Journal Cover – Impact in Agriculture

Impact in Agriculture

Peer-Reviewed • Open Access e-ISSN: 3122-735X

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Diffusion Models for Agricultural Imaging: A Systematic Review of Methods, Applications and Future Prospects

Hewa Majeed Zangana 1 ORCID , Shuai Li 2 ORCID , Sharyar Wani 3 ORCID
1 Duhok Technical College, Duhok Polytechnic University, Kurdistan Region 42001, Iraq
2 Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu 90570, Finland
3 Department of Computer Science, Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia
DOI: https://doi.org/10.65500/agriculture-2025-003
Received: 7 September 2025 | Revised: 13 October 2025 | Accepted: 11 November 2025 | Published: 21 November 2025
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Abstract

Diffusion models are rapidly reshaping agricultural image analysis, offering high-fidelity synthetic data generation where real datasets are limited, imbalanced, or costly to collect. Traditional augmentation and GAN-based synthesis often struggle to preserve fine disease features and crop textures, leading to suboptimal model performance in real field conditions. This review consolidates the latest research on diffusion-based methods applied to plant disease diagnosis, fruit quality assessment, weed and pest monitoring, nematode identification, green-wall health evaluation, and UAV-based phenotyping. Reported literature demonstrates improved texture detail, lesion clarity, and better classification accuracy when diffusion-generated images supplement training datasets. Techniques such as latent diffusion and ControlNet enhance structure control, while text-guided models support domain transfer and unseen class synthesis. Despite promising outcomes, challenges remain concerning computational cost, real-world generalization across farms and seasons, and lack of standardized evaluation protocols. Future progress is expected through multimodal diffusion integrating hyperspectral and thermal inputs, efficient deployment on edge devices, and development of open benchmarks for comparative analysis. This review positions diffusion models as a leading generative approach for agricultural AI and outlines the research opportunities needed for practical adoption in large-scale farming environments.

Keywords: Diffusion Models; Synthetic Data Generation; Agricultural Imaging; Plant Disease Detection; Weed and Pest Monitoring; UAV Crop Phenotyping; Deep Learning in Agriculture.

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References

  1. Fenu, G.; Malloci, F.M. Evaluating Impacts between Laboratory and Field-Collected Datasets for Plant Disease Classification. Agronomy 2022, 12, doi:10.3390/AGRONOMY12102359.
  2. Yang, G.; Liu, C.; Li, G.; Chen, H.; Chen, K.; Wang, Y.; Hu, X. DiffKNet-TL: Maize Phenology Monitoring with Confidence-Aware Constrained Diffusion Model Based on UAV Platform. Comput Electron Agric 2025, 239, doi:10.1016/j.compag.2025.110936.
  3. Ayoub, S.; Gulzar, Y.; Reegu, F.A.; Turaev, S. Generating Image Captions Using Bahdanau Attention Mechanism and Transfer Learning. Symmetry (Basel) 2022, 14, 2681.
  4. Kumar, T.; Brennan, R.; Mileo, A.; Bendechache, M. Image Data Augmentation Approaches: A Comprehensive Survey and Future Directions. IEEE Access 2024, 12, 187536–187571, doi:10.1109/ACCESS.2024.3470122.
  5. Wang, Z.; Wang, P.; Liu, K.; Wang, P.; Fu, Y.; Lu, C.-T.; Aggarwal, C.C.; Pei, J.; Zhou, Y. A Comprehensive Survey on Data Augmentation. IEEE Trans Knowl Data Eng 2025, 1–20, doi:10.1109/TKDE.2025.3622600.
  6. Kang, J.; Yoon, J.; Park, B.; Kim, J.; Jee, S.; Song, H.; Chung, H. RipenessGAN: Growth Day Embedding-Enhanced GAN for Stage-Wise Jujube Ripeness Data Generation. Agronomy 2025, 15, doi:10.3390/agronomy15102409.
  7. Espinoza, S.; Aguilera, C.; Rojas, L.; Campos, P.G. Analysis of Fruit Images With Deep Learning: A Systematic Literature Review and Future Directions. IEEE Access 2024, 12, 3837–3859, doi:10.1109/ACCESS.2023.3345789.
  8. Deng, B.; Lu, Y. Weed Image Augmentation by ControlNet-Added Stable Diffusion. https://doi.org/10.1117/12.3014145 2024, 13035, 188–200, doi:10.1117/12.3014145.
  9. Ambuj; Machavaram, R. Weed Image Classification with Generative AI Using Latent Denoising Diffusion Probabilistic Model and Wiener Filtering Approach. Franklin Open 2025, 12, doi:10.1016/j.fraope.2025.100369.
  10. Sordo, Z.; Chagnon, E.; Hu, Z.; Donatelli, J.J.; Andeer, P.F.; Nico, P.S.; Northen, T.R.; Ushizima, D.M.U. Synthetic Scientific Image Generation with VAE, GAN, and Diffusion Model Architectures. J Imaging 2025, 11, doi:10.3390/jimaging11080252.
  11. Yu, Y.; Wu, X.; Yu, P.; Wan, Q.; Dan, Y.; Xiao, Y.; Wang, Q. Location-Guided Lesions Representation Learning via Image Generation for Assessing Plant Leaf Diseases Severity. Plant Phenomics 2025, 7, doi:10.1016/j.plaphe.2025.100058.
  12. Das, A.; Mahto, R.; Wang, W.; Jamali, A.; Singh, P.; Roy, S.K. A Generative Framework for Detection and Classification of Plant Leaf Disease Using Diffusion Network. Appl Soft Comput 2025, 177, doi:10.1016/j.asoc.2025.113152.
  13. Deng, B.; Lu, Y. Weed Image Augmentation by ControlNet-Added Stable Diffusion for Multi-Class Weed Detection. Comput Electron Agric 2025, 232, doi:10.1016/j.compag.2025.110123.
  14. Huang, J.; Wang, Z.; Xu, M.; Ma, L.; Wu, W.; Cao, J. Enhancing Few-Shot Plant Disease Classification with Diffusion Model. Proceedings - 2024 China Automation Congress, CAC 2024 2024, 2036–2040, doi:10.1109/CAC63892.2024.10864650.
  15. Chen, D.; Qi, X.; Zheng, Y.; Lu, Y.; Huang, Y.; Li, Z. Synthetic Data Augmentation by Diffusion Probabilistic Models to Enhance Weed Recognition. Comput Electron Agric 2024, 216, 108517, doi:10.1016/J.COMPAG.2023.108517.
  16. Ouyang, X.; Zhuang, J.; Gu, J.; Ye, S. Few-Shot Data Augmentation by Morphology-Constrained Latent Diffusion for Enhanced Nematode Recognition. Computers 2025, 14, doi:10.3390/computers14050198.
  17. Hirahara, K.; Nakane, C.; Ebisawa, H.; Kuroda, T.; Iwaki, Y.; Utsumi, T.; Nomura, Y.; Koike, M.; Mineno, H. D4: Text-Guided Diffusion Model-Based Domain Adaptive Data Augmentation for Vineyard Shoot Detection. Comput Electron Agric 2025, 230, doi:10.1016/j.compag.2024.109849.
  18. Mori, N.; Naito, H.; Hosoi, F. Application of a Latent Diffusion Model to Plant Disease Detection by Generating Unseen Class Images. AgriEngineering 2024, 6, 4901–4910, doi:10.3390/agriengineering6040279.
  19. Ho, J.; Jain, A.; Abbeel, P. Denoising Diffusion Probabilistic Models. Adv Neural Inf Process Syst 2020, 33, 6840–6851.
  20. Song, J.; Meng, C.; Ermon, S. Denoising Diffusion Implicit Models. ICLR 2021 - 9th International Conference on Learning Representations 2020.
  21. Li, J.; Zhu, C.; Yang, C.; Zheng, Q.; Wang, B.; Tu, J.; Zhang, Q.; Liu, S.; Wang, X.; Qiao, J. DM_CorrMatch: A Semi-Supervised Semantic Segmentation Framework for Rapeseed Flower Coverage Estimation Using UAV Imagery. Plant Methods 2025, 21, doi:10.1186/s13007-025-01373-w.
  22. Zhang, S.; Liu, L.; Li, G.; Du, Y.; Wu, X.; Song, Z.; Li, X. Diffusion Model-Based Image Generative Method for Quality Monitoring of Direct Grain Harvesting. Comput Electron Agric 2025, 233, doi:10.1016/j.compag.2025.110130.
  23. Rombach, R.; Blattmann, A.; Lorenz, D.; Esser, P.; Ommer, B. High-Resolution Image Synthesis With Latent Diffusion Models 2022, 10684–10695.
  24. Deng, B.; Lu, Y. Image Prompt Adapter-Based Stable Diffusion for Enhanced Multi-Class Weed Generation and Detection. AgriEngineering 2025, 7, doi:10.3390/agriengineering7110389.
  25. Zhang, L.; Zhao, Y.; Zhou, C.; Zhang, J.; Yan, Y.; Chen, T.; Lv, C. JuDifformer: Multimodal Fusion Model with Transformer and Diffusion for Jujube Disease Detection. Comput Electron Agric 2025, 232, doi:10.1016/j.compag.2025.110008.
  26. Astuti, T.; Umar, A.N.; Wahyudi, R.; Rifai, Z. Enhancing Potato Leaf Disease Detection: Implementation of Convolutional Vision Transformers with Synthetic Datasets from Stable Diffusion. International Journal on Informatics Visualization 2024, 8, 2054–2065, doi:10.62527/joiv.8.4.2167.
  27. Ho, J.; Salimans, T. Classifier-Free Diffusion Guidance. 2022.
  28. Zhang, L.; Rao, A.; Agrawala, M. Adding Conditional Control to Text-to-Image Diffusion Models 2023, 3836–3847.
  29. Yang, Y.; Li, W.; Liu, R.; Wu, C.; Ren, J.; Shi, Y.; Ge, S. HindwingLib: A Library of Leaf Beetle Hindwings Generated by Stable Diffusion and ControlNet. Sci Data 2025, 12, doi:10.1038/s41597-025-05010-y.
  30. Lee, Y. Enhancing Plant Health Classification via Diffusion Model-Based Data Augmentation. Multimed Syst 2025, 31, doi:10.1007/s00530-025-01745-1.
  31. Wang, R.; Zhang, X.; Yang, Q.; Lei, L.; Liang, J.; Yang, L. Enhancing Panax Notoginseng Leaf Disease Classification with Inception-SSNet and Image Generation via Improved Diffusion Model. Agronomy 2024, 14, doi:10.3390/agronomy14091982.
  32. Du, M.; Wang, F.; Yan, W.; Guo, J.; Liu, L.; Lv, P.; He, Y.; Feng, X.; Wang, Y. Improving Food Safety: Synthetic Data Augmentation for Accurate Mushroom Species Identification in Complex Environments. Applied Food Research 2025, 5, doi:10.1016/j.afres.2025.101039.
  33. Modak, S.; Stein, A. Generative AI-Based Pipeline Architecture for Increasing Training Efficiency in Intelligent Weed Control Systems. Journal of Systems Architecture 2025, 167, doi:10.1016/j.sysarc.2025.103464.
  34. Cheng, Z.; Gu, X.; Zhang, Z.; Xu, Y.; Zhao, T.; Li, Y.; Sun, S.; Du, Y.; Cai, H. TAM-Net: A Deep Network Combining Tabular Diffusion Algorithm, Attention Mechanism, and Multi-Task Learning for Monitoring Crop Water Status from UAV Multi-Source Images. European Journal of Agronomy 2025, 170, doi:10.1016/j.eja.2025.127778.
  35. Wang, S.; Xu, S.; Lv, C.; Cai, F. RSSR-CDM: Remote Sensing Image Super-Resolution Based on an Improved Conditional Diffusion Model. J Electron Imaging 2025, 34, doi:10.1117/1.JEI.34.4.043046.
  36. Jasphin, E.T.J.; Joice, C.S. Weed Classification and Crop Health Monitoring in Microclimatic Conditions Using Thermal Image Analysis and Deep Learning Algorithms. J Plant Growth Regul 2025, 44, 2247–2263, doi:10.1007/s00344-024-11542-1.
  37. Wang, L.; Zhang, Z.; Yao, H. Agricrafter: Full-Growth-Cycle Crop Video Generation via Diffusion Models. Comput Electron Agric 2025, 239, doi:10.1016/j.compag.2025.110946.
  38. Li, W.; Yang, L.; Peng, G.; Pang, G.; Yu, Z.; Zhu, X. An Effective Microscopic Image Augmentation Approach. Sci Rep 2025, 15, doi:10.1038/s41598-025-93954-x.
  39. Yoon, M.; Lee, Y. Novel Augmentation Techniques Using Diffusion Models for Green Wall Plant Health Classification. Comput Biol Med 2025, 189, doi:10.1016/j.compbiomed.2025.109899.
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