Agricultural Image Processing: Challenges, Advances, and Future Trends

Song, Xuehua; Yan, Letian; Liu, Sihan; Gao, Tong; Han, Li; Jiang, Xiaoming; Jin, Hua; Zhu, Yi

doi:10.3390/app15169206

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Agricultural Image Processing: Challenges, Advances, and Future Trends

by

Xuehua Song

,

Letian Yan

,

Sihan Liu

,

Tong Gao

,

Li Han

,

Xiaoming Jiang

,

Hua Jin

and

Yi Zhu

^*

School of Computer Science and Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China

^*

Author to whom correspondence should be addressed.

Appl. Sci. 2025, 15(16), 9206; https://doi.org/10.3390/app15169206 (registering DOI)

Submission received: 3 July 2025 / Revised: 5 August 2025 / Accepted: 19 August 2025 / Published: 21 August 2025

(This article belongs to the Special Issue Pattern Recognition Applications of Neural Networks and Deep Learning)

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Abstract

Agricultural image processing technology plays a critical role in enabling precise disease detection, accurate yield prediction, and various smart agriculture applications. However, its practical implementation faces key challenges, including environmental interference, data scarcity and imbalance datasets, and the difficulty of deploying models on resource-constrained edge devices. This paper presents a systematic review of recent advances in addressing these challenges, with a focus on three core aspects: environmental robustness, data efficiency, and model deployment. The study identifies that attention mechanisms, Transformers, multi-scale feature fusion, and domain adaptation can enhance model robustness under complex conditions. Self-supervised learning, transfer learning, GAN-based data augmentation, SMOTE improvements, and Focal loss optimization effectively alleviate data limitations. Furthermore, model compression techniques such as pruning, quantization, and knowledge distillation facilitate efficient deployment. Future research should emphasize multi-modal fusion, causal reasoning, edge–cloud collaboration, and dedicated hardware acceleration. Integrating agricultural expertise with AI is essential for promoting large-scale adoption, as well as achieving intelligent, sustainable agricultural systems.

Keywords: agriculture; image processing; environmental challenges; data challenges; model lightweighting

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MDPI and ACS Style

Song, X.; Yan, L.; Liu, S.; Gao, T.; Han, L.; Jiang, X.; Jin, H.; Zhu, Y. Agricultural Image Processing: Challenges, Advances, and Future Trends. Appl. Sci. 2025, 15, 9206. https://doi.org/10.3390/app15169206

AMA Style

Song X, Yan L, Liu S, Gao T, Han L, Jiang X, Jin H, Zhu Y. Agricultural Image Processing: Challenges, Advances, and Future Trends. Applied Sciences. 2025; 15(16):9206. https://doi.org/10.3390/app15169206

Chicago/Turabian Style

Song, Xuehua, Letian Yan, Sihan Liu, Tong Gao, Li Han, Xiaoming Jiang, Hua Jin, and Yi Zhu. 2025. "Agricultural Image Processing: Challenges, Advances, and Future Trends" Applied Sciences 15, no. 16: 9206. https://doi.org/10.3390/app15169206

APA Style

Song, X., Yan, L., Liu, S., Gao, T., Han, L., Jiang, X., Jin, H., & Zhu, Y. (2025). Agricultural Image Processing: Challenges, Advances, and Future Trends. Applied Sciences, 15(16), 9206. https://doi.org/10.3390/app15169206

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