Repository logo
  • English
  • Català
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Italiano
  • Latviešu
  • Magyar
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Suomi
  • Svenska
  • Türkçe
  • Tiếng Việt
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Yкраї́нська
  • Log In
    New user? Click here to register.Have you forgotten your password?
Repository logo
  • Communities & Collections
  • All of DSpace
  • English
  • Català
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Italiano
  • Latviešu
  • Magyar
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Suomi
  • Svenska
  • Türkçe
  • Tiếng Việt
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Yкраї́нська
  • Log In
    New user? Click here to register.Have you forgotten your password?
  1. Home
  2. Browse by Author

Browsing by Author "ANUSHA M"

Now showing 1 - 1 of 1
Results Per Page
Sort Options
  • Loading...
    Thumbnail Image
    Item
    MACHINE LEARNING REGRESSION TECHNIQUE FOR COTTON LEAF DISEASE DETECTION AND CONTROLLING USING IOT”
    (2019-06-17T09:58:17Z) CHANDINI C; JITIN JAIN MATHEW; ANUSHA M
    “Cotton is one of the most important cash crops in India. Every year the production of cotton is reducing due to the attack of the disease. Plant diseases are generally caused by pest insect and pathogens and decrease the productivity to large-scale if not controlled within time. This paper presents a system for detection and controlling of diseases on cotton leaf along with soil quality monitoring. The work proposes a Support Vector Machine based regression system for identification and classification of five cotton leaf diseases i.e. Bacterial Blight, Alternaria, Gray Mildew, Cereospra, and Fusarium wilt. After disease detection, the name of a disease with its remedies will be provided to the farmers using android app. The Android App is also used to display the soil parameters values such as humidity, moisture and temperature along with the water level in a tank. By using Android app farmers can ON/OFF the relay to control the motor and sprinkler assembly according to need. All this leaf disease detection system and sensors for soil quality monitoring are interfaced using Arduino which make it independent and cost-effective system. The overall classification accuracy of this proposed system is 83.26%.”

DSpace software copyright © 2002-2026 LYRASIS

  • Cookie settings
  • Privacy policy
  • End User Agreement
  • Send Feedback