add survey table

Author: nheider

docs/static/files/survey.tsv

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+Title	Year	Authors	Link
+A survey of public datasets for computer vision tasks in precision agriculture	2020	Lu, Yuzhen and Young, Sierra	https://www.sciencedirect.com/science/article/pii/S0168169920312709
+A comprehensive survey on weed and crop classification using machine learning and deep learning	2024	Adhinata, Faisal Dharma and Wahyono and Sumiharto, Raden	https://www.sciencedirect.com/science/article/pii/S2589721724000278
+A Survey of Computer Vision Technologies In Urban and Controlled-environment Agriculture	2023	Luo, Jiayun and Li, Boyang and Leung, Cyril	http://arxiv.org/abs/2210.11318
+Deep-learning-based counting methods, datasets, and applications in agriculture: a review	2023	Farjon, Guy and Huijun, Liu and Edan, Yael	https://doi.org/10.1007/s11119-023-10034-8
+RGB-D datasets for robotic perception in site-specific agricultural operations—A survey	2023	Kurtser, Polina and Lowry, Stephanie	https://www.sciencedirect.com/science/article/pii/S0168169923004234
+Generative adversarial networks (GANs) for image augmentation in agriculture: A systematic review	2022	Lu, Yuzhen and Chen, Dong and Olaniyi, Ebenezer and Huang, Yanbo	https://www.sciencedirect.com/science/article/pii/S0168169922005233
+A survey of deep learning techniques for weed detection from images	2021	Hasan, A S M Mahmudul and Sohel, Ferdous and Diepeveen, Dean and Laga, Hamid and Jones, Michael G. K.	https://www.sciencedirect.com/science/article/pii/S0168169921000855
+Deep learning in agriculture: A survey	2018	Kamilaris, Andreas and Prenafeta-Boldú, Francesc X.	https://www.sciencedirect.com/science/article/pii/S0168169917308803
+A comprehensive review of remote sensing platforms, sensors, and applications in nut crops	2022	Jafarbiglu, Hamid and Pourreza, Alireza	https://www.sciencedirect.com/science/article/pii/S0168169922001612
+A review of unmanned aerial vehicle-based methods for plant stand count evaluation in row crops	2022	Pathak, Harsh and Igathinathane, C. and Zhang, Z. and Archer, D. and Hendrickson, J.	https://www.sciencedirect.com/science/article/pii/S0168169922003817
+An extensive review on agricultural robots with a focus on their perception systems	2023	Thakur, Abhishek and Venu, Sangeeth and Gurusamy, Muralimohan	https://www.sciencedirect.com/science/article/pii/S0168169923005343
+A review on weed detection using ground-based machine vision and image processing techniques	2019	Wang, Aichen and Zhang, Wen and Wei, Xinhua	https://www.sciencedirect.com/science/article/pii/S0168169918317150
+Current trends in the use of thermal imagery in assessing plant stresses: A review	2024	Adhitama Putra Hernanda, Reza and Lee, Hoonsoo and Cho, Jung-il and Kim, Geonwoo and Cho, Byoung-Kwan and Kim, Moon S.	https://www.sciencedirect.com/science/article/pii/S0168169924006185
+A review of core agricultural robot technologies for crop productions	2023	Yang, Qinghua and Du, Xiaoqiang and Wang, Zhiheng and Meng, Zhichao and Ma, Zenghong and Zhang, Qin	https://www.sciencedirect.com/science/article/pii/S0168169923000893
+Advanced agricultural disease image recognition technologies: A review	2022	Yuan, Yuan and Chen, Lei and Wu, Huarui and Li, Lin	https://www.sciencedirect.com/science/article/pii/S2214317321000032
+Impact of dataset on the study of crop disease image recognition	2022	Yuan, Yuan and Chen, Lei and Ren, Yuchen and Wang, Shimei and Li, Yin	https://www.ijabe.org/index.php/ijabe/article/view/7005
+Deep learning techniques for in-crop weed recognition in large-scale grain production systems: a review	2024	Hu, Kun and Wang, Zhiyong and Coleman, Guy and Bender, Asher and Yao, Tingting and Zeng, Shan and Song, Dezhen and Schumann, Arnold and Walsh, Michael	https://doi.org/10.1007/s11119-023-10073-1