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Awesome Label-Efficient Learning in Agricutlture: A Comprehensive Review

Awesome

A curated list of awesome Label-efficient Learning in Agricutlture papers 🔥🔥🔥.

Currently maintained by Jiajia Li @ MSU and Dong Chen @ MSU.

Work still in progress 🚀, we appreciate any suggestions and contributions ❤️.

How to contribute?

If you have any suggestions or find any missed papers, feel free to reach out or submit a pull request:

  1. Use the following markdown format.
*Author 1, Author 2, and Author 3.* **Paper Title.**  <ins>Conference/Journal/Preprint</ins> Year. [[pdf](link)]; [[other resources](link)].

  1. If one preprint paper has multiple versions, please use the earliest submitted year.

  2. Display the papers in a year descending order (the latest, the first).

Citation

Is this repository helpful? 😊

Please consider citing our paper. 👇👇👇

@article{li2023label,
  title={Label-efficient learning in agriculture: A comprehensive review},
  author={Li, Jiajia and Chen, Dong and Qi, Xinda and Li, Zhaojian and Huang, Yanbo and Morris, Daniel and Tan, Xiaobo},
  journal={Computers and Electronics in Agriculture},
  volume={215},
  pages={108412},
  year={2023},
  publisher={Elsevier}
}

New papers

  • Najafian, Keyhan, Alireza Ghanbari, Mahdi Sabet Kish, Mark Eramian, Gholam Hassan Shirdel, Ian Stavness, Lingling Jin, and Farhad Maleki. "Semi-self-supervised learning for semantic segmentation in images with dense patterns." Plant Phenomics 5 (2023): 0025.

🔍 Table of Contents

1. 💁🏽‍♀️ Introduction

Why label-efficient learning instead of supervised learning?

  • 👉 Affordable. For supervised learning, each task usually requires extensive labeled examples 💰. While for instruction learning, each task may require only one instruction and just a few examples 🤩.
  • 👉 One model, all tasks. An ideal AI system should be able to quickly understand and handle various new tasks 💫.
  • 👉 A promising research direction. Traditional supervised learning uses labeled instances to represent the task semantics, i.e., training models by observing numerous examples to recover the original task meaning. Therefore, why not directly use the task instruction, which has already occupied the essential task semantics?

2. 🎓 Surveys and Tutorials

The following topics are included:

  1. Zhou, Zhi-Hua. "A brief introduction to weakly supervised learning." National science review 5.1 (2018): 44-53. [Google Scholar] [Paper]

  2. Kääriäinen, Matti. "Active learning in the non-realizable case." International Conference on Algorithmic Learning Theory. Springer, Berlin, Heidelberg, 2006. [Google Scholar] [Paper]

  3. Schmarje, Lars, et al. "A survey on semi-, self-and unsupervised learning for image classification." IEEE Access 9 (2021): 82146-82168. [Google Scholar] [Paper]

  4. Jing, Longlong, and Yingli Tian. "Self-supervised visual feature learning with deep neural networks: A survey." IEEE transactions on pattern analysis and machine intelligence 43.11 (2020): 4037-4058. [Google Scholar] [Paper]

  5. Yan, Jun, and Xiangfeng Wang. "Unsupervised and semi‐supervised learning: the next frontier in machine learning for plant systems biology." The Plant Journal (2022). [Google Scholar] [Paper]

  6. Van Engelen, Jesper E., and Holger H. Hoos. "A survey on semi-supervised learning." Machine Learning 109.2 (2020): 373-440. [Google Scholar] [Paper]

  7. Fatima, Tazeen, and Tariq Mahmood. "Semi-Supervised Learning in Smart Agriculture: A Systematic Literature Review." 2021 6th International Multi-Topic ICT Conference (IMTIC). IEEE, 2021. [Google Scholar] [Paper]

  8. Sohn, Kihyuk, et al. "A simple semi-supervised learning framework for object detection." arXiv preprint arXiv:2005.04757 (2020). [Google Scholar] [Paper]

  9. Shen, Wei, et al. "A Survey on Label-efficient Deep Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction." arXiv preprint arXiv:2207.01223 (2022). [Google Scholar] [Paper]

  10. Settles, Burr. "Active learning literature survey." University of Wisconsin-Madison Department of Computer Sciences (2009). [Google Scholar] [Paper]

  11. Ren, Pengzhen, et al. "A survey of deep active learning." ACM computing surveys (CSUR) 54.9 (2021): 1-40. [Google Scholar] [Paper]

  12. Aggarwal, Charu C., et al. "Active learning: A survey." Data classification. Chapman and Hall/CRC, 2014. 599-634. [Google Scholar] [Paper]

  13. Chong, Yanwen, et al. "Graph-based semi-supervised learning: A review." Neurocomputing 408 (2020): 216-230. [Google Scholar] [Paper]

  14. Shen, Wei, et al. "A survey on label-efficient deep image segmentation: Bridging the gap between weak supervision and dense prediction. " IEEE Transactions on Pattern Analysis and Machine Intelligence (2023). [Google Scholar] [Paper]

  15. Foulds, James, and Eibe Frank. "A review of multi-instance learning assumptions." The knowledge engineering review 25.1 (2010): 1-25. [Google Scholar] [Paper]

  16. Carbonneau, Marc-André, et al. "Multiple instance learning: A survey of problem characteristics and applications." Pattern Recognition 77 (2018): 329-353. [Google Scholar] [Paper]

  17. Chicco, Davide. "Siamese neural networks: An overview." Artificial neural networks (2021): 73-94. [Google Scholar] [Paper]

  18. Aljalbout, Elie, et al. "Clustering with deep learning: Taxonomy and new methods." arXiv preprint arXiv:1801.07648 (2018). [Google Scholar] [Paper]

3. 🗂️ Taxonomy

In our paper, we divide the textual instructions into three categories.

3.1 Weak Supervision

3.1.1 Active learning

  1. Scheffer, Tobias, Christian Decomain, and Stefan Wrobel. "Active hidden markov models for information extraction." Advances in Intelligent Data Analysis: 4th International Conference, IDA 2001 Cascais, Portugal, September 13–15, 2001 Proceedings 4. Springer Berlin Heidelberg, 2001. [Google Scholar] [Paper]

  2. Houlsby, Neil, et al. "Bayesian active learning for classification and preference learning." arXiv preprint arXiv:1112.5745 (2011). [Google Scholar] [Paper]

  3. Gal, Yarin, Riashat Islam, and Zoubin Ghahramani. "Deep bayesian active learning with image data." International conference on machine learning. PMLR, 2017. [Google Scholar] [Paper]

  4. Gal, Yarin, and Zoubin Ghahramani. "Bayesian convolutional neural networks with Bernoulli approximate variational inference." arXiv preprint arXiv:1506.02158 (2015). [Google Scholar] [Paper]

  5. Haussmann, Manuel, Fred A. Hamprecht, and Melih Kandemir. "Deep active learning with adaptive acquisition." arXiv preprint arXiv:1906.11471 (2019). [Google Scholar] [Paper] [Code]

  6. Geifman, Yonatan, and Ran El-Yaniv. "Deep active learning with a neural architecture search." Advances in Neural Information Processing Systems 32 (2019). [Google Scholar] [Paper]

3.1.2 Semi-supervised learning

  1. Lee, Dong-Hyun. "Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks." Workshop on challenges in representation learning, ICML. Vol. 3. No. 2. 2013. [Google Scholar] [Paper] [Code]

  2. Xie, Qizhe, et al. "Self-training with noisy student improves imagenet classification." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020. [Google Scholar] [Paper] [Code]

  3. Liu, Yen-Cheng, et al. "Unbiased teacher for semi-supervised object detection." arXiv preprint arXiv:2102.09480 (2021). [Google Scholar] [Paper] [Code]

  4. Tarvainen, Antti, and Harri Valpola. "Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results." Advances in neural information processing systems 30 (2017). [Google Scholar] [Paper] [Code]

  5. Zhou, Zhi-Hua. "When semi-supervised learning meets ensemble learning." Frontiers of Electrical and Electronic Engineering in China 6 (2011): 6-16. [Google Scholar] [Paper]

  6. Zhou, Zhi-Hua. "Ensemble methods: foundations and algorithms." CRC press, 2012. [Google Scholar] [Paper]

  7. Bennett, Kristin, and Ayhan Demiriz. "Semi-supervised support vector machines." Advances in Neural Information processing systems 11 (1998). [Google Scholar] [Paper]

  8. Ben-David, Shai, et al. "Learning low density separators." Artificial Intelligence and Statistics. PMLR, 2009. [Google Scholar] [Paper] [Code]

  9. Li, Yu-Feng, et al. "Convex and scalable weakly labeled SVMs." Journal of Machine Learning Research 14.7 (2013). [Google Scholar] [Paper]

  10. Chapelle, Olivier, Vikas Sindhwani, and Sathiya S. Keerthi. "Optimization techniques for semi-supervised support vector machines." Journal of Machine Learning Research 9.2 (2008). [Google Scholar] [Paper]

  11. Liu, Wei, Jun Wang, and Shih-Fu Chang. "Robust and scalable graph-based semisupervised learning." Proceedings of the IEEE 100.9 (2012): 2624-2638. [Google Scholar] [Paper]

  12. Liu, Xiao, et al. "Random forest construction with robust semisupervised node splitting." IEEE Transactions on Image Processing 24.1 (2014): 471-483. [Google Scholar] [Paper]

  13. Fu, Yinkai, et al. "Semi-supervised segmentation of multi-scale soil pores based on a novel receptive field structure." Computers and Electronics in Agriculture 212 (2023): 108071. [Google Scholar] [Paper]

3.1.3 Weakly supervised learning

  1. Andrews, Stuart, Ioannis Tsochantaridis, and Thomas Hofmann. "Support vector machines for multiple-instance learning." Advances in neural information processing systems 15 (2002). [Google Scholar] [Paper]

  2. Wu, Jiajun, et al. "Deep multiple instance learning for image classification and auto-annotation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. [Google Scholar] [Paper]

  3. Ilse, Maximilian, Jakub Tomczak, and Max Welling. "Attention-based deep multiple instance learning." International conference on machine learning. PMLR, 2018. [Google Scholar] [Paper]

  4. Zhou, Bolei, et al. "Learning deep features for discriminative localization." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. [Google Scholar] [Paper] [Code]

  5. Selvaraju, Ramprasaath R., et al. "Grad-cam: Visual explanations from deep networks via gradient-based localization." Proceedings of the IEEE international conference on computer vision. 2017. [Google Scholar] [Paper] [Code]

  6. Chattopadhay, Aditya, et al. "Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks." 2018 IEEE winter conference on applications of computer vision (WACV). IEEE, 2018. [Google Scholar] [Paper]

  7. Wang, Haofan, et al. "Score-CAM: Score-weighted visual explanations for convolutional neural networks." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. 2020. [Google Scholar] [Paper] [Code]

3.2 No supervision

3.2.1 Self-supervised learning

  1. Caron, Mathilde, et al. "Deep clustering for unsupervised learning of visual features." Proceedings of the European conference on computer vision (ECCV). 2018. [Google Scholar] [Paper]

  2. Yang, Jianwei, Devi Parikh, and Dhruv Batra. "Joint unsupervised learning of deep representations and image clusters." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. [Google Scholar] [Paper] [Code]

  3. Xie, Junyuan, Ross Girshick, and Ali Farhadi. "Unsupervised deep embedding for clustering analysis." International conference on machine learning. PMLR, 2016. [Google Scholar] [Paper]

  4. Noroozi, Mehdi, et al. "Boosting self-supervised learning via knowledge transfer." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. [Google Scholar] [Paper]

  5. Tian, Yonglong, Dilip Krishnan, and Phillip Isola. "Contrastive multiview coding." Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XI 16. Springer International Publishing, 2020. [Google Scholar] [Paper] [Code]

  6. Caron, Mathilde, et al. "Unsupervised learning of visual features by contrasting cluster assignments." Advances in neural information processing systems 33 (2020): 9912-9924. [Google Scholar] [Paper] [Code]

  7. Chen, Ting, et al. "A simple framework for contrastive learning of visual representations." International conference on machine learning. PMLR, 2020. [Google Scholar] [Paper] [Code]

  8. Grill, Jean-Bastien, et al. "Bootstrap your own latent-a new approach to self-supervised learning." Advances in neural information processing systems 33 (2020): 21271-21284. [Google Scholar] [Paper] [Code]

  9. Chen, Xinlei, and Kaiming He. "Exploring simple siamese representation learning." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021. [Google Scholar] [Paper] [Code]

  10. Zhuang, Chengxu, Alex Lin Zhai, and Daniel Yamins. "Local aggregation for unsupervised learning of visual embeddings." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019. [Google Scholar] [Paper]

  11. Gao, Yangmingrui, Yinglun Li, Ruibo Jiang, Xiaohai Zhan, Hao Lu, Wei Guo, Wanneng Yang, Yanfeng Ding, and Shouyang Liu. "Enhancing green fraction estimation in rice and wheat crops: a self-supervised deep learning semantic segmentation approach." Plant Phenomics 5 (2023): 0064.

3.2.2 Unsupervised representation learning

  1. Arthur, David, and Sergei Vassilvitskii. "K-means++ the advantages of careful seeding." Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms. 2007. [Google Scholar] [Paper]

  2. Yang, Jianwei, Devi Parikh, and Dhruv Batra. "Joint unsupervised learning of deep representations and image clusters." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. [Google Scholar] [Paper] [Code]

  3. Ji, Zeguang, et al. "SEDLNet: An unsupervised precise lightweight extraction method for farmland areas." Computers and Electronics in Agriculture 210 (2023): 107886. [Google Scholar] [Paper]

4. 🤖 Applications

4.1 Plant health

Instructions are used in various human-computer interaction (HCI) tasks, such as virtual assistants, chatbots, etc.

  1. Coletta, Luiz FS, et al. "Novelty detection in UAV images to identify emerging threats in eucalyptus crops." Computers and Electronics in Agriculture 196 (2022): 106901. [Google Scholar] [Paper]

  2. Bollis, Edson, et al. "Weakly supervised attention-based models using activation maps for citrus mite and insect pest classification." Computers and Electronics in Agriculture 195 (2022): 106839. [Google Scholar] [Paper]

  3. Monowar, Muhammad Mostafa, et al. "Self-Supervised Clustering for Leaf Disease Identification." Agriculture 12.6 (2022): 814. [Google Scholar] [Paper]

  4. Kim, Taejoo, et al. "Instance-Aware Plant Disease Detection by Utilizing Saliency Map and Self-Supervised Pre-Training." Agriculture 12.8 (2022): 1084. [Google Scholar] [Paper]

  5. Li, Yang, and Xuewei Chao. "Semi-supervised few-shot learning approach for plant diseases recognition." Plant Methods 17 (2021): 1-10. [Google Scholar] [Paper]

  6. Fang, Uno, et al. "Self-supervised cross-iterative clustering for unlabeled plant disease images." Neurocomputing 456 (2021): 36-48. [Google Scholar] [Paper]

  7. Bollis, Edson, Helio Pedrini, and Sandra Avila. "Weakly supervised learning guided by activation mapping applied to a novel citrus pest benchmark." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 2020. [Google Scholar] [Paper]

  8. Kim, Wan-Soo, Dae-Hyun Lee, and Yong-Joo Kim. "Machine vision-based automatic disease symptom detection of onion downy mildew." Computers and Electronics in Agriculture 168 (2020): 105099. [Google Scholar] [Paper]

  9. Amorim, Willian Paraguassu, et al. "Semi-supervised learning with convolutional neural networks for UAV images automatic recognition." Computers and Electronics in Agriculture 164 (2019): 104932. [Google Scholar] [Paper]

  10. Lu, Jiang, et al. "An in-field automatic wheat disease diagnosis system." Computers and electronics in agriculture 142 (2017): 369-379. [Google Scholar] [Paper]

  11. Wu, Yang, and Lihong Xu. "Crop organ segmentation and disease identification based on weakly supervised deep neural network." Agronomy 9.11 (2019): 737. [Google Scholar] [Paper]

  12. Wang, Yinkai, et al. "Integrated Learning-Based Pest and Disease Detection Method for Tea Leaves." Forests 14.5 (2023): 1012. [Google Scholar] [Paper]

  13. Benfenati, Alessandro, et al. "Unsupervised deep learning techniques for automatic detection of plant diseases: reducing the need of manual labelling of plant images." Journal of Mathematics in Industry 13.1 (2023): 1-16. [Google Scholar] [Paper]

4.2 Weed and crop management

  1. Marszalek, Michael L., et al. "Self-supervised learning--A way to minimize time and effort for precision agriculture?." arXiv preprint arXiv:2204.02100 (2022). [Google Scholar] [Paper]

  2. Yang, Yana, et al. "Dissimilarity-based active learning for embedded weed identification." Turkish Journal of Agriculture and Forestry 46.3 (2022): 390-401. [Google Scholar] [Paper]

  3. Nong, Chunshi, Xijian Fan, and Junling Wang. "Semi-supervised Learning for Weed and Crop Segmentation Using UAV Imagery." Frontiers in Plant Science 13 (2022). [Google Scholar] [Paper]

  4. Güldenring, Ronja, and Lazaros Nalpantidis. "Self-supervised contrastive learning on agricultural images." Computers and Electronics in Agriculture 191 (2021): 106510. [Google Scholar] [Paper]

  5. Shorewala, Shantam, et al. "Weed density and distribution estimation for precision agriculture using semi-supervised learning." IEEE access 9 (2021): 27971-27986. [Google Scholar] [Paper]

  6. Hu, Chengsong, J. Alex Thomasson, and Muthukumar V. Bagavathiannan. "A powerful image synthesis and semi-supervised learning pipeline for site-specific weed detection." Computers and Electronics in Agriculture 190 (2021): 106423. [Google Scholar] [Paper]

  7. dos Santos Ferreira, Alessandro, et al. "Unsupervised deep learning and semi-automatic data labeling in weed discrimination." Computers and Electronics in Agriculture 165 (2019): 104963. [Google Scholar] [Paper]

  8. Bah, M. Dian, Adel Hafiane, and Raphael Canals. "Deep learning with unsupervised data labeling for weed detection in line crops in UAV images." Remote sensing 10.11 (2018): 1690. [Google Scholar] [Paper]

  9. Pérez-Ortiz, Maria, et al. "A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method." Applied Soft Computing 37 (2015): 533-544. [Google Scholar] [Paper]

4.3 Fruit detection

  1. Ciarfuglia, Thomas A., et al. "Weakly and semi-supervised detection, segmentation and tracking of table grapes with limited and noisy data." Computers and Electronics in Agriculture 205 (2023): 107624. [Google Scholar] [Paper]

  2. Bhattarai, Uddhav, and Manoj Karkee. "A weakly-supervised approach for flower/fruit counting in apple orchards." Computers in Industry 138 (2022): 103635. [Google Scholar] [Paper]

  3. Bellocchio, Enrico, et al. "A novel vision-based weakly supervised framework for autonomous yield estimation in agricultural applications." Engineering Applications of Artificial Intelligence 109 (2022): 104615. [Google Scholar] [Paper]

  4. Casado-García, A., et al. "Semi-supervised deep learning and low-cost cameras for the semantic segmentation of natural images in viticulture." Precision Agriculture (2022): 1-26. [Google Scholar] [Paper]

  5. Khaki, Saeed, et al. "Deepcorn: A semi-supervised deep learning method for high-throughput image-based corn kernel counting and yield estimation." Knowledge-Based Systems 218 (2021): 106874. [Google Scholar] [Paper]

  6. Bellocchio, Enrico, et al. "Combining domain adaptation and spatial consistency for unseen fruits counting: a quasi-unsupervised approach." IEEE Robotics and Automation Letters 5.2 (2020): 1079-1086. [Google Scholar] [Paper]

  7. Bellocchio, Enrico, et al. "Weakly supervised fruit counting for yield estimation using spatial consistency." IEEE Robotics and Automation Letters 4.3 (2019): 2348-2355. [Google Scholar] [Paper]

  8. Roy, Pravakar, et al. "Vision-based preharvest yield mapping for apple orchards." Computers and Electronics in Agriculture 164 (2019): 104897. [Google Scholar] [Paper]

4.4 Aquaculture

  1. Kong, Qingchen, et al. "A recurrent network based on active learning for the assessment of fish feeding status." Computers and Electronics in Agriculture 198 (2022): 106979. [Google Scholar] [Paper]

4.5 Plant Phenotyping

  1. Lin, Xufeng, et al. "Self-Supervised Leaf Segmentation under Complex Lighting Conditions." Pattern Recognition 135 (2023): 109021. [Google Scholar] [Paper] [Code]

  2. Tschand, Arya. "Semi-supervised machine learning analysis of crop color for autonomous irrigation." Smart Agricultural Technology 3 (2023): 100116. [Google Scholar] [Paper]

  3. Blok, Pieter M., et al. "Active learning with MaskAL reduces annotation effort for training Mask R-CNN on a broccoli dataset with visually similar classes." Computers and Electronics in Agriculture 197 (2022): 106917. [Google Scholar] [Paper] [Code]

  4. Rawat, Shivangana, et al. "How Useful Is Image-Based Active Learning for Plant Organ Segmentation?." Plant Phenomics 2022 (2022). [Google Scholar] [Paper]

  5. Li, Lei, et al. "Leaf vein segmentation with self-supervision." Computers and Electronics in Agriculture 203 (2022): 107352. [Google Scholar] [Paper]

  6. Zhang, Xin, et al. "The Self-Supervised Spectral–Spatial Vision Transformer Network for Accurate Prediction of Wheat Nitrogen Status from UAV Imagery." Remote Sensing 14.6 (2022): 1400. [Google Scholar] [Paper]

  7. Qiang, Zhuang, Jingmin Shi, and Fanhuai Shi. "Phenotype Tracking of Leafy Greens Based on Weakly Supervised Instance Segmentation and Data Association." Agronomy 12.7 (2022): 1567. [Google Scholar] [Paper]

  8. Adke, Shrinidhi, et al. "Supervised and weakly supervised deep learning for segmentation and counting of cotton bolls using proximal imagery." Sensors 22.10 (2022): 3688. [Google Scholar] [Paper]

  9. Dandrifosse, Sébastien, et al. "Deep learning for wheat ear segmentation and ear density measurement: From heading to maturity." Computers and Electronics in Agriculture 199 (2022): 107161. [Google Scholar] [Paper]

  10. Petti, Daniel, and Changying Li. "Weakly-supervised learning to automatically count cotton flowers from aerial imagery." Computers and Electronics in Agriculture 194 (2022): 106734. [Google Scholar] [Paper]

  11. Kim, Wan-Soo, et al. "Weakly supervised crop area segmentation for an autonomous combine harvester." Sensors 21.14 (2021): 4801. [Google Scholar] [Paper]

  12. Siddique, Abubakar, Amy Tabb, and Henry Medeiros. "Self-supervised Learning for Panoptic Segmentation of Multiple Fruit Flower Species." IEEE Robotics and Automation Letters 7.4 (2022): 12387-12394. [Google Scholar] [Paper]

  13. Najafian, Keyhan, et al. "A semi-self-supervised learning approach for wheat head detection using extremely small number of labeled samples." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021. [Google Scholar] [Paper]

  14. Fourati, Fares, Wided Souidene Mseddi, and Rabah Attia. "Wheat head detection using deep, semi-supervised and ensemble learning." Canadian Journal of Remote Sensing 47.2 (2021): 198-208. [Google Scholar] [Paper]

  15. Ghosal, Sambuddha, et al. "A weakly supervised deep learning framework for sorghum head detection and counting." Plant Phenomics (2019). [Google Scholar] [Paper]

  16. Chandra, Akshay L., et al. "Active learning with point supervision for cost-effective panicle detection in cereal crops." Plant Methods 16 (2020): 1-16. [Google Scholar] [Paper]

  17. Ayalew, Tewodros W., Jordan R. Ubbens, and Ian Stavness. "Unsupervised domain adaptation for plant organ counting." Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020, Proceedings, Part VI 16. Springer International Publishing, 2020. [Google Scholar] [Paper]

  18. Valerio Giuffrida, Mario, et al. "Leaf counting without annotations using adversarial unsupervised domain adaptation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 2019. [Google Scholar] [Paper]

  19. Wang, Yi, and Lihong Xu. "Unsupervised segmentation of greenhouse plant images based on modified Latent Dirichlet Allocation." PeerJ 6 (2018): e5036. [Google Scholar] [Paper]

  20. Zhang, Ping, and Lihong Xu. "Unsupervised segmentation of greenhouse plant images based on statistical method." Scientific reports 8.1 (2018): 1-13. [Google Scholar] [Paper]

4.6 Postharvest quality assessment

  1. Liu, Yisen, et al. "Joint optimization of autoencoder and self-supervised classifier: anomaly detection of strawberries using hyperspectral imaging." Computers and Electronics in Agriculture 198 (2022): 107007. [Google Scholar] [Paper]

  2. Li, Weitao, et al. "Greengage grading using stochastic configuration networks and a semi-supervised feedback mechanism." Information Sciences 488 (2019): 1-12. [Google Scholar] [Paper]

  3. Marino, Sofia, Pierre Beauseroy, and André Smolarz. "Weakly-supervised learning approach for potato defects segmentation." Engineering Applications of Artificial Intelligence 85 (2019): 337-346. [Google Scholar] [Paper]

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