System Development for Liquid Chemicals Point Injection Based on Convolutional Neural Network Models
The authors showed that one of the reasons for the yield loss is poor-quality determination of the infection degree of agricultural crops by pathogens. They proposed a system of liquid chemicals point application. They identified the possibility of calculating the required amount of fertilizers and...
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| Format: | Article |
| Language: | Russian |
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Federal Scientific Agroengineering Centre VIM
2021-06-01
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| Series: | Сельскохозяйственные машины и технологии |
| Subjects: | |
| Online Access: | https://www.vimsmit.com/jour/article/view/426 |
| _version_ | 1797872128936116224 |
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| author | V. S. Semenyuk E. A. Nikitin |
| author_facet | V. S. Semenyuk E. A. Nikitin |
| author_sort | V. S. Semenyuk |
| collection | DOAJ |
| description | The authors showed that one of the reasons for the yield loss is poor-quality determination of the infection degree of agricultural crops by pathogens. They proposed a system of liquid chemicals point application. They identified the possibility of calculating the required amount of fertilizers and protective equipment. (Research purpose) To develop a system of liquid chemicals point application for plant protection and nutrition based on a convolutional neural network model. (Materials and methods) The authors analyzed the existing methods of machine learning. When developing the system, they used the U-net-algorithm of convolutional neural networks, as well as data displaying diseases of winter and spring wheat – brown rust and powdery mildew. Each image was cropped by hand and marked up using a specialized Python library. In the course of applying the architecture, the authors experimentally chose the optimal metrics (jaccard metric), the learning rate – 0.0001 seconds, the number of epochs – 300, and other indicators. (Results and discussion) The authors found that when a new, previously unavailable image was submitted to the algorithm, it recognized the disease in a few seconds and returned to the user not only the original image, but also a mask over it. The accuracy of applying the mask to the affected area was determined – 80 percent. They showed that the predicted error on the validation data was 0.18758. In practice, it could differ from the declared one by no more than 10-15 percent. The authors suggested using the algorithm with a vision system. (Conclusions) The authors showed that technical means imperfection for plants chemicalization increased the consumption up to 30 percent relative to the volume required for point application. They developed a neural network algorithm for identifying the affected areas of plants and proposed the concept of a point chemicals application in order to reduce the costs of processing crops. It was determined that the neural network was able to diagnose the affected areas of plants in 1 second. |
| first_indexed | 2024-04-10T00:54:58Z |
| format | Article |
| id | doaj.art-38a6f40b48154e95a228359f9de9efae |
| institution | Directory Open Access Journal |
| issn | 2073-7599 |
| language | Russian |
| last_indexed | 2024-04-10T00:54:58Z |
| publishDate | 2021-06-01 |
| publisher | Federal Scientific Agroengineering Centre VIM |
| record_format | Article |
| series | Сельскохозяйственные машины и технологии |
| spelling | doaj.art-38a6f40b48154e95a228359f9de9efae2023-03-13T10:16:48ZrusFederal Scientific Agroengineering Centre VIMСельскохозяйственные машины и технологии2073-75992021-06-01152414510.22314/2073-7599-2021-15-1-41-45383System Development for Liquid Chemicals Point Injection Based on Convolutional Neural Network ModelsV. S. Semenyuk0E. A. Nikitin1Национальный исследовательский университет «Высшая школа экономики»Федеральный научный агроинженерный центр ВИМThe authors showed that one of the reasons for the yield loss is poor-quality determination of the infection degree of agricultural crops by pathogens. They proposed a system of liquid chemicals point application. They identified the possibility of calculating the required amount of fertilizers and protective equipment. (Research purpose) To develop a system of liquid chemicals point application for plant protection and nutrition based on a convolutional neural network model. (Materials and methods) The authors analyzed the existing methods of machine learning. When developing the system, they used the U-net-algorithm of convolutional neural networks, as well as data displaying diseases of winter and spring wheat – brown rust and powdery mildew. Each image was cropped by hand and marked up using a specialized Python library. In the course of applying the architecture, the authors experimentally chose the optimal metrics (jaccard metric), the learning rate – 0.0001 seconds, the number of epochs – 300, and other indicators. (Results and discussion) The authors found that when a new, previously unavailable image was submitted to the algorithm, it recognized the disease in a few seconds and returned to the user not only the original image, but also a mask over it. The accuracy of applying the mask to the affected area was determined – 80 percent. They showed that the predicted error on the validation data was 0.18758. In practice, it could differ from the declared one by no more than 10-15 percent. The authors suggested using the algorithm with a vision system. (Conclusions) The authors showed that technical means imperfection for plants chemicalization increased the consumption up to 30 percent relative to the volume required for point application. They developed a neural network algorithm for identifying the affected areas of plants and proposed the concept of a point chemicals application in order to reduce the costs of processing crops. It was determined that the neural network was able to diagnose the affected areas of plants in 1 second.https://www.vimsmit.com/jour/article/view/426точечное внесение удобренийвнесения средств защиты растенийсверточная нейронная сетьмашинное обучениеискусственный интеллект |
| spellingShingle | V. S. Semenyuk E. A. Nikitin System Development for Liquid Chemicals Point Injection Based on Convolutional Neural Network Models Сельскохозяйственные машины и технологии точечное внесение удобрений внесения средств защиты растений сверточная нейронная сеть машинное обучение искусственный интеллект |
| title | System Development for Liquid Chemicals Point Injection Based on Convolutional Neural Network Models |
| title_full | System Development for Liquid Chemicals Point Injection Based on Convolutional Neural Network Models |
| title_fullStr | System Development for Liquid Chemicals Point Injection Based on Convolutional Neural Network Models |
| title_full_unstemmed | System Development for Liquid Chemicals Point Injection Based on Convolutional Neural Network Models |
| title_short | System Development for Liquid Chemicals Point Injection Based on Convolutional Neural Network Models |
| title_sort | system development for liquid chemicals point injection based on convolutional neural network models |
| topic | точечное внесение удобрений внесения средств защиты растений сверточная нейронная сеть машинное обучение искусственный интеллект |
| url | https://www.vimsmit.com/jour/article/view/426 |
| work_keys_str_mv | AT vssemenyuk systemdevelopmentforliquidchemicalspointinjectionbasedonconvolutionalneuralnetworkmodels AT eanikitin systemdevelopmentforliquidchemicalspointinjectionbasedonconvolutionalneuralnetworkmodels |