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    PLANT LEAF DISEASE DETECTION USING DEEP LEARNING FOR SEGMENTATION FEATURE EXTRACTION AND CLASSIFICATION

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    Phd Thesis (10.70Mb)
    Date
    2024-07
    Author
    PARASHAR, NIDHI
    Dr. Prashant Johri, Supervisor
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    Abstract
    Timely detection of plant diseases is vital for social and global benefits because it ensures food security by preventing significant crop losses, thus maintaining stable food supplies for a growing population. Early disease management supports economic stability for farmers by avoiding devastating financial setbacks. It also promotes environmental sustainability by reducing the need for extensive chemical treatments, conserving natural resources, and protecting ecosystems. Overall, timely plant disease detection is crucial for fostering a resilient, sustainable, and healthy global community. The research question is to find ways to improve the effectiveness and accuracy of plant leaf disease detection using recent deep learning approaches emphasizing effective segmentation, feature extraction, and classification techniques. The primary research objective is to assess modern deep learning models to be effectively utilized for the timely and accurate detection of plant diseases to enhance crop production. To answer the research question, convolutional neural networks (CNNs) are employed for classifying diseases across multiple crops, including cotton, apples, and maize for more generalized solution. This study aims to provide an in-depth investigation of cutting-edge plant leaf disease detection models, including their approach, strengths, constraints, and essential computational strategies such as segmentation, feature extraction, and classification. By training and evaluating multiple pre-trained CNN models, the study assesses their ability in identifying and classifying plant diseases from leaf images, ultimately providing valuable insights into how these models can be used to improve plant disease detection approaches. The study trained several pre-trained CNN architectures with fine-tuning on cotton disease dataset and shows the effectiveness of MobileNet-V2 and Inception-V3 models in terms of accuracy, precision, F1-score and recall.
    URI
    http://10.10.11.6/handle/1/20819
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