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@online{Rules,
  author = {Valente UTAD},
  title = {{MicroMouseRules} Valente Description},
  year = 2015,
  url = {http://www.micromouse.utad.pt/?page_id=504&lang=en},
  urldate = {2018-03-01}
}


@online{AllJapan,
  author = {New Technology Foundation Japan},
  title = {All japan micromouse Description},
  year = 2017,
  url = {http://www.ntf.or.jp/mouse/micromouse2017/index_EN.html},
  urldate = {2018-03-12}
}

@INPROCEEDINGS{7886612, 
author={J. H. Su and C. S. Lee and C. W. Chen}, 
booktitle={2016 International Conference on Advanced Robotics and Intelligent Systems (ARIS)}, 
title={Sensor fusion algorithms for encoder resolution enhancement in educational mobile robots}, 
year={2016}, 
volume={}, 
number={}, 
pages={1-5}, 
keywords={Kalman filters;educational robots;micromechanical devices;mobile robots;motion control;observers;sensor fusion;signal resolution;Kalman filter;MEMS;accelerometers;educational mobile robots;encoder resolution enhancement;gyros;high-quality motion control;micro electro mechanical systems devices;observer;position signals;sensor fusion;velocity signals;Accelerometers;Kalman filters;Mobile robots;Observers;Robot sensing systems;Sensor fusion;accelerometer;encoder;mobile robots;sensor fusion}, 
doi={10.1109/ARIS.2016.7886612}, 
ISSN={}, 
month={Aug}}


@INPROCEEDINGS{sensarea,  author={P. {Bertolino}},  booktitle={2014 IEEE International Conference on Image Processing (ICIP)},   title={Sensarea, a general public video editing application},   year={2014},  volume={},  number={},  pages={3429-3431},  doi={10.1109/ICIP.2014.7025696}}



@article{DBLP:journals/corr/SelvarajuDVCPB16,
  author    = {Ramprasaath R. Selvaraju and
               Abhishek Das and
               Ramakrishna Vedantam and
               Michael Cogswell and
               Devi Parikh and
               Dhruv Batra},
  title     = {Grad-CAM: Why did you say that? Visual Explanations from Deep Networks
               via Gradient-based Localization},
  journal   = {CoRR},
  volume    = {abs/1610.02391},
  year      = {2016},
  url       = {http://arxiv.org/abs/1610.02391},
  archivePrefix = {arXiv},
  eprint    = {1610.02391},
  timestamp = {Mon, 13 Aug 2018 16:46:58 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/SelvarajuDVCPB16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@misc{zhou2015learning,
      title={Learning Deep Features for Discriminative Localization}, 
      author={Bolei Zhou and Aditya Khosla and Agata Lapedriza and Aude Oliva and Antonio Torralba},
      year={2015},
      eprint={1512.04150},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
 
 @INPROCEEDINGS{gradcamplus,

  author={Chattopadhay, Aditya and Sarkar, Anirban and Howlader, Prantik and Balasubramanian, Vineeth N},

  booktitle={2018 IEEE Winter Conference on Applications of Computer Vision (WACV)}, 

  title={Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks}, 

  year={2018},

  volume={},

  number={},

  pages={839-847},

  doi={10.1109/WACV.2018.00097}}
  
  
  @article{Pereira2019,
author = {Pereira, Carlos and Morais, R and Reis, Manuel},
doi = {10.3390/s19224850},
journal = {Sensors},
pages = {4850},
title = {{Deep Learning Techniques for Grape Plant Species Identification in Natural Images}},
volume = {19},
year = {2019}
}

@inproceedings{survey_plants_identification,
abstract = {Protection of biodiversity is quite essential and for this purpose we should know more about the species. Identification of plant species by using conventional hand-crafted features is complex. It is difficult for non-experts to remember the specific botanical terms. The idea of automatic identification of plant species is approaching reality. Machine learning and deep learning play an important role in this matter. The deep learning Convolutional Neural Networks (CNN) can apply to extract the features from leaf images. CNN or machine learning classifiers can be used for the classification of plant species. The deep learning methods outperform all handcrafted methods. This survey is about the identification of plant species using deep learning methods.},
author = {Sobha, P. G.M. and Thomas, Princy Ann},
booktitle = {2019 6th IEEE International Conference on Advances in Computing, Communication and Control, ICAC3 2019},
doi = {10.1109/ICAC347590.2019.9036796},
isbn = {9781728123868},
keywords = {CNN,convolutional neural networks,deep learning,plant species classification},
mendeley-groups = {[HCist] Classifica{\c{c}}{\~{a}}o},
pages = {1--6},
title = {{Deep Learning for Plant Species Classification Survey}},
year = {2019}
}

@inproceedings{Skrabanek2021,
author = {{\v{S}}krab{\'{a}}nek, Pavel and Dole{\v{z}}el, Petr and Matou{\v{s}}ek, Radomil and Junek, Petr},
booktitle = {Advances in Intelligent Systems and Computing},
doi = {10.1007/978-3-030-57802-2_21},
isbn = {9783030578015},
issn = {21945365},
keywords = {Agriculture mechanization,Densely connected convolutional network,In-field images,Recognition of grapevine varieties},
month = {sep},
pages = {216--225},
publisher = {Springer, Cham},
title = {{RGB Images Driven Recognition of Grapevine Varieties}},
url = {https://link.springer.com/chapter/10.1007/978-3-030-57802-2_21},
volume = {1268 AISC},
year = {2021}
}

@article{Cecotti2020,
author = {Cecotti, Hubert and Rivera, Agustin and Farhadloo, Majid and Pedroza, Miguel A.},
doi = {10.1016/j.eswa.2020.113588},
file = {:C\:/Users/Gabriel/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Cecotti et al. - 2020 - Grape detection with convolutional neural networks.pdf:pdf},
issn = {09574174},
journal = {Expert Systems with Applications},
keywords = {Agriculture,Deep learning,Machine learning,Viticulture},
month = {nov},
pages = {113588},
publisher = {Pergamon},
title = {{Grape detection with convolutional neural networks}},
volume = {159},
year = {2020}
}

@article{Liu2020,
author = {Liu, Bin and Ding, Zefeng and Tian, Liangliang and He, Dongjian and Li, Shuqin and Wang, Hongyan},
doi = {10.3389/fpls.2020.01082},
file = {:C\:/Users/Gabriel/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Liu et al. - Unknown - Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks.pdf:pdf},
issn = {1664462X},
journal = {Frontiers in Plant Science},
keywords = {convolutional neural networks,deep learning,disease identification,grape leaf diseases,image augmentation},
title = {{Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks}},
url = {www.frontiersin.org},
volume = {11},
year = {2020}
}

@article{Kangune2019,
author = {Kangune, Kaveri and Kulkarni, Vrushali and Kosamkar, Pranali},
doi = {10.1109/GCAT47503.2019.8978341},
isbn = {9781728136943},
journal = {2019 Global Conference for Advancement in Technology, GCAT 2019},
keywords = {Convolutional Neural Network(CNN),Deep Learning,Grapes ripeness estimation,Image pre-processing,Support Vector Machine(SVM)},
month = {oct},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{Grapes Ripeness Estimation using Convolutional Neural network and Support Vector Machine}},
year = {2019}
}
@article{Aravind2019,
author = {Aravind, K. R. and Raja, P. and Aniirudh, R. and Mukesh, K. V. and Ashiwin, R. and Vikas, G.},
doi = {10.1007/978-3-030-00665-5_150},
issn = {22129413},
journal = {Lecture Notes in Computational Vision and Biomechanics},
keywords = {AlexNet,Deep learning,Disease recognition,Grape crop,Multiclass support vector machine,Transfer learning},
pages = {1623--1633},
publisher = {Springer Netherlands},
title = {{Grape crop disease classification using transfer learning approach}},
volume = {30},
year = {2019}
}

@article{Kalampokas2021,
author = {Kalampokas, Τheofanis and Vrochidou, Εleni and Papakostas, George A. and Pachidis, Theodore and Kaburlasos, Vassilis G.},
doi = {10.1016/j.compag.2021.106220},
file = {:C\:/Users/Gabriel/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Kalampokas et al. - 2021 - Grape stem detection using regression convolutional neural networks.pdf:pdf},
issn = {01681699},
journal = {Computers and Electronics in Agriculture},
keywords = {Autonomous harvesting robot,Convolutional neural network,Grape stem detection,Precision agriculture,Regression model},
month = {jul},
pages = {106220},
publisher = {Elsevier},
title = {{Grape stem detection using regression convolutional neural networks}},
volume = {186},
year = {2021}
}

@article{Ramos2021,
author = {Ramos, Rodrigo P. and Gomes, J{\'{e}}ssica S. and Prates, Ricardo M. and {Simas Filho}, Eduardo F. and Teruel, Barbara J. and {dos Santos Costa}, Daniel},
doi = {10.1002/jsfa.10824},
file = {:C\:/Users/Gabriel/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ramos et al. - 2021 - Non-invasive setup for grape maturation classification using deep learning.pdf:pdf},
issn = {10970010},
journal = {Journal of the Science of Food and Agriculture},
keywords = {deep learning,grape maturation,image processing,post-harvest},
month = {mar},
number = {5},
pages = {2042--2051},
pmid = {32949040},
publisher = {John Wiley & Sons, Ltd},
title = {{Non-invasive setup for grape maturation classification using deep learning}},
url = {https://onlinelibrary.wiley.com/doi/full/10.1002/jsfa.10824 https://onlinelibrary.wiley.com/doi/abs/10.1002/jsfa.10824 https://onlinelibrary.wiley.com/doi/10.1002/jsfa.10824},
volume = {101},
year = {2021}
}

@article{Gutierrez2021,
author = {Guti{\'{e}}rrez, Salvador and Hern{\'{a}}ndez, In{\'{e}}s and Ceballos, Sara and Barrio, Ignacio and D{\'{i}}ez-Navajas, Ana M. and Tardaguila, Javier},
doi = {10.1016/j.compag.2021.105991},
file = {:C\:/Users/Gabriel/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Guti{\'{e}}rrez et al. - 2021 - Deep learning for the differentiation of downy mildew and spider mite in grapevine under field conditions.pdf:pdf},
issn = {01681699},
journal = {Computers and Electronics in Agriculture},
keywords = {Automated disease detection,Computer vision,Convolutional neural networks,Digital agriculture},
month = {mar},
pages = {105991},
publisher = {Elsevier},
title = {{Deep learning for the differentiation of downy mildew and spider mite in grapevine under field conditions}},
volume = {182},
year = {2021}
}

@article{Boulent2020,
author = {Boulent, Justine and St-Charles, Pierre-Luc and Foucher, Samuel and Th{\'{e}}au, J{\'{e}}rome},
doi = {10.3389/frai.2020.564878},
file = {:C\:/Users/Gabriel/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Boulent et al. - 2020 - Automatic Detection of Flavescence Dor{\'{e}}e Symptoms Across White Grapevine Varieties Using Deep Learning.pdf:pdf},
journal = {Frontiers in Artificial Intelligence},
keywords = {Flavescence dor{\'{e}}e,convolutional neural networks,explainable artificial intelligence,fully convolutional networks,grapevine yellows,plant diseases detection,precision viticulture,smart farming},
month = {nov},
pmid = {33733210},
publisher = {Frontiers Media SA},
title = {{Automatic Detection of Flavescence Dor{\'{e}}e Symptoms Across White Grapevine Varieties Using Deep Learning}},
url = {/pmc/articles/PMC7944144/ /pmc/articles/PMC7944144/?report=abstract https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7944144/},
volume = {3},
year = {2020}
}

@article{Alessandrini2021,
author = {Alessandrini, M. and {Calero Fuentes Rivera}, R. and Falaschetti, L. and Pau, D. and Tomaselli, V. and Turchetti, C.},
doi = {10.1016/j.dib.2021.106809},
file = {:C\:/Users/Gabriel/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Alessandrini et al. - 2021 - A grapevine leaves dataset for early detection and classification of esca disease in vineyards through mach.pdf:pdf},
issn = {23523409},
journal = {Data in Brief},
keywords = {Esca disease,Image classification,Image dataset,Machine learning,Plant diseases recognition},
month = {apr},
pages = {106809},
publisher = {Elsevier},
title = {{A grapevine leaves dataset for early detection and classification of esca disease in vineyards through machine learning}},
volume = {35},
year = {2021}
}

@article{Palacios2020,
author = {Palacios, Fernando and Bueno, Gloria and Salido, Jes{\'{u}}s and Diago, Maria P. and Hern{\'{a}}ndez, In{\'{e}}s and Tardaguila, Javier},
doi = {10.1016/j.compag.2020.105796},
file = {:C\:/Users/Gabriel/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Palacios et al. - 2020 - Automated grapevine flower detection and quantification method based on computer vision and deep learning from.pdf:pdf},
issn = {01681699},
journal = {Computers and Electronics in Agriculture},
keywords = {Non-invasive sensing technologies,Precision agriculture,Vitis vinifera L.,Yield estimation},
month = {nov},
pages = {105796},
publisher = {Elsevier},
title = {{Automated grapevine flower detection and quantification method based on computer vision and deep learning from on-the-go imaging using a mobile sensing platform under field conditions}},
volume = {178},
year = {2020}
}

@inproceedings{ADAO2019,
address = {Cham},
author = {Ad{\~{a}}o, Telmo and Pinho, Tatiana M. and Ferreira, Ant{\'{o}}nio and Sousa, Ant{\'{o}}nio and P{\'{a}}dua, Lu{\'{i}}s and Sousa, Jos{\'{e}} and Sousa, Joaquim J. and Peres, Emanuel and Morais, Raul},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-030-30241-2_23},
editor = {{Moura Oliveira}, Paulo and Novais, Paulo and Reis, Lu{\'{i}}s Paulo},
isbn = {9783030302405},
issn = {16113349},
keywords = {Ampelography,CNN,Douro Demarcated Region,Grapevine identification,Precision viticulture,ResNet,VGG,Wine Certification,Xception},
mendeley-groups = {[HCist] Classifica{\c{c}}{\~{a}}o},
pages = {258--271},
publisher = {Springer International Publishing},
title = {{Digital ampelographer: A CNN based preliminary approach}},
volume = {11804 LNAI},
year = {2019}
}

@article{Pereira2018,
author = {Pereira, Carlos S. and Morais, R. and Reis, M. J.C.S.},
doi = {10.1007/978-3-319-93000-8_12},
isbn = {9783319929996},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
keywords = {Color model,Leaf segmentation,Natural vineyard images,Threshold techniques},
month = {jun},
pages = {96--106},
publisher = {Springer, Cham},
title = {{Pixel-Based Leaf Segmentation from Natural Vineyard Images Using Color Model and Threshold Techniques}},
url = {https://link.springer.com/chapter/10.1007/978-3-319-93000-8_12},
volume = {10882 LNCS},
year = {2018}
}

@article{McInnes2018,
archivePrefix = {arXiv},
arxivId = {1802.03426},
author = {McInnes, Leland and Healy, John and Melville, James},
eprint = {1802.03426},
file = {:C\:/Users/Gabriel/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/McInnes, Healy, Melville - 2018 - UMAP Uniform Manifold Approximation and Projection for Dimension Reduction.pdf:pdf},
month = {feb},
title = {{UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction}},
url = {http://arxiv.org/abs/1802.03426},
year = {2018}
}

@article{Shantkumari2021,
author = {Shantkumari, M. and Uma, S. V.},
doi = {10.1007/s11042-020-09853-y},
file = {:C\:/Users/Gabriel/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Shantkumari, Uma - 2021 - Grape leaf segmentation for disease identification through adaptive Snake algorithm model.pdf:pdf},
issn = {15737721},
journal = {Multimedia Tools and Applications},
keywords = {Adaptive Snake model,Leaf disease,Segmentation},
month = {nov},
number = {6},
pages = {8861--8879},
publisher = {Springer},
title = {{Grape leaf segmentation for disease identification through adaptive Snake algorithm model}},
url = {https://link.springer.com/article/10.1007/s11042-020-09853-y},
volume = {80},
year = {2021}
}

@article{Adeel2019,
author = {Adeel, Alishba and Khan, Muhammad Attique and Sharif, Muhammad and Azam, Faisal and Shah, Jamal Hussain and Umer, Tariq and Wan, Shaohua},
doi = {10.1016/j.suscom.2019.08.002},
file = {:C\:/Users/Gabriel/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Adeel et al. - 2019 - Diagnosis and recognition of grape leaf diseases An automated system based on a novel saliency approach and canoni.pdf:pdf},
issn = {22105379},
journal = {Sustainable Computing: Informatics and Systems},
keywords = {Contrast stretching,Features fusion,Fruit diseases,Recognition,Reduction,Saliency estimation},
month = {dec},
pages = {100349},
publisher = {Elsevier},
title = {{Diagnosis and recognition of grape leaf diseases: An automated system based on a novel saliency approach and canonical correlation analysis based multiple features fusion}},
volume = {24},
year = {2019}
}

@article{Garcia-Garcia2017,
archivePrefix = {arXiv},
arxivId = {1704.06857},
author = {Garcia-Garcia, Alberto and Orts-Escolano, Sergio and Oprea, Sergiu and Villena-Martinez, Victor and Garcia-Rodriguez, Jose},
eprint = {1704.06857},
file = {:C\:/Users/Gabriel/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Garcia-Garcia et al. - 2017 - A Review on Deep Learning Techniques Applied to Semantic Segmentation.pdf:pdf},
keywords = {Deep Learning,Index Terms-Semantic Segmentation,Object Segmentation !,Scene Labeling},
month = {apr},
title = {{A Review on Deep Learning Techniques Applied to Semantic Segmentation}},
url = {http://arxiv.org/abs/1704.06857},
year = {2017}
}

@article{Ghosh2019,
archivePrefix = {arXiv},
arxivId = {1907.06119},
author = {Ghosh, Swarnendu and Das, Nibaran and Das, Ishita and Maulik, Ujjwal},
doi = {10.1145/3329784},
eprint = {1907.06119},
issn = {15577341},
journal = {ACM Computing Surveys},
keywords = {Convolutional neural networks,Deep learning,Semantic image segmentation},
month = {aug},
number = {4},
publisher = {
		ACM
		PUB27
		New York, NY, USA
	},
title = {{Understanding deep learning techniques for image segmentation}},
url = {https://dl.acm.org/doi/abs/10.1145/3329784},
volume = {52},
year = {2019}
}

@article{ParulJadhav2021,
author = {Parul Jadhav, Dhanashree Barbole},
doi = {10.17762/itii.v9i1.138},
file = {:C\:/Users/Gabriel/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ms. Dhanashree Barbole - 2021 - COMPARATIVE ANALYSIS OF DEEP LEARNING ARCHITECTURES FOR GRAPE CLUSTER INSTANCE SEGMENTATION.pdf:pdf},
issn = {2203-1731},
journal = {Information Technology in Industry},
keywords = {Mask RCNN,Modified U-net,Performance,Segmentation of grape cluster,U-net,YOLO v3},
month = {mar},
number = {1},
pages = {344--352},
publisher = {Auricle Technologies, Pvt., Ltd.},
title = {{Comparative Analysis of Deep Learning Architectures for Grape Cluster Instance Segmentation}},
url = {http://it-in-industry.org/index.php/itii/article/view/138},
volume = {9},
year = {2021}
}


@article{Redmon2018,
archivePrefix = {arXiv},
arxivId = {1804.02767},
author = {Redmon, Joseph and Farhadi, Ali},
eprint = {1804.02767},
file = {:C\:/Users/Gabriel/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Redmon, Farhadi - 2018 - YOLOv3 An Incremental Improvement.pdf:pdf},
month = {apr},
title = {{YOLOv3: An Incremental Improvement}},
url = {http://arxiv.org/abs/1804.02767},
year = {2018}
}

@article{maskrcnn,
archivePrefix = {arXiv},
arxivId = {1703.06870},
author = {He, Kaiming and Gkioxari, Georgia and Doll{\'{a}}r, Piotr and Girshick, Ross},
doi = {10.1109/TPAMI.2018.2844175},
eprint = {1703.06870},
issn = {19393539},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Instance segmentation,convolutional neural network,object detection,pose estimation},
number = {2},
pages = {386--397},
pmid = {29994331},
title = {{Mask R-CNN}},
url = {http://arxiv.org/abs/1703.06870},
volume = {42},
year = {2020}
}

@article{unet,
archivePrefix = {arXiv},
arxivId = {1505.04597},
author = {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
eprint = {1505.04597},
journal = {CoRR},
title = {{U-Net: Convolutional Networks for Biomedical Image Segmentation}},
url = {http://arxiv.org/abs/1505.04597},
volume = {abs/1505.0},
year = {2015}
}

@article{Yang2020,
author = {Yang, Kunlong and Zhong, Weizhen and Li, Fengguo},
doi = {10.3390/agronomy10111721},
issn = {20734395},
journal = {Agronomy},
keywords = {Deep learning,Image segmentation,Mask R-CNN,Plant classification,VGG16},
month = {nov},
number = {11},
pages = {1721},
publisher = {Multidisciplinary Digital Publishing Institute},
title = {{Leaf segmentation and classification with a complicated background using deep learning}},
url = {https://www.mdpi.com/2073-4395/10/11/1721/htm https://www.mdpi.com/2073-4395/10/11/1721},
volume = {10},
year = {2020}
}

@article{Gimenez-Gallego2020,
author = {Gim{\'{e}}nez-Gallego, Jaime and Gonz{\'{a}}lez-Teruel, Juan D. and Jim{\'{e}}nez-Buend{\'{i}}a, Manuel and Toledo-Moreo, Ana B. and Soto-Valles, Fulgencio and Torres-S{\'{a}}nchez, Roque},
doi = {10.3390/app10010202},
file = {:C\:/Users/Gabriel/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Gim{\'{e}}nez-Gallego et al. - 2019 - Segmentation of Multiple Tree Leaves Pictures with Natural Backgrounds using Deep Learning for Image-Bas.pdf:pdf},
issn = {20763417},
journal = {Applied Sciences (Switzerland)},
keywords = {CWSI,Clustering,Deep learning,Deficit irrigation,Image segmentation,Model training,SVM,Thermography},
month = {dec},
number = {1},
pages = {202},
publisher = {Multidisciplinary Digital Publishing Institute},
title = {{Segmentation of multiple tree leaves pictures with natural backgrounds using deep learning for image-based agriculture applications}},
url = {https://www.mdpi.com/2076-3417/10/1/202/htm https://www.mdpi.com/2076-3417/10/1/202},
volume = {10},
year = {2020}
}

@article{Xiong2020,
author = {Xiong, Yonghua and Liang, Longfei and Wang, Lin and She, Jinhua and Wu, Min},
doi = {10.1016/j.compag.2020.105712},
file = {:C\:/Users/gabri/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Xiong et al. - 2020 - Identification of cash crop diseases using automatic image segmentation algorithm and deep learning with expanded.pdf:pdf},
issn = {01681699},
journal = {Computers and Electronics in Agriculture},
keywords = {Convolutional neural network,Crop disease,Deep learning,Identification,Image segmentation},
month = {oct},
pages = {105712},
publisher = {Elsevier},
title = {{Identification of cash crop diseases using automatic image segmentation algorithm and deep learning with expanded dataset}},
volume = {177},
year = {2020}
}


@inproceedings{Kirti2021,
author = {Kirti, Kirti and Rajpal, Navin and Yadav, Jyotsna},
booktitle = {Proceedings - IEEE 2021 International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2021},
doi = {10.1109/ICCCIS51004.2021.9397205},
isbn = {9781728185293},
keywords = {Agriculture,Crop,Deep Learning,Disease Identification,Grape Plant,Pattern Recognition,ResNet 50},
month = {feb},
pages = {97--101},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{Black measles disease identification in grape plant (vitis vinifera) using deep learning}},
year = {2021}
}

@article{Hughes2015,
archivePrefix = {arXiv},
arxivId = {1511.08060},
author = {Hughes, David. P. and Salathe, Marcel},
eprint = {1511.08060},
file = {:home/gabriel/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Hughes, Salathe - 2015 - An open access repository of images on plant health to enable the development of mobile disease diagnostics.pdf:pdf},
keywords = {crowdsourcing,digital epidemiology,disease diagnosis,food security,machine learning,plant pathology},
month = {nov},
title = {{An open access repository of images on plant health to enable the development of mobile disease diagnostics}},
url = {https://arxiv.org/abs/1511.08060v2},
year = {2015}
}

@article{DBLP:journals/corr/abs-1807-10931,
archivePrefix = {arXiv},
arxivId = {1807.10931},
author = {Ward, Daniel and Moghadam, Peyman and Hudson, Nicolas},
eprint = {1807.10931},
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month = {apr},
title = {{"Why Should You Trust My Explanation?" Understanding Uncertainty in LIME Explanations}},
url = {https://arxiv.org/abs/1904.12991v2 http://arxiv.org/abs/1904.12991},
year = {2019}
}

@inproceedings{Visani2020,
archivePrefix = {arXiv},
arxivId = {2006.05714},
author = {Visani, Giorgio and Bagli, Enrico and Chesani, Federico},
booktitle = {CEUR Workshop Proceedings},
eprint = {2006.05714},
file = {:C\:/Users/gabri/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Visani, Bagli, Chesani - 2020 - OptiLIME Optimized LIME Explanations for Diagnostic Computer Algorithms.pdf:pdf},
issn = {16130073},
keywords = {Explainable AI (XAI),Explanation,Healthcare,Interpretable Machine Learning,LIME,Model Agnostic,Stability},
month = {jun},
publisher = {CEUR-WS},
title = {{OptiLIME: Optimized lime explanations for diagnostic computer algorithms}},
url = {https://arxiv.org/abs/2006.05714v2},
volume = {2699},
year = {2020}
}

@inproceedings{Schallner2020,
archivePrefix = {arXiv},
arxivId = {1910.07856},
author = {Schallner, Ludwig and Rabold, Johannes and Scholz, Oliver and Schmid, Ute},
booktitle = {Communications in Computer and Information Science},
doi = {10.1007/978-3-030-43823-4_13},
eprint = {1910.07856},
file = {:C\:/Users/gabri/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Schallner et al. - 2019 - Effect of Superpixel Aggregation on Explanations in LIME -- A Case Study with Biological Data.pdf:pdf},
isbn = {9783030438227},
issn = {18650937},
keywords = {Explainable AI,LIME,Superpixel},
month = {oct},
pages = {147--158},
publisher = {Springer},
title = {{Effect of superpixel aggregation on explanations in LIME – A case study with biological data}},
url = {https://arxiv.org/abs/1910.07856v1},
volume = {1167 CCIS},
year = {2020}
}

@article{Garreau2020,
archivePrefix = {arXiv},
arxivId = {2001.03447},
author = {Garreau, Damien and von Luxburg, Ulrike},
eprint = {2001.03447},
file = {:C\:/Users/gabri/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Garreau, von Luxburg - 2020 - Explaining the Explainer A First Theoretical Analysis of LIME.pdf:pdf},
month = {jan},
title = {{Explaining the Explainer: A First Theoretical Analysis of LIME}},
url = {http://arxiv.org/abs/2001.03447},
year = {2020}
}

@article{Stiffler2018,
author = {Stiffler, Mitchell and Hudler, Adam and Lee, Eunjin and Braines, Dave and Mott, David and Harborne, Daniel},
file = {:C\:/Users/gabri/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Stiffler et al. - Unknown - An Analysis of Reliability Using LIME with Deep Learning Models.pdf:pdf},
keywords = {Deep Learning,Interpretability,Machine Learning},
title = {{An Analysis of Reliability Using LIME with Deep Learning Models}},
url = {https://dais-ita.org/sites/default/files/2534.pdf},
year = {2018}
}

@article{Zheng2016,
arxivId = {1604.00133},
author = {Zheng, Liang and Zhao, Yali and Wang, Shengjin and Wang, Jingdong and Tian, Qi},
eprint = {1604.00133},
file = {:C\:/Users/gabri/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Zheng et al. - 2016 - Good Practice in CNN Feature Transfer.pdf:pdf},
keywords = {()},
month = {apr},
title = {{Good Practice in CNN Feature Transfer}},
url = {https://arxiv.org/abs/1604.00133v1 http://arxiv.org/abs/1604.00133},
year = {2016}
}

@misc{Zhang,
author = {Zhang, Chengwei},
title = {{Tony607/Focal_Loss_Keras: Multi-class classification with focal loss for imbalanced datasets}},
url = {https://github.com/Tony607/Focal_Loss_Keras},
urldate = {2021-09-16},
year = {2019}
}

@misc{JasonBrownlee2014,
author = {{Jason Brownlee}},
booktitle = {Machine Learning Mastery},
title = {{Classification Accuracy is Not Enough: More Performance Measures You Can Use}},
url = {https://machinelearningmastery.com/classification-accuracy-is-not-enough-more-performance-measures-you-can-use/},
urldate = {2021-09-16},
year = {2014}
}

@misc{Griffo2017,
author = {Griffo, Umberto},
title = {{umbertogriffo/focal-loss-keras: Binary and Categorical Focal loss implementation in Keras.}},
url = {https://github.com/umbertogriffo/focal-loss-keras},
urldate = {2021-09-16},
year = {2017}
}

@misc{Woof2019,
author = {Woof, Samson},
year = {2019},
title = {samson6460/tf_keras_gradcamplusplus: tensorflow.keras implementation of gradcam and gradcam++},
url = {https://github.com/samson6460/tf_keras_gradcamplusplus},
urldate = {2021-09-16}
}

@misc{Du2020,
author = {Du, Shuchen},
booktitle = {AI Salon},
title = {{Understanding Dice Loss for Crisp Boundary Detection}},
url = {https://medium.com/ai-salon/understanding-dice-loss-for-crisp-boundary-detection-bb30c2e5f62b},
urldate = {2021-09-20},
year = {2020}
}

@incollection{Giacosa2019,
author = {Giacosa, Elisa},
booktitle = {Production and Management of Beverages},
doi = {10.1016/b978-0-12-815260-7.00012-2},
month = {jan},
pages = {361--380},
publisher = {Woodhead Publishing},
title = {{Wine Consumption in a Certain Territory. Which Factors May Have Impact on It?}},
year = {2019}
}

@article{TheInternationalOrganisationofVineandWine2018,
author = {{The International Organisation of Vine and Wine}},
file = {:C\:/Users/gabri/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/The International Organisation of Vine and Wine - 2018 - STATE OF THE WORLD VITIVINICULTURAL SECTOR IN 2020.pdf:pdf},
title = {{STATE OF THE WORLD VITIVINICULTURAL SECTOR IN 2020}},
url = {https://www.oiv.int/public/medias/7909/oiv-state-of-the-world-vitivinicultural-sector-in-2020.pdf},
year = {2020}
}

@book{Hogg2018,
address = {Vila Real},
author = {Hogg, Tim and Rebelo, Jo{\~{a}}o},
isbn = {978-989-704-344-4},
publisher = {Universidade de Tr{\'{a}}s-os-Montes e Alto Douro},
title = {{Rumo estrat{\'{e}}gico para o setor dos vinhos do Porto e Douro}},
year = {2018}
}

@misc{Caldeira2018,
author = {Caldeira, Carlos},
booktitle = {Agriculta e Mar Actual},
month = {sep},
title = {{Ind{\'{u}}stria do vinho contribui para a economia portuguesa com valor acrescentado}},
url = {https://agriculturaemar.com/novo-banco-industria-do-vinho-contribui-para-a-economia-portuguesa-com-valor-acrescentado/},
urldate = {2021-09-23},
year = {2018}
}

@article{Valverde2021,
author = {Valverde, Carmen},
file = {:C\:/Users/gabri/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Valverde - 2021 - Portuguese Wine Exports Rise in 2020 Despite COVID-19 Challenges.pdf:pdf},
title = {{Portuguese Wine Exports Rise in 2020 Despite COVID-19 Challenges}},
year = {2021}
}

@article{MONCAYO2016185,
author = {Moncayo, S and Rosales, J D and Izquierdo-Hornillos, R and Anzano, J and Caceres, J O},
doi = {https://doi.org/10.1016/j.talanta.2016.05.059},
issn = {0039-9140},
journal = {Talanta},
keywords = {LIBS,Laser Induced Breakdown Spectroscopy,Neural networks,PDO,Protected designation of origin,Wine},
mendeley-groups = {[HCist] Classifica{\c{c}}{\~{a}}o},
pages = {185--191},
title = {{Classification of red wine based on its protected designation of origin (PDO) using Laser-induced Breakdown Spectroscopy (LIBS)}},
url = {http://www.sciencedirect.com/science/article/pii/S0039914016303824},
volume = {158},
year = {2016}
}

@article{ampelografia,
author = {Garcia-Mu{\~{n}}oz, Sonia and Mu{\~{n}}oz-Organero, G and de Andr{\'{e}}s, Mar{\'{i}}a and Cabello, Felix},
doi = {10.20870/oeno-one.2011.45.3.1497},
journal = {Journal International des Sciences de la Vigne et du Vin},
pages = {125--137},
title = {{Ampelography - An old technique with future uses: the case of minor varieties of Vitis vinifera L. from the Balearic Islands}},
volume = {45},
year = {2011}
}

@article{tassie2010vine,
author = {Tassie, Libby},
journal = {The Grape and Wine Research and Development Corporation (GWRDC) Innovators network},
mendeley-groups = {[HCist] Classifica{\c{c}}{\~{a}}o},
title = {{Vine identification – knowing what you have}},
url = {https://www.wineaustralia.com/getmedia/21669eff-05de-41d9-9ef8-283b1e01edcb/201008-Vine-identification.pdf},
year = {2010}
}

@article{Xie2017,
archivePrefix = {arXiv},
arxivId = {1611.05431},
author = {Xie, Saining and Girshick, Ross and Doll{\'{a}}r, Piotr and Tu, Zhuowen and He, Kaiming},
doi = {10.1109/CVPR.2017.634},
eprint = {1611.05431},
file = {:C\:/Users/gabri/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Xie et al. - 2016 - Aggregated Residual Transformations for Deep Neural Networks.pdf:pdf},
isbn = {9781538604571},
journal = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017},
month = {nov},
pages = {5987--5995},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{Aggregated residual transformations for deep neural networks}},
url = {https://arxiv.org/abs/1611.05431v2},
volume = {2017-January},
year = {2017}
}

@inproceedings{Mukhoti2020,
archivePrefix = {arXiv},
arxivId = {2002.09437},
author = {Mukhoti, Jishnu and Kulharia, Viveka and Sanyal, Amartya and Golodetz, Stuart and Torr, Philip H.S. and Dokania, Puneet K.},
booktitle = {Advances in Neural Information Processing Systems},
eprint = {2002.09437},
file = {:C\:/Users/gabri/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Mukhoti et al. - 2020 - Calibrating Deep Neural Networks using Focal Loss.pdf:pdf},
issn = {10495258},
month = {feb},
publisher = {Neural information processing systems foundation},
title = {{Calibrating deep neural networks using focal loss}},
url = {https://arxiv.org/abs/2002.09437v2},
volume = {2020-Decem},
year = {2020}
}

@INPROCEEDINGS{8578843,  author={Hu, Jie and Shen, Li and Sun, Gang},  booktitle={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},   title={Squeeze-and-Excitation Networks},   year={2018},  volume={},  number={},  pages={7132-7141},  doi={10.1109/CVPR.2018.00745}}

@inproceedings{Howard2019,
archivePrefix = {arXiv},
arxivId = {1905.02244},
author = {Howard, Andrew and Sandler, Mark and Chen, Bo and Wang, Weijun and Chen, Liang Chieh and Tan, Mingxing and Chu, Grace and Vasudevan, Vijay and Zhu, Yukun and Pang, Ruoming and Le, Quoc and Adam, Hartwig},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
doi = {10.1109/ICCV.2019.00140},
eprint = {1905.02244},
file = {:C\:/Users/gabri/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Howard et al. - 2019 - Searching for MobileNetV3.pdf:pdf},
isbn = {9781728148038},
issn = {15505499},
month = {may},
pages = {1314--1324},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{Searching for mobileNetV3}},
url = {https://arxiv.org/abs/1905.02244v5},
volume = {2019-Octob},
year = {2019}
}

@inproceedings{Woo2018,
archivePrefix = {arXiv},
arxivId = {1807.06521},
author = {Woo, Sanghyun and Park, Jongchan and Lee, Joon Young and Kweon, In So},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-030-01234-2_1},
eprint = {1807.06521},
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isbn = {9783030012335},
issn = {16113349},
keywords = {Attention mechanism,Gated convolution,Object recognition},
month = {jul},
pages = {3--19},
publisher = {Springer Verlag},
title = {{CBAM: Convolutional block attention module}},
url = {https://arxiv.org/abs/1807.06521v2},
volume = {11211 LNCS},
year = {2018}
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@article{Schramowski2020,
archivePrefix = {arXiv},
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doi = {10.1038/s42256-020-0212-3},
eprint = {2001.05371},
issn = {25225839},
journal = {Nature Machine Intelligence},
keywords = {Machine learning,Plant sciences},
month = {aug},
number = {8},
pages = {476--486},
publisher = {Nature Publishing Group},
title = {{Making deep neural networks right for the right scientific reasons by interacting with their explanations}},
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volume = {2},
year = {2020}
}

@article{Luvisi2016,
author = {Luvisi, Andrea},
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file = {:C\:/Users/Gabriel/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Luvisi - 2016 - Electronic identification technology for agriculture, plant, and food. A review.pdf:pdf},
issn = {1773-0155},
journal = {Agronomy for Sustainable Development 2016 36:1},
keywords = {Agriculture,Soil Science & Conservation,Sustainable Development},
month = {feb},
number = {1},
pages = {1--14},
publisher = {Springer},
title = {{Electronic identification technology for agriculture, plant, and food. A review}},
url = {https://link.springer.com/article/10.1007/s13593-016-0352-3},
volume = {36},
year = {2016}
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@book{poole2017artificial,
  title={Artificial Intelligence: Foundations of Computational Agents},
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}

@book{rebala2019introduction,
  title={An Introduction to Machine Learning},
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}

@misc{EasyTensorFlow,
author = {{Easy TensorFlow}},
title = {{Convolutional Neural Nets}},
url = {https://www.easy-tensorflow.com/tf-tutorials/convolutional-neural-nets-cnns},
urldate = {2021-10-25}
}

@misc{Shuchen2019,
author = {Shuchen, Du},
booktitle = {AI Salon},
month = {dec},
title = {{Demystifying Focal Loss I: A More Focused Cross Entropy Loss}},
url = {https://medium.com/ai-salon/demystifying-focal-loss-i-a-more-focused-version-of-cross-entropy-loss-f49e4b044213},
urldate = {2021-10-25},
year = {2019}
}

@article{Chitwood2014,
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doi = {10.1104/PP.113.229708},
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issn = {0032-0889},
journal = {Plant Physiology},
month = {jan},
number = {1},
pages = {259--272},
publisher = {Oxford Academic},
title = {{A Modern Ampelography: A Genetic Basis for Leaf Shape and Venation Patterning in Grape}},
url = {https://academic.oup.com/plphys/article/164/1/259/6112771},
volume = {164},
year = {2014}
}

@book{galet1971precis,
  title={Precis D'Ampelographie Pratique (Par) P. Galet},
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  year={1971}
}

@article{Pavek2003,
author = {Pavek, Diane S. and Lamboy, Warren F. and Garvey, Edward J.},
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issn = {1573-5109},
journal = {Genetic Resources and Crop Evolution 2003 50:2},
keywords = {Agriculture,Plant Genetics and Genomics,Plant Physiology,Plant Sciences,Plant Systematics/Taxonomy/Biogeography},
month = {mar},
number = {2},
pages = {165--173},
publisher = {Springer},
title = {{Selecting in situ conservation sites for grape genetic resources in the USA}},
url = {https://link.springer.com/article/10.1023/A:1022947605916},
volume = {50},
year = {2003}
}

@article{P2004,
author = {This, P. and Jung, A. and Boccacci, P. and Borrego, J. and Botta, R. and Costantini, L. and Crespan, M. and Dangl, G. S. and Eisenheld, C. and Ferreira-Monteiro, F. and Grando, S. and Ib{\'{a}}{\~{n}}ez, J. and Lacombe, T. and Laucou, V. and Magalh{\~{a}}es, R. and Meredith, C. P. and Milani, N. and Peterlunger, E. and Regner, F. and Zulini, L. and Maul, E.},
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issn = {00405752},
journal = {Theoretical and Applied Genetics},
keywords = {A Jung,Alleles,Automation,Chromosome Mapping,DNA,DNA Primers,E Maul,MEDLINE,Microsatellite Repeats*,NCBI,NIH,NLM,National Center for Biotechnology Information,National Institutes of Health,National Library of Medicine,Non-U.S. Gov't,P This,Plant / genetics,Plant / isolation & purification,Polymerase Chain Reaction / methods,PubMed Abstract,Research Support,Species Specificity,Vitis / classification,Vitis / genetics*,Wine,doi:10.1007/s00122-004-1760-3,pmid:15565426},
month = {nov},
number = {7},
pages = {1448--1458},
pmid = {15565426},
publisher = {Theor Appl Genet},
title = {{Development of a standard set of microsatellite reference alleles for identification of grape cultivars}},
url = {https://pubmed.ncbi.nlm.nih.gov/15565426/},
volume = {109},
year = {2004}
}

@article{Calo1996,
author = {Calo, A. and Tomasi, D. and Crespan, M. and Costacurta, A.},
doi = {10.17660/actahortic.1996.427.27},
isbn = {9789066059085},
issn = {05677572},
journal = {Acta Horticulturae},
month = {dec},
pages = {217--231},
publisher = {International Society for Horticultural Science},
title = {{Relationship between environmental factors and the dynamics of growth and composition of the grapevine}},
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year = {1996}
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@article{Cunha2009,
author = {Cunha, J. and Santos, M. Teixeira and Carneiro, L. C. and Fevereiro, P. and Eiras-Dias, J. E.},
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journal = {Genetic Resources and Crop Evolution},
keywords = {Morphology,Multivariate analysis,Vitis vinifera L. ssp. sylvestris,Vitis vinifera L. ssp. vinifera,cpSSR},
month = {nov},
number = {7},
pages = {975--989},
title = {{Portuguese traditional grapevine cultivars and wild vines (Vitis vinifera L.) share morphological and genetic traits}},
volume = {56},
year = {2009}
}

@article{Neto2006,
author = {Neto, Jo{\~{a}}o Camargo and Meyer, George E. and Jones, David D. and Samal, Ashok K.},
doi = {10.1016/J.COMPAG.2005.09.004},
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issn = {0168-1699},
journal = {Computers and Electronics in Agriculture},
keywords = {Discriminant analysis,Elliptic Fourier,Leaves,Machine vision,Shape features},
month = {feb},
number = {2},
pages = {121--134},
publisher = {Elsevier},
title = {{Plant species identification using Elliptic Fourier leaf shape analysis}},
volume = {50},
year = {2006}
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@inproceedings{deng2009imagenet,
author = {Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle = {2009 IEEE conference on computer vision and pattern recognition},
organization = {Ieee},
pages = {248--255},
title = {{Imagenet: A large-scale hierarchical image database}},
year = {2009}
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@article{DBLP:journals/corr/LinMBHPRDZ14,
  author    = {Tsung{-}Yi Lin and
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               Ross B. Girshick and
               James Hays and
               Pietro Perona and
               Deva Ramanan and
               Piotr Doll{\'{a}}r and
               C. Lawrence Zitnick},
  title     = {Microsoft {COCO:} Common Objects in Context},
  journal   = {CoRR},
  volume    = {abs/1405.0312},
  year      = {2014},
  url       = {http://arxiv.org/abs/1405.0312},
  eprinttype = {arXiv},
  eprint    = {1405.0312},
  timestamp = {Mon, 13 Aug 2018 16:48:13 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/LinMBHPRDZ14.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/MilletariNA16,
  author    = {Fausto Milletari and
               Nassir Navab and
               Seyed{-}Ahmad Ahmadi},
  title     = {V-Net: Fully Convolutional Neural Networks for Volumetric Medical
               Image Segmentation},
  journal   = {CoRR},
  volume    = {abs/1606.04797},
  year      = {2016},
  url       = {http://arxiv.org/abs/1606.04797},
  eprinttype = {arXiv},
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@article{Shruti2020,
   title={A survey of loss functions for semantic segmentation},
   url={http://dx.doi.org/10.1109/CIBCB48159.2020.9277638},
   DOI={10.1109/cibcb48159.2020.9277638},
   journal={2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)},
   publisher={IEEE},
   author={Jadon, Shruti},
   year={2020},
   month={Oct}
}

@misc{Pedersen2021,
author = {Pedersen, Thomas Lin and Benesty, Micha{\"{e}}l},
month = {feb},
title = {{Understanding lime}},
url = {https://cran.r-project.org/web/packages/lime/vignettes/Understanding_lime.html},
urldate = {2021-11-14},
year = {2021}
}