TRAINING OF YOLOV5 NEURAL NETWORK FOR CLASSIFICATION OF PLANT SPECIES AND DISEASES BY PHOTOGRAPHS OF PLANT LEAVES

Authors

  • Ērika Dmitrijeva Rezekne Academy of Technologies
  • Arina Amālija Belova Rezekne Academy of Technologies
  • Sergejs Kodors Rezekne Academy of Technologies

DOI:

https://doi.org/10.17770/het2022.26.6946

Keywords:

diseases, neural network, plants, object detection, RoboFlow, YoLoV5,

Abstract

40% percent of crops are lost every year due to plant diseases. It is physically difficult for people to detect plant diseases in large-scale fields, especially at an early stage. The paper deals with the YoloV5 neural network training using different technologies. The neural network is trained to classify plant species and their diseases using photographs. The open access dataset PlantDoc was used for training. PlantDoc provides 2,569 images of 13 plant species and 27 classes for image classification and object detection. For the purity of the experiment, training was performed 10 times without changing the parameters. As a result of each training, we had obtained testing data on which we could draw conclusions.

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References

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Published

2023-01-09