| Issue |
E3S Web Conf.
Volume 217, 2020
International Scientific and Practical Conference “Environmental Risks and Safety in Mechanical Engineering” (ERSME-2020)
|
|
|---|---|---|
| Article Number | 10006 | |
| Number of page(s) | 8 | |
| Section | Natural Resource and Soil Management | |
| DOI | https://doi.org/10.1051/e3sconf/202021710006 | |
| Published online | 14 December 2020 | |
Algorithm for recognizing and measuring parameters of biological objects in agriculture based on deep learning convolutional neural networks
Federal Scientific Agro Engineering Center VIM, 109428, 1st Institutsky proezd, 5, Moscow, Russia
* Corresponding author: arturnex@gmail.com
As of today existing techniques and tools for measuring leaf area involve the detachment of leaves for further scanning and calculations to determine leaf area. The disadvantages of existing solutions for determining the area of the sheet surface are labor intensity, the duration of these studies, the relatively low accuracy of measurements. Due to these facts this study is an important work aimed to simplifying the process of analyzing biological parameters and other important characteristics of plants, as well as increasing the efficiency of this agrotechnical task. This work aims developing a set of software tools with trained neural networks to determine whether a photographed leaf belongs to the leaves of a soybean crop, assess the health of soybean plants and determine the surface area of a soybean leaf with geotagging.
© The Authors, published by EDP Sciences, 2020
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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