23.2.9.9 Plant Disease Analysis, General Plant Diseasses

Chapter Contents (Back)
Change Detection. Disease. Plant Disease. Mostly close range:
See also Agriculture, Inspection -- Food Products, Plants, Farms. Trees mostly:
See also Forest Change Evaluation, Bark Beetle, Pine Shoot Beetle, Other Insects.
See also Apple Trees, Plantations, Analysis, Diseases.

Zhang, N.[Ning], Yang, G.J.[Gui-Jun], Pan, Y.C.[Yu-Chun], Yang, X.D.[Xiao-Dong], Chen, L.P.[Li-Ping], Zhao, C.J.[Chun-Jiang],
A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
Survey, Plant Disease. BibRef

Shin, J.Y.[Ju-Young], Kim, B.Y.[Bu-Yo], Park, J.[Junsang], Kim, K.R.[Kyu Rang], Cha, J.W.[Joo Wan],
Prediction of Leaf Wetness Duration Using Geostationary Satellite Observations and Machine Learning Algorithms,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
Leaf wetness duration (LWD) and plant diseases are strongly associated BibRef

Sinha, A.[Aditya], Shekhawat, R.S.[Rajveer Singh],
Review of image processing approaches for detecting plant diseases,
IET-IPR(14), No. 8, 19 June 2020, pp. 1427-1439.
DOI Link 2005
BibRef

Poblete, T., Camino, C., Beck, P.S.A., Hornero, A., Kattenborn, T., Saponari, M., Boscia, D., Navas-Cortes, J.A., Zarco-Tejada, P.J.,
Detection of Xylella fastidiosa infection symptoms with airborne multispectral and thermal imagery: Assessing bandset reduction performance from hyperspectral analysis,
PandRS(162), 2020, pp. 27-40.
Elsevier DOI 2004
Hyperspectral, Multispectral, Thermal, Radiative transfer, Airborne, Machine learning BibRef

Liu, X., Min, W., Mei, S., Wang, L., Jiang, S.,
Plant Disease Recognition: A Large-Scale Benchmark Dataset and a Visual Region and Loss Reweighting Approach,
IP(30), 2021, pp. 2003-2015.
IEEE DOI 2101
Diseases, Agriculture, Plants (biology), Visualization, Image recognition, Feature extraction, Medical diagnosis, feature aggregation BibRef

Gunasekaran, S.[Suresh], Gunavathi, K.[Kandasamy],
Delta tributary network: An efficient alternate approach for bottleneck layers in CNN for plant disease classification,
IET-IPR(15), No. 3, 2021, pp. 818-832.
DOI Link 2106
BibRef

Chen, J.[Junde], Zhang, D.[Defu], Suzauddola, M.[Md], Nanehkaran, Y.A.[Yaser Ahangari], Sun, Y.D.[Yuan-Dong],
Identification of plant disease images via a squeeze-and-excitation MobileNet model and twice transfer learning,
IET-IPR(15), No. 5, 2021, pp. 1115-1127.
DOI Link 2106
BibRef

Ouhami, M.[Maryam], Hafiane, A.[Adel], Es-Saady, Y.[Youssef], El Hajji, M.[Mohamed], Canals, R.[Raphael],
Computer Vision, IoT and Data Fusion for Crop Disease Detection Using Machine Learning: A Survey and Ongoing Research,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Oh, S.C.[Sung-Chan], Lee, D.Y.[Da-Young], Gongora-Canul, C.[Carlos], Ashapure, A.[Akash], Carpenter, J.[Joshua], Cruz, A.P., Fernandez-Campos, M.[Mariela], Lane, B.Z.[Brenden Z.], Telenko, D.E.P.[Darcy E. P.], Jung, J.H.[Jin-Ha], Cruz, C.D.,
Tar Spot Disease Quantification Using Unmanned Aircraft Systems (UAS) Data,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Wei, X.[Xing], Johnson, M.A.[Marcela A.], Langston, D.B.[David B.], Mehl, H.L.[Hillary L.], Li, S.[Song],
Identifying Optimal Wavelengths as Disease Signatures Using Hyperspectral Sensor and Machine Learning,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Neupane, K.[Krishna], Baysal-Gurel, F.[Fulya],
Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Liang, G.C.[Gui-Chou], Ouyang, Y.C.[Yen-Chieh], Dai, S.M.[Shu-Mei],
Detection and Classification of Rice Infestation with Rice Leaf Folder (Cnaphalocrocis medinalis) Using Hyperspectral Imaging Techniques,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Luo, L.[Lili], Chang, Q.R.[Qing-Rui], Wang, Q.[Qi], Huang, Y.[Yong],
Identification and Severity Monitoring of Maize Dwarf Mosaic Virus Infection Based on Hyperspectral Measurements,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Zhang, Y.[Yan], Wa, S.Y.[Shi-Yun], Liu, Y.T.[Yu-Tong], Zhou, X.Y.[Xiao-Ya], Sun, P.[Pengshuo], Ma, Q.[Qin],
High-Accuracy Detection of Maize Leaf Diseases CNN Based on Multi-Pathway Activation Function Module,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Lei, S.H.[Shu-Han], Luo, J.B.[Jian-Biao], Tao, X.J.[Xiao-Jun], Qiu, Z.X.[Zi-Xuan],
Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Pane, C.[Catello], Manganiello, G.[Gelsomina], Nicastro, N.[Nicola], Carotenuto, F.[Francesco],
Early Detection of Wild Rocket Tracheofusariosis Using Hyperspectral Image-Based Machine Learning,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
BibRef

McDonald, M.R.[Mary Ruth], Tayviah, C.S.[Cyril Selasi], Gossen, B.D.[Bruce D.],
Human vs. Machine, the Eyes Have It. Assessment of Stemphylium Leaf Blight on Onion Using Aerial Photographs from an NIR Camera,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Chen, Y.P.[Yi-Peng], Xu, K.[Ke], Zhou, P.[Peng], Ban, X.J.[Xiao-Juan], He, D.[Di],
Improved cross entropy loss for noisy labels in vision leaf disease classification,
IET-IPR(16), No. 6, 2022, pp. 1511-1519.
DOI Link 2204
BibRef

Wang, Y.M.[Yeniu Mickey], Ostendorf, B.[Bertram], Gautam, D.[Deepak], Habili, N.[Nuredin], Pagay, V.[Vinay],
Plant Viral Disease Detection: From Molecular Diagnosis to Optical Sensing Technology: A Multidisciplinary Review,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Guan, Q.[Qiang], Song, K.[Kai], Feng, S.[Shuai], Yu, F.H.[Feng-Hua], Xu, T.Y.[Tong-Yu],
Detection of Peanut Leaf Spot Disease Based on Leaf-, Plant-, and Field-Scale Hyperspectral Reflectance,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Prasad, A.[Aaditya], Mehta, N.[Nikhil], Horak, M.[Matthew], Bae, W.D.[Wan D.],
A Two-Step Machine Learning Approach for Crop Disease Detection Using GAN and UAV Technology,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Kuswidiyanto, L.W.[Lukas Wiku], Noh, H.H.[Hyun-Ho], Han, X.Z.[Xiong-Zhe],
Plant Disease Diagnosis Using Deep Learning Based on Aerial Hyperspectral Images: A Review,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Shahi, T.B.[Tej Bahadur], Xu, C.Y.[Cheng-Yuan], Neupane, A.[Arjun], Guo, W.[William],
Recent Advances in Crop Disease Detection Using UAV and Deep Learning Techniques,
RS(15), No. 9, 2023, pp. xx-yy.
DOI Link 2305
BibRef

Pumhirunroj, B.[Benjamabhorn], Littidej, P.[Patiwat], Boonmars, T.[Thidarut], Bootyothee, K.[Kanokwan], Artchayasawat, A.[Atchara], Khamphilung, P.[Phusit], Slack, D.[Donald],
Machine-Learning-Based Forest Classification and Regression (FCR) for Spatial Prediction of Liver Fluke Opisthorchis viverrini (OV) Infection in Small Sub-Watersheds,
IJGI(12), No. 12, 2023, pp. 503.
DOI Link 2312
food-borne trematode parasite. BibRef

Lyu, Y.[Yang], Han, X.Z.[Xiong-Zhe], Wang, P.[Pingan], Shin, J.Y.[Jae-Yeong], Ju, M.W.[Min-Woong],
Unmanned Aerial Vehicle-Based RGB Imaging and Lightweight Deep Learning for Downy Mildew Detection in Kimchi Cabbage,
RS(17), No. 14, 2025, pp. 2388.
DOI Link 2508
BibRef


Mathiyalagan, P., Riddhi, D., Kalpana, M.,
Blackgram Plant Leaves Disease Detection,
ICCVMI23(1-4)
IEEE DOI 2403
Economics, Agriculture, Monitoring, Machine intelligence, Diseases, Blackgram, ResNet V2 BibRef

Maski, P.[Prajwal], Thondiyath, A.[Asokan],
Plant Disease Detection Using Advanced Deep Learning Algorithms: A Case Study of Papaya Ring Spot Disease,
ICIVC21(49-54)
IEEE DOI 2112
Training, Deep learning, Productivity, Agricultural robots, Plants (biology), Robot sensing systems, model training BibRef

Dadsetan, S.[Saba], Pichler, D.[David], Wilson, D.[David], Hovakimyan, N.[Naira], Hobbs, J.[Jennifer],
Superpixels and Graph Convolutional Neural Networks for Efficient Detection of Nutrient Deficiency Stress from Aerial Imagery,
AgriVision21(2944-2953)
IEEE DOI 2109
Image segmentation, Semantics, Agriculture, Real-time systems, Computational efficiency, Convolutional neural networks BibRef

Garg, K.[Kanish], Bhugra, S.[Swati], Lall, B.[Brejesh],
Automatic Quantification of Plant Disease from Field Image Data Using Deep Learning,
WACV21(1964-1971)
IEEE DOI 2106
Deep learning, Training, Location awareness, Image segmentation, Visualization, Pipelines, Manuals BibRef

Lee, S.H.[Sue Han], Goëau, H.[Hervé], Bonnet, P.[Pierre], Joly, A.[Alexis],
Conditional Multi-Task learning for Plant Disease Identification,
ICPR21(3320-3327)
IEEE DOI 2105
Training, Learning systems, Deep learning, Scalability, Benchmark testing, Diseases, multi-task learning BibRef

Fuentes, A.[Alvaro], Yoon, S.[Sook], Park, D.S.[Dong Sun],
Deep Learning-based Techniques for Plant Diseases Recognition in Real-field Scenarios,
ACIVS20(3-14).
Springer DOI 2003
BibRef

Costa, J.[Joana], Silva, C.[Catarina], Ribeiro, B.[Bernardete],
Hierarchical Deep Learning Approach for Plant Disease Detection,
IbPRIA19(II:383-393).
Springer DOI 1910
BibRef

Moghadam, P., Ward, D., Goan, E., Jayawardena, S., Sikka, P., Hernandez, E.,
Plant Disease Detection Using Hyperspectral Imaging,
DICTA17(1-8)
IEEE DOI 1804
agriculture, crops, feature extraction, hyperspectral imaging, image classification, learning (artificial intelligence), Vegetation mapping BibRef

Nebiker, S., Lack, N., Abächerli, M., Läderach, S.,
Light-weight Multispectral UAV Sensors And Their Capabilities For Predicting Grain Yield And Detecting Plant Diseases,
ISPRS16(B1: 963-970).
DOI Link 1610
BibRef

Siricharoen, P., Scotney, B., Morrow, P., Parr, G.,
Texture and shape attribute selection for plant disease monitoring in a mobile cloud-based environment,
ICIP16(489-493)
IEEE DOI 1610
Diseases BibRef

Ennadifi, E., Laraba, S., Vincke, D., Mercatoris, B., Gosselin, B.,
Wheat Diseases Classification and Localization Using Convolutional Neural Networks and GradCAM Visualization,
ISCV20(1-5)
IEEE DOI 2011
agriculture, convolutional neural nets, crops, feature extraction, image classification, image segmentation, plant diseases detection BibRef

Kawasaki, Y.[Yusuke], Uga, H.[Hiroyuki], Kagiwada, S.[Satoshi], Iyatomi, H.[Hitoshi],
Basic Study of Automated Diagnosis of Viral Plant Diseases Using Convolutional Neural Networks,
ISVC15(II: 638-645).
Springer DOI 1601
BibRef

Neumann, M.[Marion], Hallau, L.[Lisa], Klatt, B.[Benjamin], Kersting, K.[Kristian], Bauckhage, C.[Christian],
Erosion Band Features for Cell Phone Image Based Plant Disease Classification,
ICPR14(3315-3320)
IEEE DOI 1412
Cameras BibRef

Pang, J.[Jun], Bai, Z.Y.[Zhong-Ying], Lai, J.C.[Jun-Chen], Li, S.K.[Shao-Kun],
Automatic segmentation of crop leaf spot disease images by integrating local threshold and seeded region growing,
IASP11(590-594).
IEEE DOI 1112
BibRef

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
LiDAR for Land Cover, Laser Scanners for Land Cover, Remote Sensing .


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