Using a neural network to identify diseased potato plants

Relevance. In order to obtain high-quality seed material, seed farms should pay great attention to crop cultivation technologies. At the same time, an important role is played by the implementation of such breeding measure as phyto-cleaning of breeding and seed plots, in order to identify and eliminate infected plants. However, it is worth noting the fact that the implementation of such measure requires the presence of highly qualified specialists capable of detecting plant diseases at early stages. However, currently there is a shortage of such employees in agriculture, and therefore the development of innovative digital technologies aimed at detecting infected plants is an urgent task. Currently, machine vision and neural network technologies designed to solve such problems are actively developing.
Methods. As part of the research, existing machine vision technologies were analyzed, as well as developed machine learning technologies. Then, based on the analysis, a software package based on a convolutional neural network was developed. During the training and testing of the neural network, framing technologies, affine transformation methods, information and logical analysis of the initial information were used.
Results. To determine the quality of the software package for the identification of diseased potato plants, a series of tests was conducted. During the research, the accuracy with which the distribution of plants to a particular group was carried out was evaluated. The analysis of the results showed that the chosen neural network design successfully coped with the experimental task. At the same time, for the further development of this direction, it is necessary to create an extensive information base on potato diseases. That will allow in the future to develop a software and hardware complex for the analysis of potato plantings and the identification of infected plants in real time.

1. Zeyruk V.N., Vasilyeva S.V., Novikova I.I., Belyakova N.A., Derevyagina M.K., Belov G.L. Prospects of development of ecological methods of potato protection from diseases and pests. Agrarian science. 2019;3:54-59. (In Russian.)

2. Starovoitova O.A., Zhevora S.V., Starovoitov V.I. and ets. Competitive technologies of seed production, potato production and storage Moskva: Russianian Research Institute of Information and Technical and Economic Research on Engineering and Technical Support of the Agro-industrial complex, 2018. — 236 с. — ISBN 978-5-7367-1433-9 (In Russian.)

3. Zhevora S. V., Zeiruk V. N., Belov G. L. [et al.] Advanced diagnostic methods for potato pathogens Moscow: Russian Research Institute of Information and Technical and Economic Research on Engineering and Technical support of the agro-industrial complex, 2019. — 92с. — ISBN 978-5-7367-1504-6. (In Russian.)

4. Islam M. et al. Detection of potato diseases using image segmentation and multiclass support vector machine //2017 IEEE 30th canadian conference on electrical and computer engineering (CCECE). — IEEE, 2017. — С. 1-4

5. Wang H. et al. Image recognition of plant diseases based on backpropa-gation networks //2012 5th International Congress on Image and Signal Pro-cessing. — IEEE, 2012. — С. 894-900

6. Liu B. et al. Identification of apple leaf diseases based on deep convolu-tional neural networks //Symmetry. — 2018. — Т. 10. — №. 1. — С. 11.

7. Qin F. et al. Identification of alfalfa leaf diseases using image recognition technology //PLoS One. — 2016. — Т. 11. — №. 12. — С. e0168274.

8. Cruz A. C. et al. X-FIDO: An effective application for detecting olive quick decline syndrome with deep learning and data fusion //Frontiers in plant science. — 2017. — Т. 8. — С. 1741.

9. Anand R., Veni S., Aravinth J. An application of image processing tech-niques for detection of diseases on brinjal leaves using k-means clustering method //2016 international conference on recent trends in information tech-nology (ICRTIT). — IEEE, 2016. — С. 1-6.

10. Johannes A. et al. Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case //Computers and electronics in agriculture. — 2017. — Т. 138. — С. 200-209.

11. Krithika N., Selvarani A. G. An individual grape leaf disease identification using leaf skeletons and KNN classification //2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS). — IEEE, 2017. — С. 1-5.

12. Es-saady Y. et al. Automatic recognition of plant leaves diseases based on serial combination of two SVM classifiers //2016 International Conference on Electrical and Information Technologies (ICEIT). — IEEE, 2016. — С. 561-566

13. Rothe P. R., Kshirsagar R. V. Automated extraction of digital images features of three kinds of cotton leaf diseases //2014 International Conference on Electronics, Communication and Computational Engineering (ICECCE). — IEEE, 2014. — С. 67-71.

14. Padol P. B., Sawant S. D. Fusion classification technique used to detect downy and Powdery Mildew grape leaf diseases //2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC). — IEEE, 2016. — С. 298-301.

15. Fuentes A. et al. A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition //Sensors. — 2017. — Т. 17. — №. 9. — С. 2022.

16. Barbedo J. G. A. Factors influencing the use of deep learning for plant disease recognition //Biosystems engineering. — 2018. — Т. 172. — С. 84-91.

17. Barbedo J. G. A. Plant disease identification from individual lesions and spots using deep learning //Biosystems Engineering. — 2019. — Т. 180. — С. 96-107.

18. Nachtigall L. G., Araujo R. M., Nachtigall G. R. Classification of apple tree disorders using convolutional neural networks //2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI). — IEEE, 2016. — С. 472-476.

19. Liu B. et al. Identification of apple leaf diseases based on deep convolutional neural networks //Symmetry. — 2018. — Т. 10. — №. 1. — С. 11

20. Cruz A. C. et al. X-FIDO: An effective application for detecting olive quick decline syndrome with deep learning and data fusion // Frontiers in plant science. — 2017. — Т. 8. — С. 1741.

21. Dhakal A., Shakya S. Image-based plant disease detection with deep learning //International Journal of Computer Trends and Technology. — 2018. — Т. 61. — №. 1. — С. 26-29.