22.2.8.8 Plant Disease Analysis, General Plant Diseasses

Chapter Contents (Back)
Change Detection. 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.

Zhang, N.[Ning], Yang, G.[Guijun], Pan, Y.[Yuchun], 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.[Sungchan], 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

ur Rehman, Z.[Zia], Khan, M.A.[Muhammad Attique], Ahmed, F.[Fawad], Damaševicius, R.[Robertas], Naqvi, S.R.[Syed Rameez], Nisar, W.[Wasif], Javed, K.[Kashif],
Recognizing apple leaf diseases using a novel parallel real-time processing framework based on MASK RCNN and transfer learning: An application for smart agriculture,
IET-IPR(15), No. 10, 2021, pp. 2157-2168.
DOI Link 2108
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, Pattern recognition, 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

Yu, H., Son, C.,
Leaf Spot Attention Network for Apple Leaf Disease Identification,
AgriVision20(229-237)
IEEE DOI 2008
Diseases, Feature extraction, Machine learning, Image color analysis, Image segmentation, Optical sensors 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, Pattern recognition, 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 .


Last update:Sep 19, 2021 at 21:11:01