Published April 30, 2026 | Version CC-BY-NC-ND 4.0

The Evolution of Image Processing: A Critical Overview of Modern Trends and Key Technologies

  • 1. Department of Bionic Engineering, College of Biological and Agricultural Engineering, Jilin University, China.

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  • 1. Department of Bionic Engineering, College of Biological and Agricultural Engineering, Jilin University, China.
  • 2. College of Agronomy, Gansu Agricultural University, Lanzhou, China.

Description

Abstract: Image processing has emerged as a critical tool across diverse domains, including agriculture, healthcare, industrial automation, and robotics. This review highlights the major technologies employed in image analysis and explores their methodologies, strengths, and practical applications. Approaches range from traditional image processing techniques to advanced machine learning and deep learning frameworks, as well as specialised modalities such as hyperspectral and 3D imaging. Each method provides distinct advantages, from simple filtering and segmentation to real-time object detection and high-precision phenotyping, enabling more accurate and efficient analysis across various fields.

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Dates

Accepted
2026-04-15
Manuscript received on 29 March 2026 | Revised Manuscript received on 04 April 2026 | Manuscript Accepted on 15 April 2026 | Manuscript published on 30 April 2026

References

  • Trigka, M.; Dritsas, E. A Comprehensive Survey of Deep Learning Approaches in Image Processing. Sensors 2025, 25, 531. DOI: https://doi.org/10.3390/s2502053.
  • Wang L, Zhang S, Xu N, He Q, Zhu Y, Chang Z, Wu Y, Wang H, Qi S, Zhang L, Shi Y, Qu X, Zhou X, Song J. Role of artificial intelligence in medical image analysis. Chin Med J (Engl). 2025 Nov 20; 138(22):2879-2894. Epub 2025 Oct 24. PMID: 41131954; PMCID: PMC12634253. DOI: https://doi.org/10.1097/CM9.0000000000003824
  • 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. DOI: https://doi.org/10.3390/app15169206.
  • V Srinivas Durga Prasad. (2022, December 15). Artificial Intelligence and Machine Learning-based Image Processing. Design & Reuse. https://www.design-reuse.com/article/61392/artificial-intelligence-and machine-learning-based-image-processing.
  • Unal, C.; Cinar, I.; Saripinar, Z.; Koklu, M. Comparative Evaluation of YOLOv8 and YOLO11 for Image-Based Classification of Sugar Beet Seed Treatment Levels. Sensors 2026, 26, 2137. DOI: https://doi.org/10.3390/s26072137.
  • Botero-Valencia, J.; García-Pineda, V.; Valencia-Arias, A.; Valencia, J.; Reyes-Vera, E.; Mejía-Herrera, M.; Hernández-García, R. Machine Learning in Sustainable Agriculture: Systematic Review and Research Perspectives. Agriculture 2025, 15, 377. DOI: https://doi.org/10.3390/agriculture15040377.
  • Zualkernan, I.; Abuhani, D.A.; Hussain, M.H.; Khan, J.; ElMohandes, M. Machine Learning for Precision Agriculture Using Imagery from Unmanned Aerial Vehicles (UAVs): A Survey. Drones 2023, 7, 382. DOI: https://doi.org/10.3390/drones7060382.
  • Chen, L.; Wu, Y.; Yang, N.; Sun, Z. Advances in Hyperspectral and Diffraction Imaging for Agricultural Applications. Agriculture 2025, 15, 1775. DOI: https://doi.org/10.3390/agriculture15161775.
  • Hyperspectral Camera Price (2026 Cost Guide) Hyperspectral imaging system: dark room with camera setup (left) and computer for image acquisition (right) (with permission. https://surfaceoptics.com/hyperspectral-camera-price.
  • Walsh JJ, Mangina E, Negrão S. Advancements in Imaging Sensors and AI for Plant Stress Detection: A Systematic Literature Review. Plant Phenomics. 2023 Mar 1; 6:0153. PMID: 38435466; PMCID: PMC10905704. DOI: https://doi.org/10.34133/plantphenomics.0153
  • Multispectral imaging—a space system captures a swath of Earth; each pixel records a spectrum to identify materials. Reproduced with permission from [[https://innoter.com/en/articles/multispectral imaging/] (https://innoter.com/en/articles/multispectral-imaging.).
  • Kim, E.; Kim, S.-Y.; Lee, C.-H.; Kim, S.; Ryu, J.; Kim, G.-H.; Lee, S. K.; Kim, G. Advanced 3D Depth Imaging Techniques for Morphometric Analysis of Detected On-Tree Apples Based on AI Technology. Agriculture 2025, 15, 1148. DOI: https://doi.org/10.3390/agriculture15111148.
  • Ait Nasser A, Akhloufi MA. A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography. Diagnostics (Basel). 2023 Jan 3;13(1):159. PMID: 36611451; PMCID: PMC9818166. DOI: https://doi.org/10.3390/diagnostics13010159