Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security, IACIDS 2023, 23-25 November 2023, Lavasa, Pune, India

Research Article

Bell Pepper Leaf Disease Classification Using Support Vector Machine

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  • @INPROCEEDINGS{10.4108/eai.23-11-2023.2343177,
        author={Suryaprabha  D and Saraswathi  S and Lekha  J and Loghesh  VS},
        title={ Bell Pepper Leaf Disease Classification Using Support Vector Machine},
        proceedings={Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security, IACIDS 2023, 23-25 November 2023, Lavasa, Pune, India},
        publisher={EAI},
        proceedings_a={IACIDS},
        year={2024},
        month={3},
        keywords={bell pepper support vector machine classify image dataset bacterial leaves},
        doi={10.4108/eai.23-11-2023.2343177}
    }
    
  • Suryaprabha D
    Saraswathi S
    Lekha J
    Loghesh VS
    Year: 2024
    Bell Pepper Leaf Disease Classification Using Support Vector Machine
    IACIDS
    EAI
    DOI: 10.4108/eai.23-11-2023.2343177
Suryaprabha D1,*, Saraswathi S1, Lekha J2, Loghesh VS2
  • 1: Nehru Arts and Science College, Coimbatore
  • 2: Christ University, Lavasa, Pune
*Contact email: spayrus@gmail.com

Abstract

Bell Pepper is a plant commonly cultivated in India and a significant exporter to various foreign nations. Bell Pepper is a type of plant that is mainly utilized as a spice. However, bacterial infection occurs while crop cultivation is rapidly increasing, leading to low-quality exports and degraded levels of spice content in the plants. In our paper, we utilize the classification technique of Support Vector Machine (SVM) to extract the features of the plant leaves and develop an algorithm to accurately classify the bacterial leaves to prevent the further spread of the disease. The overall process encompasses utilizing an image dataset containing images of diseased and healthy plants, converting the images into a gray scale version, extracting the image's essential features, and utilizing the image to receive an accurate result from the classifier. This research will identify the rotten plants by detecting the bacterial spots present in the leaves and save the time and effort of the farmers cultivating the plants.