429 lines
19 KiB
BibTeX
429 lines
19 KiB
BibTeX
@article{an2019,
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title = {Identification and {{Classification}} of {{Maize Drought Stress Using Deep Convolutional Neural Network}}},
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author = {An, Jiangyong and Li, Wanyi and Li, Maosong and Cui, Sanrong and Yue, Huanran},
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date = {2019-02},
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journaltitle = {Symmetry},
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volume = {11},
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number = {2},
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pages = {256},
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publisher = {{Multidisciplinary Digital Publishing Institute}},
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issn = {2073-8994},
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doi = {10.3390/sym11020256},
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issue = {2},
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langid = {english},
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keywords = {deep convolutional neural network,drought classification,drought identification,drought stress,maize,phenotype,traditional machine learning}
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}
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@article{ariss2022,
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title = {{{ResNet-based Parkinson}}'s {{Disease Classification}}},
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author = {Ariss, Omar El and Hu, Kaoning},
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date = {2022},
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journaltitle = {IEEE Transactions on Artificial Intelligence},
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pages = {1--11},
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issn = {2691-4581},
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doi = {10.1109/TAI.2022.3193651},
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eventtitle = {{{IEEE Transactions}} on {{Artificial Intelligence}}},
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keywords = {Convolutional Neural Networks,deep learning,Deep learning,diagnosis,Diseases,Feature extraction,frequency features,heat map,Heating systems,Parkinson's disease,Parkinson's Disease,Recording,Residual neural networks,ResNet,speech recording,transfer learning}
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}
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@article{atanasov2021,
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title = {Predicting Soil Moisture Based on the Color of the Leaves Using Data Mining and Machine Learning Techniques},
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author = {Atanasov, S. S.},
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date = {2021-01},
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journaltitle = {IOP Conference Series: Materials Science and Engineering},
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shortjournal = {IOP Conf. Ser.: Mater. Sci. Eng.},
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volume = {1031},
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number = {1},
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pages = {012076},
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publisher = {{IOP Publishing}},
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issn = {1757-899X},
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doi = {10.1088/1757-899X/1031/1/012076},
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langid = {english}
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}
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@article{awad2019,
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title = {Toward {{Precision}} in {{Crop Yield Estimation Using Remote Sensing}} and {{Optimization Techniques}}},
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author = {Awad, Mohamad M.},
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date = {2019-03},
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journaltitle = {Agriculture},
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volume = {9},
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number = {3},
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pages = {54},
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publisher = {{Multidisciplinary Digital Publishing Institute}},
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issn = {2077-0472},
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doi = {10.3390/agriculture9030054},
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issue = {3},
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langid = {english},
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keywords = {crop yield,environment,evapotranspiration,image processing,remote sensing}
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}
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@inproceedings{azimi2020,
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title = {Water {{Stress Identification}} in {{Chickpea Plant Shoot Images}} Using {{Deep Learning}}},
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booktitle = {2020 {{IEEE}} 17th {{India Council International Conference}} ({{INDICON}})},
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author = {Azimi, Shiva and Kaur, Taranjit and Gandhi, Tapan K},
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date = {2020-12},
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pages = {1--7},
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issn = {2325-9418},
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doi = {10.1109/INDICON49873.2020.9342388},
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eventtitle = {2020 {{IEEE}} 17th {{India Council International Conference}} ({{INDICON}})},
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keywords = {computer vision,deep learning,Deep learning,Nitrogen,plant phenotyping,Proteins,Real-time systems,Stress,Support vector machines,Tools,water stress}
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}
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@article{azimi2021,
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title = {Intelligent {{Monitoring}} of {{Stress Induced}} by {{Water Deficiency}} in {{Plants Using Deep Learning}}},
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author = {Azimi, Shiva and Wadhawan, Rohan and Gandhi, Tapan K.},
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date = {2021},
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journaltitle = {IEEE Transactions on Instrumentation and Measurement},
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volume = {70},
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pages = {1--13},
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issn = {1557-9662},
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doi = {10.1109/TIM.2021.3111994},
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eventtitle = {{{IEEE Transactions}} on {{Instrumentation}} and {{Measurement}}},
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keywords = {Computer vision,convolutional neural network (CNN),Convolutional neural networks,Crops,deep learning (DL),Long short term memory,long short-term memory (LSTM),monitoring,neural network,Pipelines,plant phenotyping,spatiotemporal analysis,Stress,Visualization,water stress}
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}
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@article{benos2021,
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title = {Machine {{Learning}} in {{Agriculture}}: {{A Comprehensive Updated Review}}},
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shorttitle = {Machine {{Learning}} in {{Agriculture}}},
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author = {Benos, Lefteris and Tagarakis, Aristotelis C. and Dolias, Georgios and Berruto, Remigio and Kateris, Dimitrios and Bochtis, Dionysis},
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date = {2021-01},
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journaltitle = {Sensors},
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volume = {21},
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number = {11},
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pages = {3758},
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publisher = {{Multidisciplinary Digital Publishing Institute}},
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issn = {1424-8220},
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doi = {10.3390/s21113758},
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issue = {11},
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langid = {english},
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keywords = {artificial intelligence,crop management,livestock management,machine learning,precision agriculture,precision livestock farming,soil management,water management}
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}
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@article{bergstra2012,
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title = {Random Search for Hyper-Parameter Optimization},
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author = {Bergstra, James and Bengio, Yoshua},
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date = {2012-02-01},
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journaltitle = {The Journal of Machine Learning Research},
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shortjournal = {J. Mach. Learn. Res.},
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volume = {13},
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pages = {281--305},
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issn = {1532-4435},
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issue = {null},
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keywords = {deep learning,global optimization,model selection,neural networks,response surface modeling}
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}
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@online{bochkovskiy2020,
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title = {{{YOLOv4}}: {{Optimal Speed}} and {{Accuracy}} of {{Object Detection}}},
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shorttitle = {{{YOLOv4}}},
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author = {Bochkovskiy, Alexey and Wang, Chien-Yao and Liao, Hong-Yuan Mark},
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date = {2020-04-22},
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number = {arXiv:2004.10934},
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eprint = {arXiv:2004.10934},
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eprinttype = {arxiv},
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doi = {10.48550/arXiv.2004.10934},
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pubstate = {preprint},
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keywords = {Computer Science - Computer Vision and Pattern Recognition,Electrical Engineering and Systems Science - Image and Video Processing}
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}
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@online{brown2020,
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title = {Language {{Models}} Are {{Few-Shot Learners}}},
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author = {Brown, Tom B. and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom and Child, Rewon and Ramesh, Aditya and Ziegler, Daniel M. and Wu, Jeffrey and Winter, Clemens and Hesse, Christopher and Chen, Mark and Sigler, Eric and Litwin, Mateusz and Gray, Scott and Chess, Benjamin and Clark, Jack and Berner, Christopher and McCandlish, Sam and Radford, Alec and Sutskever, Ilya and Amodei, Dario},
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date = {2020-07-22},
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number = {arXiv:2005.14165},
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eprint = {arXiv:2005.14165},
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eprinttype = {arxiv},
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doi = {10.48550/arXiv.2005.14165},
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pubstate = {preprint},
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keywords = {Computer Science - Computation and Language}
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}
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@article{chandel2021,
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title = {Identifying Crop Water Stress Using Deep Learning Models},
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author = {Chandel, Narendra Singh and Chakraborty, Subir Kumar and Rajwade, Yogesh Anand and Dubey, Kumkum and Tiwari, Mukesh K. and Jat, Dilip},
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date = {2021-05-01},
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journaltitle = {Neural Computing and Applications},
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shortjournal = {Neural Comput \& Applic},
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volume = {33},
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number = {10},
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pages = {5353--5367},
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issn = {1433-3058},
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doi = {10.1007/s00521-020-05325-4},
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langid = {english},
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keywords = {Confusion matrix,Crop phenotyping,DCNN,Digital agriculture,Machine learning}
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}
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@inproceedings{deng2009,
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title = {{{ImageNet}}: {{A}} Large-Scale Hierarchical Image Database},
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shorttitle = {{{ImageNet}}},
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booktitle = {2009 {{IEEE Conference}} on {{Computer Vision}} and {{Pattern Recognition}}},
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author = {Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
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date = {2009-06},
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pages = {248--255},
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issn = {1063-6919},
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doi = {10.1109/CVPR.2009.5206848},
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eventtitle = {2009 {{IEEE Conference}} on {{Computer Vision}} and {{Pattern Recognition}}},
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keywords = {Explosions,Image databases,Image retrieval,Information retrieval,Internet,Large-scale systems,Multimedia databases,Ontologies,Robustness,Spine}
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}
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@inproceedings{he2016,
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title = {Deep {{Residual Learning}} for {{Image Recognition}}},
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booktitle = {2016 {{IEEE Conference}} on {{Computer Vision}} and {{Pattern Recognition}} ({{CVPR}})},
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author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
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date = {2016-06},
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pages = {770--778},
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issn = {1063-6919},
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doi = {10.1109/CVPR.2016.90},
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eventtitle = {2016 {{IEEE Conference}} on {{Computer Vision}} and {{Pattern Recognition}} ({{CVPR}})},
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keywords = {Complexity theory,Degradation,Image recognition,Image segmentation,Neural networks,Training,Visualization}
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}
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@online{kingma2017,
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title = {Adam: {{A Method}} for {{Stochastic Optimization}}},
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shorttitle = {Adam},
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author = {Kingma, Diederik P. and Ba, Jimmy},
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date = {2017-01-29},
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number = {arXiv:1412.6980},
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eprint = {arXiv:1412.6980},
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eprinttype = {arxiv},
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doi = {10.48550/arXiv.1412.6980},
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pubstate = {preprint},
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keywords = {Computer Science - Machine Learning}
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}
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@article{kuznetsova2020,
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title = {The {{Open Images Dataset V4}}: {{Unified}} Image Classification, Object Detection, and Visual Relationship Detection at Scale},
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shorttitle = {The {{Open Images Dataset V4}}},
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author = {Kuznetsova, Alina and Rom, Hassan and Alldrin, Neil and Uijlings, Jasper and Krasin, Ivan and Pont-Tuset, Jordi and Kamali, Shahab and Popov, Stefan and Malloci, Matteo and Kolesnikov, Alexander and Duerig, Tom and Ferrari, Vittorio},
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date = {2020-07},
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journaltitle = {International Journal of Computer Vision},
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shortjournal = {Int J Comput Vis},
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volume = {128},
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number = {7},
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eprint = {1811.00982},
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eprinttype = {arxiv},
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eprintclass = {cs},
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pages = {1956--1981},
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issn = {0920-5691, 1573-1405},
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doi = {10.1007/s11263-020-01316-z},
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keywords = {Computer Science - Computer Vision and Pattern Recognition}
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}
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@online{lin2015,
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title = {Microsoft {{COCO}}: {{Common Objects}} in {{Context}}},
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shorttitle = {Microsoft {{COCO}}},
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author = {Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Bourdev, Lubomir and Girshick, Ross and Hays, James and Perona, Pietro and Ramanan, Deva and Zitnick, C. Lawrence and Dollár, Piotr},
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date = {2015-02-20},
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number = {arXiv:1405.0312},
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eprint = {arXiv:1405.0312},
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eprinttype = {arxiv},
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doi = {10.48550/arXiv.1405.0312},
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pubstate = {preprint},
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keywords = {Computer Science - Computer Vision and Pattern Recognition}
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}
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@article{lopez-garcia2022,
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title = {Machine {{Learning-Based Processing}} of {{Multispectral}} and {{RGB UAV Imagery}} for the {{Multitemporal Monitoring}} of {{Vineyard Water Status}}},
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author = {López-García, Patricia and Intrigliolo, Diego and Moreno, Miguel A. and Martínez-Moreno, Alejandro and Ortega, José Fernando and Pérez-Álvarez, Eva Pilar and Ballesteros, Rocío},
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date = {2022-09},
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journaltitle = {Agronomy},
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volume = {12},
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number = {9},
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pages = {2122},
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publisher = {{Multidisciplinary Digital Publishing Institute}},
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issn = {2073-4395},
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doi = {10.3390/agronomy12092122},
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issue = {9},
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langid = {english},
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keywords = {ANN,machine learning,multispectral images,RGB images,UAV,vineyard,water stress}
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}
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@article{mateo-aroca2019,
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title = {Remote {{Image Capture System}} to {{Improve Aerial Supervision}} for {{Precision Irrigation}} in {{Agriculture}}},
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author = {Mateo-Aroca, Antonio and García-Mateos, Ginés and Ruiz-Canales, Antonio and Molina-García-Pardo, José María and Molina-Martínez, José Miguel},
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date = {2019-02},
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journaltitle = {Water},
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volume = {11},
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number = {2},
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pages = {255},
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publisher = {{Multidisciplinary Digital Publishing Institute}},
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issn = {2073-4441},
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doi = {10.3390/w11020255},
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issue = {2},
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langid = {english},
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keywords = {image capture system,irrigation management,lettuce,wireless,ZigBee and XBee}
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}
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@article{mcenroe2022,
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title = {A {{Survey}} on the {{Convergence}} of {{Edge Computing}} and {{AI}} for {{UAVs}}: {{Opportunities}} and {{Challenges}}},
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shorttitle = {A {{Survey}} on the {{Convergence}} of {{Edge Computing}} and {{AI}} for {{UAVs}}},
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author = {McEnroe, Patrick and Wang, Shen and Liyanage, Madhusanka},
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date = {2022-09},
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journaltitle = {IEEE Internet of Things Journal},
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volume = {9},
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number = {17},
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pages = {15435--15459},
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issn = {2327-4662},
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|
doi = {10.1109/JIOT.2022.3176400},
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eventtitle = {{{IEEE Internet}} of {{Things Journal}}},
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keywords = {Artificial intelligence,Artificial intelligence (AI),Autonomous aerial vehicles,Cloud computing,edge AI,edge computing,Edge computing,edge intelligence,Internet of Things,Internet of Things (IoT),MEC,Servers,Task analysis,unmanned aerial vehicle (UAV)}
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}
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@article{nadafzadeh2019,
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title = {Design and Fabrication of an Intelligent Control System for Determination of Watering Time for Turfgrass Plant Using Computer Vision System and Artificial Neural Network},
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author = {Nadafzadeh, Maryam and Abdanan Mehdizadeh, Saman},
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date = {2019-10-01},
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journaltitle = {Precision Agriculture},
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shortjournal = {Precision Agric},
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volume = {20},
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number = {5},
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pages = {857--879},
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issn = {1573-1618},
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doi = {10.1007/s11119-018-9618-x},
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langid = {english},
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keywords = {Artificial neural network,Digital image processing,Drought stress,Genetic algorithm,Intelligent irrigation control}
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}
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@article{ramos-giraldo2020,
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title = {Drought {{Stress Detection Using Low-Cost Computer Vision Systems}} and {{Machine Learning Techniques}}},
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author = {Ramos-Giraldo, Paula and Reberg-Horton, Chris and Locke, Anna M. and Mirsky, Steven and Lobaton, Edgar},
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date = {2020-05},
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journaltitle = {IT Professional},
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volume = {22},
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number = {3},
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pages = {27--29},
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issn = {1941-045X},
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doi = {10.1109/MITP.2020.2986103},
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eventtitle = {{{IT Professional}}},
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keywords = {Agriculture,Climate change,Computer vision,Loss measurement,Machine learning,Stress measurement}
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}
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@inproceedings{ramos-giraldo2020a,
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title = {Low-Cost {{Smart Camera System}} for {{Water Stress Detection}} in {{Crops}}},
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booktitle = {2020 {{IEEE SENSORS}}},
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author = {Ramos-Giraldo, Paula and Reberg-Horton, S. Chris and Mirsky, Steven and Lobaton, Edgar and Locke, Anna M. and Henriquez, Esleyther and Zuniga, Ane and Minin, Artem},
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date = {2020-10},
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|
pages = {1--4},
|
|
issn = {2168-9229},
|
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doi = {10.1109/SENSORS47125.2020.9278744},
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eventtitle = {2020 {{IEEE SENSORS}}},
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keywords = {Agriculture,Cameras,Computational modeling,computer vision,edge and cloud computing,IoT,machine learning,Sensor systems,Sensors,smart farming,Stress,Temperature sensors}
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|
}
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@article{selvaraju2020,
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title = {Grad-{{CAM}}: {{Visual Explanations}} from {{Deep Networks}} via {{Gradient-based Localization}}},
|
|
shorttitle = {Grad-{{CAM}}},
|
|
author = {Selvaraju, Ramprasaath R. and Cogswell, Michael and Das, Abhishek and Vedantam, Ramakrishna and Parikh, Devi and Batra, Dhruv},
|
|
date = {2020-02},
|
|
journaltitle = {International Journal of Computer Vision},
|
|
shortjournal = {Int J Comput Vis},
|
|
volume = {128},
|
|
number = {2},
|
|
eprint = {1610.02391},
|
|
eprinttype = {arxiv},
|
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eprintclass = {cs},
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pages = {336--359},
|
|
issn = {0920-5691, 1573-1405},
|
|
doi = {10.1007/s11263-019-01228-7},
|
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keywords = {Computer Science - Artificial Intelligence,Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning}
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|
}
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@article{su2020,
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title = {Machine {{Learning-Based Crop Drought Mapping System}} by {{UAV Remote Sensing RGB Imagery}}},
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author = {Su, Jinya and Coombes, Matthew and Liu, Cunjia and Zhu, Yongchao and Song, Xingyang and Fang, Shibo and Guo, Lei and Chen, Wen-Hua},
|
|
date = {2020-01},
|
|
journaltitle = {Unmanned Systems},
|
|
shortjournal = {Un. Sys.},
|
|
volume = {08},
|
|
number = {01},
|
|
pages = {71--83},
|
|
publisher = {{World Scientific Publishing Co.}},
|
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issn = {2301-3850},
|
|
doi = {10.1142/S2301385020500053},
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keywords = {Area-wise classification,Support Vector Machine (SVM),Unmanned Aerial Vehicle (UAV),wheat drought mapping}
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}
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@article{virnodkar2020,
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title = {Remote Sensing and Machine Learning for Crop Water Stress Determination in Various Crops: A Critical Review},
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shorttitle = {Remote Sensing and Machine Learning for Crop Water Stress Determination in Various Crops},
|
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author = {Virnodkar, Shyamal S. and Pachghare, Vinod K. and Patil, V. C. and Jha, Sunil Kumar},
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date = {2020-10-01},
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|
journaltitle = {Precision Agriculture},
|
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shortjournal = {Precision Agric},
|
|
volume = {21},
|
|
number = {5},
|
|
pages = {1121--1155},
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issn = {1573-1618},
|
|
doi = {10.1007/s11119-020-09711-9},
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langid = {english},
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keywords = {Crop water stress,Crops,Machine learning,Remote sensing}
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}
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|
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@article{wakamori2020,
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title = {Multimodal Neural Network with Clustering-Based Drop for Estimating Plant Water Stress},
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author = {Wakamori, Kazumasa and Mizuno, Ryosuke and Nakanishi, Gota and Mineno, Hiroshi},
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date = {2020-01-01},
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journaltitle = {Computers and Electronics in Agriculture},
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shortjournal = {Computers and Electronics in Agriculture},
|
|
volume = {168},
|
|
pages = {105118},
|
|
issn = {0168-1699},
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doi = {10.1016/j.compag.2019.105118},
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langid = {english},
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keywords = {Image processing,Multimodal deep learning,Plant water stress,Time-series modeling}
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}
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@online{zheng2019,
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title = {Distance-{{IoU Loss}}: {{Faster}} and {{Better Learning}} for {{Bounding Box Regression}}},
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|
shorttitle = {Distance-{{IoU Loss}}},
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|
author = {Zheng, Zhaohui and Wang, Ping and Liu, Wei and Li, Jinze and Ye, Rongguang and Ren, Dongwei},
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date = {2019-11-19},
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number = {arXiv:1911.08287},
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eprint = {arXiv:1911.08287},
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eprinttype = {arxiv},
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doi = {10.48550/arXiv.1911.08287},
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pubstate = {preprint},
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keywords = {Computer Science - Computer Vision and Pattern Recognition}
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}
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@article{zhong2022,
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title = {Classification of {{Cassava Leaf Disease Based}} on a {{Non-Balanced Dataset Using Transformer-Embedded ResNet}}},
|
|
author = {Zhong, Yiwei and Huang, Baojin and Tang, Chaowei},
|
|
date = {2022-09},
|
|
journaltitle = {Agriculture},
|
|
volume = {12},
|
|
number = {9},
|
|
pages = {1360},
|
|
publisher = {{Multidisciplinary Digital Publishing Institute}},
|
|
issn = {2077-0472},
|
|
doi = {10.3390/agriculture12091360},
|
|
issue = {9},
|
|
langid = {english},
|
|
keywords = {cassava diseases,convolutional neural network,focal angular margin penalty softmax loss (FAMP-Softmax),intelligent agricultural engineering,transformer-embedded ResNet (T-RNet),unbalanced image samples}
|
|
}
|
|
|
|
@online{zhou2015,
|
|
title = {Learning {{Deep Features}} for {{Discriminative Localization}}},
|
|
author = {Zhou, Bolei and Khosla, Aditya and Lapedriza, Agata and Oliva, Aude and Torralba, Antonio},
|
|
date = {2015-12-13},
|
|
number = {arXiv:1512.04150},
|
|
eprint = {arXiv:1512.04150},
|
|
eprinttype = {arxiv},
|
|
doi = {10.48550/arXiv.1512.04150},
|
|
pubstate = {preprint},
|
|
keywords = {Computer Science - Computer Vision and Pattern Recognition}
|
|
}
|
|
|
|
@article{zhuang2017,
|
|
title = {Early Detection of Water Stress in Maize Based on Digital Images},
|
|
author = {Zhuang, Shuo and Wang, Ping and Jiang, Boran and Li, Maosong and Gong, Zhihong},
|
|
date = {2017-08-01},
|
|
journaltitle = {Computers and Electronics in Agriculture},
|
|
shortjournal = {Computers and Electronics in Agriculture},
|
|
volume = {140},
|
|
pages = {461--468},
|
|
issn = {0168-1699},
|
|
doi = {10.1016/j.compag.2017.06.022},
|
|
langid = {english},
|
|
keywords = {Early maize,Feature extraction,Gradient boosting decision tree,Image segmentation,Water stress}
|
|
}
|