Diffusion models for agricultural imaging: a systematic review of methods, applications and future prospects

Zangana, Hewa Majeed and Li, Shuai and Wani, Sharyar (2025) Diffusion models for agricultural imaging: a systematic review of methods, applications and future prospects. Impact in Agriculture, 1 (3). pp. 1-11. E-ISSN 3122-735X

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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

Item Type:	Article (Review)
Uncontrolled Keywords:	Diffusion Models; Synthetic Data Generation; Agricultural Imaging; Plant Disease Detection; Weed and Pest Monitoring; UAV Crop Phenotyping; Deep Learning in Agriculture
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr. Sharyar Wani
Date Deposited:	22 Jan 2026 15:29
Last Modified:	22 Jan 2026 15:29
Queue Number:	2026-01-Q1764
URI:	http://irep.iium.edu.my/id/eprint/126982

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