExternalCitation · doi-reference
Self-supervised transformer-based pre-training method using latent semantic masking auto-encoder for pest and disease classification
10.1016/j.compag.2022.107448 · ExternalCitation · doi-reference
Automatic detection and counting of planthoppers on white flat plate images captured by AR glasses for planthopper field survey
10.1016/j.compag.2024.108639 · ExternalCitation · doi-reference
RPH-Counter: Field detection and counting of rice planthoppers using a fully convolutional network with object-level supervision
10.1016/j.compag.2024.109242 · ExternalCitation · doi-reference
RustQNet: Multimodal Deep Learning for Quantitative Inversion of Wheat Stripe Rust Disease Index
10.1016/j.compag.2024.109245 · ExternalCitation · doi-reference
A lightweight model for early perception of rice diseases driven by photothermal information fusion
10.1016/j.compag.2025.110150 · ExternalCitation · doi-reference
MOS sensor array based on multi-modal data weighted composite membership optimization for rice blast detection in symptomless stage
10.1016/j.compag.2025.110153 · ExternalCitation · doi-reference
Automated Counting of Rice Planthoppers in Paddy Fields Based on Image Processing
10.1016/s2095-3119(14)60799-1 · ExternalCitation · doi-reference
The global burden of pathogens and pests on major food crops
10.1038/s41559-018-0793-y · ExternalCitation · doi-reference
A sustainable crop protection through integrated technologies: UAV-based detection, real-time pesticide mixing, and adaptive spraying
10.1038/s41598-025-19473-x · ExternalCitation · doi-reference
Identification of crop diseases using improved convolutional neural networks
10.1049/iet-cvi.2019.0136 · ExternalCitation · doi-reference
Plant Disease Detection by Imaging Sensors–Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping
10.1094/pdis-03-15-0340-fe · ExternalCitation · doi-reference
Risk Assessment Models for Wheat Fusarium Head Blight Epidemics Based on Within-Season Weather Data
10.1094/phyto.2003.93.4.428 · ExternalCitation · doi-reference
Northern Maize Leaf Blight Detection Under Complex Field Environment Based on Deep Learning
10.1109/access.2020.2973658 · ExternalCitation · doi-reference
Apple Leaf Disease Recognition and Sub-Class Categorization Based on Improved Multi-Scale Feature Fusion Network
10.1109/access.2021.3094802 · ExternalCitation · doi-reference
Rice-Fusion: A Multimodality Data Fusion Framework for Rice Disease Diagnosis
10.1109/access.2022.3140815 · ExternalCitation · doi-reference
Embedded AI for Wheat Yellow Rust Infection Type Classification
10.1109/access.2023.3254430 · ExternalCitation · doi-reference
A Systematic Literature Review on Plant Disease Detection: Motivations, Classification Techniques, Datasets, Challenges, and Future Trends
10.1109/access.2023.3284760 · ExternalCitation · doi-reference
10.1109/cvpr.2019.00899
10.1109/cvpr.2019.00899 · ExternalCitation · doi-reference
10.1109/iccv.2017.322
10.1109/iccv.2017.322 · ExternalCitation · doi-reference
3-D Deep Learning Approach for Remote Sensing Image Classification
10.1109/tgrs.2018.2818945 · ExternalCitation · doi-reference
Aerial Visual Perception in Smart Farming: Field Study of Wheat Yellow Rust Monitoring
10.1109/tii.2020.2979237 · ExternalCitation · doi-reference
A rapid detection method of early spore viability based on AC impedance measurement
10.1111/jfpe.13520 · ExternalCitation · doi-reference
Synthesis without meta-analysis (SWiM) in systematic reviews: Reporting guideline
10.1136/bmj.l6890 · ExternalCitation · doi-reference
The PRISMA 2020 statement: An updated guideline for reporting systematic reviews
10.1136/bmj.n71 · ExternalCitation · doi-reference
10.1145/3570991.3570994
10.1145/3570991.3570994 · ExternalCitation · doi-reference
10.1186/s12859-019-3065-1
10.1186/s12859-019-3065-1 · ExternalCitation · doi-reference
Applying spectral fractal dimension index to predict the SPAD value of rice leaves under bacterial blight disease stress
10.1186/s13007-022-00898-8 · ExternalCitation · doi-reference
From visual estimates to fully automated sensor-based measurements of plant disease severity: Status and challenges for improving accuracy
10.1186/s42483-020-00049-8 · ExternalCitation · doi-reference
10.1371/journal.pone.0187470
10.1371/journal.pone.0187470 · ExternalCitation · doi-reference
10.1371/journal.pone.0314535
10.1371/journal.pone.0314535 · ExternalCitation · doi-reference
10.20944/preprints202104.0755.v1
10.20944/preprints202104.0755.v1 · ExternalCitation · doi-reference
10.3389/fpls.2016.01419
10.3389/fpls.2016.01419 · ExternalCitation · doi-reference
10.3389/fpls.2018.01195
10.3389/fpls.2018.01195 · ExternalCitation · doi-reference
10.3389/fpls.2020.570381
10.3389/fpls.2020.570381 · ExternalCitation · doi-reference
10.3389/fpls.2021.628575
10.3389/fpls.2021.628575 · ExternalCitation · doi-reference
10.3389/fpls.2021.693521
10.3389/fpls.2021.693521 · ExternalCitation · doi-reference
10.3389/fpls.2021.701038
10.3389/fpls.2021.701038 · ExternalCitation · doi-reference
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10.3389/fpls.2022.1004427 · ExternalCitation · doi-reference
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Hyperspectral and Chlorophyll Fluorescence Imaging for Early Detection of Plant Diseases, with Special Reference to Fusarium spec. Infections on Wheat
10.3390/agriculture4010032 · ExternalCitation · doi-reference
Climate-Based Prediction of Rice Blast Disease Using Count Time Series and Machine Learning Approaches
10.3390/agriengineering6040246 · ExternalCitation · doi-reference
10.3390/agronomy12061451
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10.3390/horticulturae11030336
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10.3390/rs17040698 · ExternalCitation · doi-reference
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10.3390/s22030757 · ExternalCitation · doi-reference
10.3390/su15021502
10.3390/su15021502 · ExternalCitation · doi-reference
Recent advances in pest and disease recognition: A comprehensive review
10.4081/jae.2025.1776 · ExternalCitation · doi-reference
