Detection and Classification of Disease from Mango fruit using Convolutional Recurrent Neural Network with Metaheruistic Optimizer
Keywords:
Mango fruit, optimization, background removal, instance segmentation, CNN, disease classificationAbstract
Fruits play a vital role in providing essential nutrients to sustain goodhuman health. The significant reduction in crop output is mostly attributed to the substantial impact of fruit diseases, which arise as a consequence of inadequate maintenance practices and the proliferation of fungal pathogens. The mango fruit has significant global consumption and is vulnerable to illnesses that may impact both its quality and quantity. The process of manual inspection is characterized by its arduous nature, extensive time requirements, heavy reliance on human labor, and lack of efficiency. This study employs an image classification methodology to discern several illnesses present in mangoes and distinguish them from the unaffected specimens. The preprocessing phase consists of two primary stages, namely background removal and contrast enhancement. The method of histogram equalization involves enhancing the contrast of animage. Following the preprocessing stage, the subsequent step involves the use of instance segmentation, which serves as a significant operation. The radiomic characteristics that have been retrieved are inputted into the CNN_FOA, a Convolutional Recurrent Neural Network with a classifier based on the FireFly Optimizer. This CNN_FOA is used to classify the mango images into different categories. The suggested model has undergone experimental verification and validation, yielding optimal outcomes with a precision rate of 97%.
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