Awesome GANs in Agriculture

In agricultural image analysis, optimal model performance is required for better fulfilling visual recognition tasks (e.g., classification, detection, and segmentation). Sufficient ground-truth datasets, however, are often difficult to obtain, posing a bottleneck to the development of advanced high-performance models. As artificial intelligence through deep learning is impacting analysis and modeling of agriculture images, data augmentation plays a vital role in boosting model performance by artificially generating new images to expand training datasets. Beyond traditional data augmentation techniques that rely on geometric and photometric transformations, generative adversarial network (GAN) invented in 2014 in the computer vision community, provides a suite of novel approaches that can learn good data representation and generate synthetic samples with the appearance of real images. Since 2018, there has been a growth of research into GANs for data augmentation or image synthesis for various agricultural applications. This repo contains GANs for augmenting agricultural images for improved model performance.

Citation

Please consider cite our paper if you find this repo is helpful.

@article{lu2022generative,
  title={Generative adversarial networks (GANs) for image augmentation in agriculture: A systematic review},
  author={Lu, Yuzhen and Chen, Dong and Olaniyi, Ebenezer and Huang, Yanbo},
  journal={Computers and Electronics in Agriculture},
  volume={200},
  pages={107208},
  year={2022},
  publisher={Elsevier}
}

Fresh Papers

• Modak, Sourav, and Anthony Stein. "Generative AI-based Pipeline Architecture for Increasing Training Efficiency in Intelligent Weed Control Systems." arXiv preprint arXiv:2411.00548 (2024).
• Sapkota, Ranjan, Dawood Ahmed, and Manoj Karkee. "Creating Image Datasets in Agricultural Environments using DALL. E: Generative AI-Powered Large Language Model.", 2024
• Sapkota, Ranjan, Dawood Ahmed, and Manoj Karkee. "Synthetic Meets Authentic: Leveraging Text-to-Image Generated Datasets for Apple Detection in Orchard Environments.", 2024
• Li, Tang, Motoaki Asai, Yoichiro Kato, Yuya Fukano, and Wei Guo. "Channel Attention GAN-based Synthetic Weed Generation for Precise Weed Identification." Plant Phenomics (2024).

Contents

• Applications of GANs in Agriculture
  • Precision Agriculture
    • Plant Health
    • Weeds
    • Fruit Detection
    • Aquaculture
    • Animal Farming
  • Plant Phenotyping
  • Postharvest Quality Assessment
• GAN Achitectures
• GAN Review Papers
• GAN Implementations
• Applications of Diffusion Models in Agriculture

Applications of GANs in Agriculture

Precision Agriculture

Plant Health

2023

• Guerrero-Ibañez, Antonio, and Angelica Reyes-Muñoz. "Monitoring Tomato Leaf Disease through Convolutional Neural Networks." Electronics 12.1 (2023): 229. [scholar] [paper]

2022

• Li, Mingxuan, et al. "FWDGAN-based data augmentation for tomato leaf disease identification." Computers and Electronics in Agriculture 194 (2022): 106779. [scholar] [paper]
• Wang, Fengyi, et al. "Practical cucumber leaf disease recognition using improved Swin Transformer and small sample size." Computers and Electronics in Agriculture 199 (2022): 107163. [scholar] [paper]
• Jin, Haibin, et al. "GrapeGAN: Unsupervised image enhancement for improved grape leaf disease recognition." Computers and Electronics in Agriculture 198 (2022): 107055. [scholar] [paper]
• Chen, Weirong, et al. "MS-DNet: A mobile neural network for plant disease identification." Computers and Electronics in Agriculture 199 (2022): 107175. [scholar] [paper]
• Xu, M., Yoon, S., Fuentes, A., Yang, J., & Park, D. S. "Style-Consistent Image Translation: A Novel Data Augmentation Paradigm to Improve Plant Disease Recognition." Frontiers in Plant Science, 12(February), 1–16. [scholar] [paper]

2021

• Abbas, Amreen, et al. "Tomato plant disease detection using transfer learning with C-GAN synthetic images." Computers and Electronics in Agriculture 187 (2021): 106279. [scholar] [paper]
• Hu, W. J., Xie, T. Y., Li, B. S., Du, Y. X., & Xiong, N. N. "An edge intelligence-based generative data augmentation system for IoT image recognition tasks." Journal of Internet Technology, 22(4), 765–777. [scholar] [paper]
• Kim, C., Lee, H., & Jung, H., "Fruit tree disease classification system using generative adversarial networks." International Journal of Electrical and Computer Engineering, 11(3), 2508–2515. [scholar] [paper]
• Zeng, M., Gao, H., & Wan, L. "Few-Shot Grape Leaf Diseases Classification Based on Generative Adversarial Network." Journal of Physics: Conference Series, 1883(1). [scholar] [paper]
• Gomaa, A. A., & El-Latif, Y. M. A. "Early Prediction of Plant Diseases using CNN and GANs." International Journal of Advanced Computer Science and Applications, 12(5), 514–519. [scholar] [paper]
• Maqsood, M. H., Mumtaz, R., Haq, I. U., Shafi, U., Zaidi, S. M. H., & Hafeez, M. "Super resolution generative adversarial network (SRGANs) for wheat stripe rust classification." Sensors, 21(23), 1–12. [scholar] [paper]
• Zhang, Jingyao, Rao, Y., Man, C., Jiang, Z., & Li, S. "Identification of cucumber leaf diseases using deep learning and small sample size for agricultural Internet of Things." International Journal of Distributed Sensor Networks, 17(4). [scholar] [paper]
• Zhao, Y., Chen, Z., Gao, X., Song, W., Xiong, Q., Hu, J., & Zhang, Z. "Plant Disease Detection using Generated Leaves Based on DoubleGAN." IEEE/ACM Transactions on Computational Biology and Bioinformatics, 5963(c), 1–10. [scholar] [paper]
• Deng, H., Luo, D., Chang, Z., Li, H., & Yang, X. "Rahc_gan: A data augmentation method for tomato leaf disease recognition." Symmetry, 13(9). [scholar] [paper]
• Nerkar, B., & Talbar, S. "Cross-dataset learning for performance improvement of leaf disease detection using reinforced generative adversarial networks." International Journal of Information Technology (Singapore), 13(6), 2305–2312. [scholar] [paper]

2020

• Wu, Q., Chen, Y., & Meng, J. "DCGAN-based data augmentation for tomato leaf disease identification." IEEE Access, 8, 98716–98728.[scholar] [paper]
• Sun, R., Zhang, M., Yang, K., & Liu, J., "Data enhancement for plant disease classification using generated lesions." Applied Sciences (Switzerland), 10(2).[scholar] [paper]
• Zeng, Q., Ma, X., Cheng, B., Zhou, E., & Pang, W. "GANS-based data augmentation for citrus disease severity detection using deep learning." IEEE Access, 8, 172882–172891. [scholar] [paper]
• Yuwana, R. S., Fauziah, F., Heryana, A., Krisnandi, D., Kusumo, R. B. S., & Pardede, H. F., "Data Augmentation using Adversarial Networks for Tea Diseases Detection." Jurnal Elektronika Dan Telekomunikasi, 20(1), 29. [scholar] [paper]
• Cap, Q. H., Uga, H., Kagiwada, S., & Iyatomi, H."LeafGAN: An Effective Data Augmentation Method for Practical Plant Disease Diagnosis." IEEE Transactions on Automation Science and Engineering, 1–10. [scholar] [paper]
• Wen, J., Shi, Y., Zhou, X., & Xue, Y. "Crop disease classification on inadequate low-resolution target images." Sensors (Switzerland), 20(16), 1–17. [scholar] [paper]
• Nazki, H., Yoon, S., Fuentes, A., & Park, D. S. "Unsupervised image translation using adversarial networks for improved plant disease recognition." Computers and Electronics in Agriculture, 168(August 2019), 105117. [scholar] [paper]
• Liu, B., Tan, C., Li, S., He, J., & Wang, H. "A Data Augmentation Method Based on Generative Adversarial Networks for Grape Leaf Disease Identification." IEEE Access, 8, 102188–102198. [scholar] [paper]
• Dai, Q., Cheng, X., Qiao, Y., & Zhang, Y. "Agricultural pest super-resolution and identification with attention enhanced residual and dense fusion generative and adversarial network." IEEE Access, 8, 81943–81959. [scholar] [paper]
• Bi, L., & Hu, G. "Improving Image-Based Plant Disease Classification With Generative Adversarial Network Under Limited Training Set." Frontiers in Plant Science, 11(December), 1–12. [scholar] [paper]

2019

• Hu, G., Wu, H., Zhang, Y., & Wan, M., 2019. A low shot learning method for tea leaf’s disease identification. Computers and Electronics in Agriculture, 163(June). [scholar]
• Douarre, C., Crispim-Junior, C. F., Gelibert, A., Tougne, L., & Rousseau, D., 2019. Novel data augmentation strategies to boost supervised segmentation of plant disease. Computers and Electronics in Agriculture, 165(August), 104967.[scholar] [paper]
• Zhang, M., Liu, S., Yang, F., & Liu, J. "Classification of Canker on Small Datasets Using Improved Deep Convolutional Generative Adversarial Networks." IEEE Access, 7, 49680–49690. [scholar] [paper]
• Nazki, H., Lee, J., Yoon, S., & Park, D. S. "Image-to-Image Translation with GAN for Synthetic Data Augmentation in Plant Disease Datasets." Korean Institute of Smart Media, 8(2), 46–57. [scholar] [paper]
• Tian, Y., Yang, G., Wang, Z., Li, E., & Liang, Z. "Detection of apple lesions in orchards based on deep learning methods of cyclegan and YoloV3-dense." Journal of Sensors, 2019. [scholar] [paper]
• Arsenovic, M., Karanovic, M., Sladojevic, S., Anderla, A., & Stefanovic, D. "Solving current limitations of deep learning based approaches for plant disease detection." Symmetry, 11(7). [scholar] [paper]
• Lu, C. Y., Arcega Rustia, D. J., & Lin, T. Te. "Generative Adversarial Network Based Image Augmentation for Insect Pest Classification Enhancement." IFAC-PapersOnLine, 52(30), 1–5. [scholar] [paper]

Weeds

2022

• Divyanth, L. G., et al. "Image-to-Image Translation-Based Data Augmentation for Improving Crop/Weed Classification Models for Precision Agriculture Applications." Algorithms 15.11 (2022): 401. [scholar] [paper]

2021

• Fawakherji, M., Potena, C., Pretto, A., Bloisi, D. D., & Nardi, D. "Multi-Spectral Image Synthesis for Crop/Weed Segmentation in Precision Farming." Robotics and Autonomous Systems, 146, 103861. [scholar] [paper]
• Espejo-Garcia, B., Mylonas, N., Athanasakos, L., Vali, E., & Fountas, S. "Combining generative adversarial networks and agricultural transfer learning for weeds identification." Biosystems Engineering, 204, 79–89. scholar [paper]
• Khan, S, Tufail, M, Khan, M. T., Khan, Z. A., Iqbal, J., and Alam, M. “A novel semi-supervised framework for UAV based crop/weed classification.” PLoS One, vol. 16, no. 5 [scholar] [paper]

2019

• Kerdegari,H., Razaak M., Argyriou V., Remagnino P. "Semi-supervised GAN for Classification of Multispectral Imagery Acquired by UAVs." [scholar] [paper]

Fruit Detection

2023

• Zhang, Tingting, et al. "Large-scale apple orchard mapping from multi-source data using the semantic segmentation model with image-to-image translation and transfer learning." Computers and Electronics in Agriculture 213 (2023): 108204.

2022

• Ufuah, Donald, et al. "A Data Augmentation Approach Based on Generative Adversarial Networks for Date Fruit Classification." Applied Engineering in Agriculture (2022): 0.

2021

• Fei, Z., Olenskyj, A., Bailey, B. N., & Earles, M. Enlisting 3D Crop Models and GANs for More Data Efficient and Generalizable Fruit Detection. 1269–1277. [scholar] [paper]
• Kierdorf, J., Weber, I., Kicherer, A., Zabawa, L., Drees, L., & Roscher, R. "Behind the leaves -- Estimation of occluded grapevine berries with conditional generative adversarial networks." [scholar] [paper]

2020

• Barth, R., Hemming, J., & Van Henten, E. J. "Optimising realism of synthetic images using cycle generative adversarial networks for improved part segmentation." Computers and Electronics in Agriculture, 173(October 2019), 105378. [scholar] [paper]
• Luo Z., Huiling Y., Zhang Y. "Pine Cone Detection Using Boundary Equilibrium Generative Adversarial Networks and Improved YOLOv3 Model." Sensors 2020, 20(16), 4430. [scholar] [paper]
• Bellocchio, E.,Costante G., Cascianelli, S., Fravolini, M. L., Valigi P."Combining Domain Adaptation and Spatial Consistency for Unseen Fruits Counting: A Quasi-Unsupervised Approach." IEEE Robotics and Automation Letters,Volume: 5, Issue: 2, pp. 1079 - 1086. [scholar] [paper]
• Olatunji, J. R., Redding, G. P., Rowe, C. L., & East, A. R. "Reconstruction of kiwifruit fruit geometry using a CGAN trained on a synthetic dataset." Computers and Electronics in Agriculture, 177(August), 105699. [scholar] [paper]

2018

• Barth, R., Hemming, J., & Van Henten, E. J. "Improved Part Segmentation Performance by Optimising Realism of Synthetic Images using Cycle Generative Adversarial Networks." arXiv:1803.06301v1 [cs.CV] 16 Mar 2018. [scholar] [paper]

Aquaculture

2021

• Zhang, Junjie, Yang, G., Sun, L., Zhou, C., Zhou, X., Li, Q., Bi, M., & Guo, J., 2021. Shrimp egg counting with fully convolutional regression network and generative adversarial network. Aquacultural Engineering, 94(June), 102175. [scholar] [paper]

2018

• Zhao, Jian, Li, Y., Zhang, F., Zhu, S., Liu, Y., Lu, H., & Ye, Z., 2018. Semi-Supervised Learning-Based Live Fish Identification in Aquaculture Using Modified Deep Convolutional Generative Adversarial Networks. Transactions of the ASABE, 61(2), 699–710. [scholar] [paper]

Animal Farming

2021

• Ahmed, G., Malick, R. A. S., Akhunzada, A., Zahid, S., Sagri, M. R. and Gani, A. “An approach towards iot-based predictive service for early detection of diseases in poultry chickens,” Sustain., vol. 13, no. 23, pp. 1–16, 2021. [scholar] [paper]
• Singh P., Devi K. J., Varish N. "Muzzle Pattern Based Cattle Identification Using Generative Adversarial Networks." Advances in Intelligent Systems and Computing Book series, vol. 1392. [scholar] [paper]

2020

• Li, H., & Tang, J. "Dairy goat image generation based on improved-self-attention generative adversarial." IEEE Access (Volume: 8) pp. 62448 - 62457. [scholar] [paper]

Plant Phenotyping

2022

• • Krosney, A. E. and Sotoodeh, P. and Henry, C. J. and Beck, M. A. and Bidinosti, C. P. "Inside Out: Transforming Images of Lab-Grown Plants for Machine Learning Applications in Agriculture", (2022) [scholar]
• Qi, Chao, et al. "Tea Chrysanthemum Detection by Leveraging Generative Adversarial Networks and Edge Computing." Frontiers in plant science 13 (2022). [scholar] [paper]

2021

• Drees, L., Junker-Frohn, L. V., Kierdorf, J., & Roscher, R. "Temporal prediction and evaluation of Brassica growth in the field using conditional generative adversarial networks." Computers and Electronics in Agriculture, 190(August), 106415. [scholar] [paper]
• Zane K. J. H., Andrew P. F. "Domain Adaptation of Synthetic Images for Wheat Head Detection." Plants, 10(12), 2633. [scholar] [paper]

2020

• Zhu, F., He, M., & Zheng, Z., "Data augmentation using improved cDCGAN for plant vigor rating." Computers and Electronics in Agriculture, 175(51), 105603. [scholar] [paper]
• Shete, S., Srinivasan, S., & Gonsalves, T. A. "TasselGAN: An Application of the Generative Adversarial Model for Creating Field-Based Maize Tassel Data." Plant Phenomics, 2020, 8309605. [scholar] [paper]

2019

• Madsen, Simon L., Dyrmann, M., Jørgensen, R. N., & Karstoft, H. "Generating artificial images of plant seedlings using generative adversarial networks." Biosystems Engineering, 187, 147–159. [scholar] [paper]
• Madsen, Simon Leminen, Mortensen, A. K., Jørgensen, R. N., & Karstoft, H. "Disentangling information in artificial images of plant seedlings using semi-supervised GAN." Remote Sensing, 11(22), 1–16. [scholar] [paper]

2018

• Zhu, Y., Aoun, M., Krijn, M., & Vanschoren, J., 2018. Data Augmentation using Conditional Generative Adversarial Networks for Leaf Counting in Arabidopsis Plants. Bmvc, 121–125. [scholar] [paper]

2017

• Giuffrida, M. V., Scharr, H., & Tsaftaris, S. A., 2017. ARIGAN: Synthetic arabidopsis plants using generative adversarial network. Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017, 2018-Janua(i), 2064–2071.

Postharvest Quality Assessment

2022

• Bird, J. J., Barnes, C. M., Manso, L. J., Ekárt, A., & Faria, D. R. Fruit quality and defect image classification with conditional GAN data augmentation. Scientia Horticulturae, 293(November 2021). [scholar] [paper]

2021

• Wang, C., & Xiao, Z. "Lychee surface defect detection based on deep convolutional neural networks with GAN-based data augmentation." Agronomy, 11(8), 1–17. [scholar] [paper]
• Yang, X., Guo, M., Lyu, Q., & Ma, M., "Detection and classification of damaged wheat kernels based on progressive neural architecture search." Biosystems Engineering, 208, 176–185. [scholar] [paper]

2020

• Guo Z., Zheng H., Xu X., Ju,J., Zheng Z., , You C, Gu Yu "Quality grading of jujubes using composite convolutional neural networks in combination with RGB color space segmentation and deep convolutional generative adversarial networks." Journal of Food Process Engineering. [scholar] [paper]
• Marino, S., Beauseroy, P., & Smolarz, A. "Unsupervised adversarial deep domain adaptation method for potato defects classification." Computers and Electronics in Agriculture, 174. [scholar] [paper]

2019

• Chou et al. "Deep-Learning Based Defective Bean Inspection with GAN Structured Automated Labeled Data Augmentation in Coffee Industry." Appl. Sci. 2019, 9(19), 4166. [scholar] [paper]

GAN Achitectures

2022

• Xu, Mingle, et al. "A Comprehensive Survey of Image Augmentation Techniques for Deep Learning." arXiv preprint arXiv:2205.01491 (2022).

2021

• Zhang et al. "StyleSwin: Transformer-based GAN for High-resolution Image Generation." arXiv:2112.10762. [scholar] [paper]
• Jiang et al. "TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up." Advances in Neural Information Processing Systems 34 (NeurIPS 2021). [scholar] [paper]

2020

• Karras et al. "Analyzing and Improving the Image Quality of StyleGAN." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 8110-8119. [scholar] [paper]

2019

• Gong et al. "AutoGAN: Neural Architecture Search for Generative Adversarial Networks." Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 3224-3234. [scholar] [paper]
• Brock, A., Donahue, J., Simonyan K. "Large Scale GAN Training for High Fidelity Natural Image Synthesis" arXiv:1809.11096 [scholar] [paper] -Zhang et al. "Self-Attention Generative Adversarial Networks." Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7354-7363, 2019. [scholar] [paper] Kerras et al. "A Style-Based Generator Architecture for Generative Adversarial Networks." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 4401-4410. [scholar] [paper]

2018

• Wang et al. "ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks." Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018, pp. 0-0 [scholar] [paper]
• Karras et al. "Progressive Growing of GANs for Improved Quality, Stability, and Variation." arXiv:1710.10196. [scholar] [paper]

2017

• Zhu, J. Y., Park, T., Isola, P., & Efros, A. A., 2017. Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. Proceedings of the IEEE International Conference on Computer Vision, 2017-Octob, 2242–2251.
• Ishola et al. "Image-To-Image Translation With Conditional Adversarial Networks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1125-1134. [scholar] [paper]
• Ledig et al. "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 4681-4690. [scholar] [paper]
• Arjovsky et al. "Wasserstein Generative Adversarial Networks." Proceedings of the 34th International Conference on Machine Learning, PMLR 70:214-223. [scholar] [paper]
• Gulrajani et al. "Improved Training of Wasserstein GANs." Advances in Neural Information Processing Systems 30 (NIPS 2017). [scholar] [paper]

2016

• Chen et al. "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets." Advances in Neural Information Processing Systems 29 (NIPS 2016) [scholar] [paper]

• Liu, M. Y., & Tuzel, O., 2016. Coupled generative adversarial networks. Advances in Neural Information Processing Systems, Nips, 469–477. [scholar] [paper]

2014

• Mirza, M., & Osindero, S. "Conditional Generative Adversarial Nets." 1–7. [scholar] [paper]
• Goodfellow et al. "Generative Adversarial Nets." Advances in Neural Information Processing Systems 27 (NIPS 2014). [scholar] [paper]

2015 Radford, A., Metz, L., Chintala S. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv:1511.06434v2. pp. 1-16. [scholar] [paper]

GAN Review Papers

2022

• Cohen, Gilad, and Raja Giryes. "Generative Adversarial Networks." arXiv preprint arXiv:2203.00667 (2022). [arXiv]

2021

• Pavan Kumar, M. R., and Prabhu Jayagopal. "Generative adversarial networks: a survey on applications and challenges." International Journal of Multimedia Information Retrieval 10.1 (2021): 1-24. [scholar] [paper]
• Gui, Jie, et al. "A review on generative adversarial networks: Algorithms, theory, and applications." IEEE Transactions on Knowledge and Data Engineering (2021). [scholar] [paper]
• Wang, Zhengwei, Qi She, and Tomas E. Ward. "Generative adversarial networks in computer vision: A survey and taxonomy." ACM Computing Surveys (CSUR) 54.2 (2021): 1-38. [scholar] [arXiv]

2019

• Di Mattia, Federico, et al. "A survey on gans for anomaly detection." arXiv preprint arXiv:1906.11632 (2019). [scholar] [arXiv]
• Yi, Xin, Ekta Walia, and Paul Babyn. "Generative adversarial network in medical imaging: A review." Medical image analysis 58 (2019): 101552. [scholar] [arXiv]
• Zamorski, Maciej, et al. "Generative adversarial networks: recent developments." International Conference on Artificial Intelligence and Soft Computing. Springer, Cham, 2019. [scholar] [arXiv]
• Hong, Yongjun, et al. "How generative adversarial networks and their variants work: An overview." ACM Computing Surveys (CSUR) 52.1 (2019): 1-43. [scholar] [paper]

2018

• Creswell, Antonia, et al. "Generative adversarial networks: An overview." IEEE Signal Processing Magazine 35.1 (2018): 53-65. [scholar] [paper] [arXiv]
• Cao, Yang-Jie, et al. "Recent advances of generative adversarial networks in computer vision." IEEE Access 7 (2018): 14985-15006. [scholar] [paper]

GAN Implementations

• Pytorch StudioGAN: https://github.com/POSTECH-CVLab/PyTorch-StudioGAN

Applications of Diffusion Models in Agriculture

• Chen, Dong, et al. "Deep Data Augmentation for Weed Recognition Enhancement: A Diffusion Probabilistic Model and Transfer Learning Based Approach." arXiv preprint arXiv:2210.09509 (2022). [scholar] [paper]