International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Deep Learning Methods for Recognition of Orchard Crops' Diseases

  • Sabitov, Baratbek (Kyrgyz National University named after I. Arabaev, Department of Applied Informatics) ;
  • Biibsunova, Saltanat (Arabaev Kyrgyz State University, Department of Applied Informatics) ;
  • Kashkaroeva, Altyn (Arabaev Kyrgyz State University, Department of Applied Informatics) ;
  • Biibosunov, Bolotbek (Arabaev Kyrgyz State University, Department of Applied Informatics)
  • Received : 2022.10.05
  • Published : 2022.10.30
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Abstract

Diseases of agricultural plants in recent years have spread greatly across the regions of the Kyrgyz Republic and pose a serious threat to the yield of many crops. The consequences of it can greatly affect the food security for an entire country. Due to force majeure, abnormal cases in climatic conditions, the annual incomes of many farmers and agricultural producers can be destroyed locally. Along with this, the rapid detection of plant diseases also remains difficult in many parts of the regions due to the lack of necessary infrastructure. In this case, it is possible to pave the way for the diagnosis of diseases with the help of the latest achievements due to the possibilities of feedback from the farmer - developer in the formation and updating of the database of sick and healthy plants with the help of advances in computer vision, developing on the basis of machine and deep learning. Currently, model training is increasingly used already on publicly available datasets, i.e. it has become popular to build new models already on trained models. The latter is called as transfer training and is developing very quickly. Using a publicly available data set from PlantVillage, which consists of 54,306 or NewPlantVillage with a data volumed with 87,356 images of sick and healthy plant leaves collected under controlled conditions, it is possible to build a deep convolutional neural network to identify 14 types of crops and 26 diseases. At the same time, the trained model can achieve an accuracy of more than 99% on a specially selected test set.

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References

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