Identification of Unhealthy Leaves in Paddy by using Computer Vision based Deep Learning Model

U. Vignesh, R. Elakya

Research output: Contribution to journalArticlepeer-review


India is one of the leading productions of Paddy. Compared to previous year Gross Domestic Product (GDP) Export rate of Paddy in the year 2021 has increased to around 33%. Paddy is the Major food production crop in India. Every crop is prone to many diseases throughout their lifespan. The disease can affect the crop at any stage of their growing phase. Early detection of disease is the only solution to reduce the damage. Early detection may reduce the damage caused and increase the quality as well as quantity of Production. Major disease which causes more damage in paddy production is Rice Blast, Brown Spot, Sheath Blight, Sheath Rot and False Smut. Early detection of these diseases can reduce the damage and increase the production value. Recent technology of computer vision and by using Deep learning model can accurately predict and diagnose the early symptom of diseases. We used Convolutional Neural Network classifier of deep learning model to predict the early symptom of disease in paddy. We compared four main classifier VGG16, VGG19, Inception-V3 and ResNet50, among these four Inception-V3 achieved a highest accuracy of 95.3%.

Original languageEnglish
Article numberIJEER-RDEC9305
Pages (from-to)796-800
Number of pages5
JournalInternational Journal of Electrical and Electronics Research
Issue number4
Publication statusPublished - 2022

All Science Journal Classification (ASJC) codes

  • Engineering (miscellaneous)
  • Electrical and Electronic Engineering


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