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Deep learning detector for pests and plant disease recognition

Ileladewa, Oluwatimilehin Adekunle (2020) Deep learning detector for pests and plant disease recognition. Final Year Project, UTAR.

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    Pests and diseases in plants have been a major challenge factor in the agricultural field. And developing a quick and accurate model could help in detecting pests and diseases in plants. Meanwhile, evolution in deep convolutional neural networks for image classification has rapidly improved the accuracy of object detection, classification and system recognition. However, in this project, deep learning techniques are used in developing a model for diseases and pest detection in plants, and then train and test the model before eventually integrating the model into a mobile application. The goal of this project is to develop a framework that can classify the class of a plant, and detect areas of the plant leaf already affected by diseases, and eventually deploy the framework on a mobile application. In order to find a suitable meta-architecture for the aim of the project, we use the combination of Single Shot MultiBox Detector and MobileNet (SSD MobileNet) where Single Shot MultiBox Detector (SSD) is the algorithm that takes a single shot to detect multiple objects within an image, and mobilenet is a neural network for recognition and classification. The system is trained using a large dataset containing different classes of both diseased and healthy images of plants from PlantVillage. Final results of the project reveal that our proposed system can recognize and detect various type of pests and diseases that have been trained in the model, with the ability to handle the complexity of a plant’s surrounding area.

    Item Type: Final Year Project / Dissertation / Thesis (Final Year Project)
    Subjects: Q Science > Q Science (General)
    Divisions: Faculty of Information and Communication Technology > Bachelor of Computer Science (Hons)
    Depositing User: ML Main Library
    Date Deposited: 07 Jan 2021 20:43
    Last Modified: 07 Jan 2021 20:43
    URI: http://eprints.utar.edu.my/id/eprint/3953

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