20.7.3.7.4 Plant Disease

Chapter Contents (Back)
Precision Agriculture. Agriculture Tools. Disease in Plants. Plant Disease.
See also Wheat Rust, Blight, Disease, Damage.
See also Forest Change Evaluation, Bark Beetle, Pine Shoot Beetle, Other Insects.

Atoum, Y.[Yousef], Afridi, M.J.[Muhammad Jamal], Liu, X.M.[Xiao-Ming], McGrath, J.M.[J. Mitchell], Hanson, L.E.[Linda E.],
On developing and enhancing plant-level disease rating systems in real fields,
PR(53), No. 1, 2016, pp. 287-299.
Elsevier DOI 1602
CLS Rater BibRef

Xu, W.[Wei], Wang, Q.[Qili], Chen, R.[Runyu],
Spatio-temporal prediction of crop disease severity for agricultural emergency management based on recurrent neural networks,
GeoInfo(22), No. 2, April 2018, pp. 363-381.
WWW Link. 1805
BibRef

Zhao, H.Q.[Heng-Qian], Yang, C.H.[Cheng-Hai], Guo, W.[Wei], Zhang, L.[Lifu], Zhang, D.Y.[Dong-Yan],
Automatic Estimation of Crop Disease Severity Levels Based on Vegetation Index Normalization,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
BibRef
And: Correction: RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Kouadio, L.[Louis], El Jarroudi, M.[Moussa], Belabess, Z.[Zineb], Laasli, S.E.[Salah-Eddine], Roni, M.Z.K.[Md Zohurul Kadir], Amine, I.D.I.[Ibn Dahou Idrissi], Mokhtari, N.[Nourreddine], Mokrini, F.[Fouad], Junk, J.[Jürgen], Lahlali, R.[Rachid],
A Review on UAV-Based Applications for Plant Disease Detection and Monitoring,
RS(15), No. 17, 2023, pp. 4273.
DOI Link 2310
BibRef

Sharma, P.[Purushottam], Kumar, M.[Manoj], Sharma, R.[Richa], Bhushan, S.[Shashi], Gupta, S.I.[Sun-Il],
An automated system to detect crop diseases using deep learning,
IJCVR(13), No. 5, 2023, pp. 556-571.
DOI Link 2310
BibRef

Yilmaz, E.[Esra], Bocekci, S.C.[Sevim Ceylan], Safak, C.[Cengiz], Yildiz, K.[Kazim],
Advancements in smart agriculture: A systematic literature review on state-of-the-art plant disease detection with computer vision,
IET-CV(19), No. 1, 2025, pp. e70004.
DOI Link 2502
image processing, learning (artificial intelligence) BibRef

Wang, S.H.[Shao-Hua], Xu, D.C.[Da-Chuan], Liang, H.J.[Hao-Jian], Bai, Y.Q.[Yong-Qing], Li, X.[Xiao], Zhou, J.Y.[Jun-Yuan], Su, C.[Cheng], Wei, W.Y.[Wen-Yu],
Advances in Deep Learning Applications for Plant Disease and Pest Detection: A Review,
RS(17), No. 4, 2025, pp. 698.
DOI Link 2502
Survey, Plant Disease. BibRef

Mishra, M.[Monalisa], Pati, B.[Bibudhendu], Choudhury, P.[Prasenjit],
Multilevel classification of disease in plants with IoT using a hybrid optimisation algorithm,
IJCVR(16), No. 1, 2026, pp. 100-140.
DOI Link 2512
BibRef


Liu, X.[Xiang], Liu, Z.X.[Zhao-Xiang], Hu, H.[Huan], Chen, Z.Z.[Ze-Zhou], Wang, K.[Kohou], Wang, K.[Kai], Lian, S.[Shiguo],
A Multimodal Benchmark Dataset and Model for Crop Disease Diagnosis,
ECCV24(LXXXVI: 157-170).
Springer DOI 2412
BibRef

Ahmad, J.[Jamil], Gueaieb, W.[Wail], El Saddik, A.[Abdulmotaleb], de Masi, G.[Giulia], Karray, F.[Fakhri],
Knowledge-Infused Learning for Fine-Grained Plant Disease Recognition,
ICIP24(395-401)
IEEE DOI 2411
Training, Visualization, Plant diseases, Predictive models, Feature extraction, Data models, Robustness, knowledge-infusion, explainability BibRef

Lopes, F.A.[Felipe A.], Sagan, V.[Vasit], Esposito, F.[Flavio],
PlantPlotGAN: A Physics-Informed Generative Adversarial Network for Plant Disease Prediction,
WACV24(7051-7060)
IEEE DOI 2404
Training, Plant diseases, Plantations, Vegetation mapping, Predictive models, Remote Sensing BibRef

Prashanth, K.[Komuravelli], Harsha, J.S.[Jaladi Sri], Kumar, S.A.[Sivapuram Arun], Srilekha, J.[Jaladi],
Towards Accurate Disease Segmentation in Plant Images: A Comprehensive Dataset Creation and Network Evaluation,
WACV24(7071-7079)
IEEE DOI 2404
Training, Productivity, Image segmentation, Plant diseases, Pathology, Plants (biology), Refining BibRef

Tsai, Y.H.[Yao-Hong], Hsu, T.C.[Tse-Chuan],
An Effective Deep Neural Network in Edge Computing Enabled Internet of Things for Plant Diseases Monitoring,
IoTDesign24(695-699)
IEEE DOI 2404
Performance evaluation, Deep learning, Plant diseases, Image recognition, Prototypes, Feature extraction, Loss measurement BibRef

Padeiro, C.V.[Carlos Victorino], Komamizu, T.[Takahiro], Ide, I.[Ichiro],
Towards Achieving Lightweight Deep Neural Network for Precision Agriculture with Maize Disease Detection,
MVA23(1-6)
DOI Link 2403
Visualization, Plant diseases, Power supplies, Crops, Object detection, Detectors, Network architecture BibRef

Pagé-Fortin, M.[Mathieu],
Class-Incremental Learning of Plant and Disease Detection: Growing Branches with Knowledge Distillation,
CVPPA23(593-603)
IEEE DOI 2401
BibRef

Chai, A.Y.H.[Abel Yu Hao], Lee, S.H.[Sue Han], Tay, F.S.[Fei Siang], Then, Y.L.[Yi Lung], Goëau, H.[Hervé], Bonnet, P.[Pierre], Joly, A.[Alexis],
Pairwise Feature Learning for Unseen Plant Disease Recognition,
ICIP23(306-310)
IEEE DOI 2312
BibRef

Siricharoen, P.[Punnarai], Scotney, B.[Bryan], Morrow, P.[Philip], Parr, G.[Gerard],
A Lightweight Mobile System for Crop Disease Diagnosis,
ICIAR16(783-791).
Springer DOI 1608
BibRef

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Food Descriptions, Dishes, Recipe Generation .


Last update:Dec 22, 2025 at 13:59:36