ARTIFICIAL INTELLIGENCE-BASED SYSTEMS FOR MONITORING THE QUALITY OF VEGETABLE CROP SEEDS
Received: 2026-05-31
Published: 2026-06-06
Abstract
This article analyzes modern methods and the significance of monitoring systems for vegetable crop seed quality based on artificial intelligence technologies in agriculture. The study examines the possibilities of using artificial intelligence algorithms, computer vision technologies, and data processing methods to determine seed germination, purity, moisture content, and resistance to diseases. In addition, the article highlights the role of automated monitoring systems in rapidly and accurately assessing seed quality, reducing human error, and increasing crop productivity. The research findings are of important scientific and practical significance for the implementation of digital technologies and the development of smart agrotechnologies in vegetable farming.
Keywords
List of references
-
Kamilaris, A., Prenafeta-Boldú, F.X. (2018). Deep Learning in Agriculture: A Survey. Computers and Electronics in Agriculture, 147, 70–90. https://www.sciencedirect.com/science/article/pii/S0168169917308803
-
Mohanty, S.P., Hughes, D.P., Salathé, M. (2016). Using Deep Learning for Image-Based Plant Disease Detection. Frontiers in Plant Science, 7, 1419. https://www.frontiersin.org/articles/10.3389/fpls.2016.01419
-
Patel, K.K., Kar, A., Jha, S.N., Khan, M.A. (2012). Machine Vision System: A Tool for Quality Evaluation in Food and Agricultural Products. Journal of Food Science and Technology, 49(2), 123–141. https://link.springer.com/article/10.1007/s13197-011-0321-4
-
Mahlein, A.K. (2016). Plant Disease Detection by Imaging Sensors. Annual Review of Phytopathology, 54, 373–393. https://www.annualreviews.org/doi/10.1146/annurev-phyto-080615-100118
-
Li, J., Huang, W., Zhao, C., Zhang, B. (2019). Applications of Hyperspectral Imaging Technology in Seed Quality Detection. Sensors, 19(5), 1090. https://www.mdpi.com/1424-8220/19/5/1090
-
Shahin, M.A., Symons, S.J. (2001). Seed Sizing from Digital Images. Canadian Biosystems Engineering, 43, 3.1–3.5. https://csbe-scgab.ca/docs/journal/43/c0301.pdf
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