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

Tomato Leaf Disease Detection using Machine Learning Model

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  • @INPROCEEDINGS{10.4108/eai.23-11-2023.2343189,
        author={Lekha  J and Saraswathi  S and Suryaprabha  D and Noel Mathew Thomas},
        title={Tomato Leaf Disease Detection using Machine Learning Model},
        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={machine learning plant disease detection knn cnn svm tomato leaf disease detection},
        doi={10.4108/eai.23-11-2023.2343189}
    }
    
  • Lekha J
    Saraswathi S
    Suryaprabha D
    Noel Mathew Thomas
    Year: 2024
    Tomato Leaf Disease Detection using Machine Learning Model
    IACIDS
    EAI
    DOI: 10.4108/eai.23-11-2023.2343189
Lekha J1,*, Saraswathi S2, Suryaprabha D2, Noel Mathew Thomas1
  • 1: Christ University, Lavasa, Pune
  • 2: Nehru Arts and Science College, Coimbatore
*Contact email: lekha.j@christuniversity.in

Abstract

Agriculture is the primary source of employment for over half of India's population, making it heavily dependent on this sector. Indian farmers encounter a plethora of challenges during their agricultural pursuits, which include but are not limited to droughts, pests, infertile land, lack of irrigation, and plant diseases. As per reliable reports, plant diseases and pests are accountable for crop losses amounting to 5000 crores annually in India, rendering them a significant apprehension for the farming community. Plant disease identification can be a cubersome task and this paper aims to develop a disease identification model for Tomato leaves using three different Machine Learning algorithms, namely Convolutional Neural Network (CNN), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). The primary goal is to evaluate and compare the performance of each algorithm for the identification of Tomato leaf diseases.