Open Access
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 |
- Taratukhin O.D., Novikova L.Yu., Seferova I.V., Kozlov K.N., Modeling of soybean phenology using artificial neural networks, Biophysics 64, 3, 563-571 (2019) [Google Scholar]
- Niazian, M, Niedbala, G, Machine Learning for Plant Breeding and Biotechnology, AGRICULTURE-BASEL 12-16 (2020) [Google Scholar]
- Kharchuk O.A., Kirillov A.F., non-Destructive determination of the leaf surface of soybean plants in seasonal dynamics, Eurasian Union of scientists 2-3 (59), 33-36 (2019) [Google Scholar]
- Samarkina E.I., Samarkina N.I., Sokolova I.G., Zharov I.N., Method for measuring the parameters of sheet plates using a digital image using specialized software, Plant resources 4(55): 537-547 (2019) [Google Scholar]
- Kurbanov R.K., Zakharova O.M., Zakharova N.I., Gorshkov D.M., Software for monitoring and controlling indicators of soybean crop selection processes, Innovations in agriculture 3 (32), 122-132 (2019) [Google Scholar]
- Machado B.B., BioLeaf: A professional mobile application to measure foliar damage caused by insect herbivory, Computers and Electronics in Agriculture 129, 44-55 (2016) [Google Scholar]
- T. Dauma, H. Buchwaldb, A. Gerlicherb, R. Birnera, Smartphone apps as a new method to collect data on smallholder farming systems in the digital age: A case study from Zambia, Computers and Electronics in Agriculture 153, 144-150 (2018) [Google Scholar]
- Tutygin V.S., al Vindi B.H.M.A., Ryabtsev I.A., System for recognizing plant diseases from leaf images based on fuzzy logic and neural networks, Modern science: actual problems of theory and practice. Series: Natural and technical Sciences 3, 107-115 (2019) [Google Scholar]
- Sh.P. Mohanty, D.P. Hughes, M. Salathé, Using Deep Learning for Image-Based Plant Disease Detection, Frontiers in Plant Science 7, 14-19 [Google Scholar]
- P.K. Gikunda, N. Jouandeau, State-of-the-Art Convolutional Neural Networks for Smart Farms: A Review, Advances in Intelligent Systems and Computing 763-775 (2019) [Google Scholar]
- Sala F, Arsene G.G., Iord?nescu O., Boldea M, Leaf area constant model in optimizing foliar area measurementin plants: A case study in apple tree, Sci. Hortic. 193, 218-224 (2015) [Google Scholar]
- Roginsky A.S., Moshtyls. O., Assessment of the relative area of damage by larvae of the chestnut mining moth (Cameraria ohridella) to the leaves of complex leaves of flowering and non-flowering specimens of the Siberian chestnut in green areas of Minsk.-75th scientific conference of students and postgraduates of the Belarusian state University 2, 342-346 (2018) [Google Scholar]
- A.I. Dyshekov, I.G. Smirnov, M.A. Mirzaev, M.A. Shereuzhev, Published Principles of functioning of the autonomous device for weed control for precision agriculture, Published under licence by IOP (2020) [Google Scholar]
- A.I. Dyshekov, I.G. Smirnov, M.A. Shereuzhev, Development of algorithm and technical device for weed recognition, Innovation in agriculture 28, 288-294 [Google Scholar]
- Sladojevic, Srdjan & Arsenovic, Marko & Anderla, Andras & Stefanovi?, Darko. Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification. Computational Intelligence and Neuroscience 1-11 (2016)10. 1155/2016/3289801 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.