- The primary objective of this research work is to find coconut palm disease and coconut maturity, with the objective of improving the quality of the product yield. It is difficult for a farmer to manually monitor coconut palm disease and maturity, which can take an extended period. Coconut palm diseases include seeding disease, white fungal disease, and spotting disease. The proposed system aids in the identification of coconut palm disease and coconut maturity, as well as providing remedies for the disease and determining coconut maturity, which is the project's primary objective. Currently, the solutions are found through image-processing techniques involving the identification of coconut maturity stages. In fact, a better convolutional neural network model for identifying the mature and immature phases of coconut maturity is suggested. Due of the ecological intricacy and the resemblance between coconuts and their backdrops, it might be difficult to determine when coconuts are ready to be harvested without the help of a human. To solve the problem, a CNN model with RESNET architecture was employed for coconut maturity prediction, and RESNET architecture was used for coconut maturity analysis. The final output will be easily obtained by using these architectures.


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    Title :

    Coconut Maturity Detection System Using CNN Model with ResNet50 Architecture


    Contributors:


    Publication date :

    2023-11-22


    Size :

    733506 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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