mlsanityData to 7 Oct 2026

Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review

arXiv 2204.04707 ↗First posted 10 Apr 2022cs.CV eess.IVcited by 3

Not on the map. Why?

Abstract (arXiv)

In agricultural image analysis, optimal model performance is keenly pursued for better fulfilling visual recognition tasks (e.g., image classification, segmentation, object detection and localization), in the presence of challenges with biological variability and unstructured environments. Large-scale, balanced and ground-truthed image datasets, however, are often difficult to obtain to fuel the development of advanced, high-performance models. As artificial intelligence through deep learning is impacting analysis and modeling of agricultural images, data augmentation plays a crucial role in boosting model performance while reducing manual efforts for data preparation, by algorithmically expanding training datasets. Beyond traditional data augmentation techniques, generative adversarial network (GAN) invented in 2014 in the computer vision community, provides a suite of novel approaches that can learn good data representations and generate highly realistic samples. Since 2017, there has been a growth of research into GANs for image augmentation or synthesis in agriculture for improved model performance. This paper presents an overview of the evolution of GAN architectures followed by a systematic review of their application to agriculture (https://github.com/Derekabc/GANs-Agriculture), involving various vision tasks for plant health, weeds, fruits, aquaculture, animal farming, plant phenotyping as well as postharvest detection of fruit defects. Challenges and opportunities of GANs are discussed for future research.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 116 of its 264 references that are in the map. First posted on arXiv 10 Apr 2022.

Builds on

116 of 264 references.

References · 116

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  5. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  6. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  7. 2020End-to-End Object Detection with Transformerscited by 7,384
  8. 2017Mask R-CNNcited by 7,259
  9. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  10. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  11. 2022Generative Adversarial Networkscited by 5,603
  12. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
38 more
  1. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  2. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
  3. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  4. 2016Improved Techniques for Training GANscited by 2,938
  5. 2018YOLOv3: An Incremental Improvementcited by 2,652
  6. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
  7. 2017Non-local Neural Networkscited by 2,290
  8. 2017Improved Training of Wasserstein GANscited by 2,223
  9. 2014Conditional Generative Adversarial Netscited by 2,152
  10. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  11. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 1,998
  12. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  13. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,618
  14. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
  15. 2020WILDS: A Benchmark of in-the-Wild Distribution Shiftscited by 1,366
  16. 2016Progressive Neural Networkscited by 1,332
  17. 2017Adversarial Discriminative Domain Adaptationcited by 1,309
  18. 2019Semantic Image Synthesis with Spatially-Adaptive Normalizationcited by 1,295
  19. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,250
  20. 2019LVIS: A Dataset for Large Vocabulary Instance Segmentationcited by 1,215
  21. 2020Training Generative Adversarial Networks with Limited Datacited by 1,184
  22. 2021Alias-Free Generative Adversarial Networkscited by 1,181
  23. 2021Scaling Vision Transformerscited by 993
  24. 2021Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Datacited by 961
  25. 2019Improved Precision and Recall Metric for Assessing Generative Modelscited by 925
  26. 2017Revisiting Unreasonable Effectiveness of Data in Deep Learning Eracited by 916
  27. 2016InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Netscited by 858
  28. 2017A Survey on Multi-Task Learningcited by 838
  29. 2018Self-Attention Generative Adversarial Networkscited by 809
  30. 2016Generative Adversarial Text to Image Synthesiscited by 735
  31. 2017Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkscited by 707
  32. 2019Modeling Tabular data using Conditional GANcited by 695
  33. 2017Generative Adversarial Networks: An Overviewcited by 588
  34. 2016StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 575
  35. 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 562
  36. 2018Albumentations: fast and flexible image augmentationscited by 558
  37. 2016f-GAN: Training Generative Neural Samplers using Variational Divergence Minimizationcited by 504
  38. 2017Progressive Neural Architecture Searchcited by 483

50 most cited of 116 All 116 (JSON)

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 2.

3 papers.

  1. 2023A Unified Framework for Generative Data Augmentation: A Comprehensive Surveycited by 4
  2. 2022Inside Out: Transforming Images of Lab-Grown Plants for Machine Learning Applications in Agriculturecited by 1
  3. 2023Synthetically Generating Human-like Data for Sequential Decision Making Tasks via Reward-Shaped Imitation Learningcited by 0