BIO Web Conf.
Volume 66, 2023International Scientific and Practical Conference “AGRARIAN SCIENCE - 2023” (AgriScience2023)
|Number of page(s)||6|
|Section||Economics and Management, Digital Platforms in the Agro-Industrial Complex|
|Published online||08 September 2023|
Digital monitoring of crops in grain ecosystems
1 Kuban State University, 149 Stavropolskaya st., 350040, Krasnodar, Russia
2 Kuban State Agrarian University named after I.T. Trubilin, 13 Kalinina st., 350044, Krasnodar, Russia
* Corresponding author: email@example.com
In the conditions of rapid global population growth, resource depletion, and increasing demand for grains, an efficient agricultural management system becomes a crucial element for ensuring food security in Russia and worldwide. The foundation of such management is an intelligent grain production monitoring system, where diagnosing grain crop diseases serves as a critically significant subsystem. This article presents an approach based on the utilization of neural networks, specifically the U-Net architecture for semantic segmentation, adapted for the analysis and detection of helminthosporium through images of maize leaves. Quality evaluation of segmentation employs metrics like Intersection over Union (IoU) and Dice coefficient, computed from a held-out dataset, ensuring an objective assessment of results. The research demonstrates high accuracy and similarity between the model's predictions and expert annotations, while also showcasing the convergence of loss function during neural network training. A notable advantage of the proposed approach lies in the lightweight nature of the suggested architecture and the ability to utilize trained models as cores for decision support systems, including on local devices without network connectivity.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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