sc 18: e10

Research Article

IOT Enabled Weedicide Control Using Image Processing at Agriculture Field

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  • @ARTICLE{10.4108/eai.30-6-2021.170252,
        author={G. Manjula and P. Visu and S. Chakaravarthi},
        title={IOT Enabled Weedicide Control Using Image Processing at Agriculture Field},
        journal={EAI Endorsed Transactions on Smart Cities: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SC},
        year={2021},
        month={6},
        keywords={IOT, Image Processing, Arduino Mega Platform, GSM, Smart farming},
        doi={10.4108/eai.30-6-2021.170252}
    }
    
  • G. Manjula
    P. Visu
    S. Chakaravarthi
    Year: 2021
    IOT Enabled Weedicide Control Using Image Processing at Agriculture Field
    SC
    EAI
    DOI: 10.4108/eai.30-6-2021.170252
G. Manjula1,*, P. Visu2, S. Chakaravarthi3
  • 1: P.G Student, Department CSE, Velammal Engineering College, Chennai, India
  • 2: Professor, Department CSE, Velammal Engineering College, Chennai, India
  • 3: Professor & Head, Department of CSE, Velammal Engineering College, Surapet, Chennai
*Contact email: gmmanju17@gmail.com

Abstract

The Aim of this project is to automate plant monitoring and smart gardening using IOT in the Arduino Mega Platform. Identifying diseases in plants leave is a challenging task for farmers and also for researchers. The key highlight of the project is able to detect the type of disease by use of image processing. Image Processing steps are pre-processing, spot segmentation and features extraction, and classification. The extracted features are optimized by genetic algorithm and classified by KNN Classifier. We proposed a methodology that is tested for four types of apple plant disease including healthy leaves, Black Rot, Rust, and Scab. When the disease is identified we provided a pesticide solution displayed in the LCD Display and the same is sent to the farmer mobile with the help of GSM. All the Stages are monitored in an IOT Webpage.