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

Ērika Dmitrijeva, Arina Amālija Belova, Sergejs Kodors

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.

Keywords


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

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References


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

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