Abstract
Abstract: Tomato quality assessment is a critical task in the agricultural sedulity, impacting product, distribution, and consumer satisfaction. This study presents a new approach for the quality assessment of tomato fruits using deep knowledge ways. By employing convolutional neural networks(CNNs) and a dataset of annotated tomato images, we have developed a robust model suitable of assessing various quality parameters, including size, color, shape, and scars. The deep knowledge model offers high delicacy and effectiveness in distinguishing between decoration and sour tomatoes, furnishing precious perceptivity for farmers and stakeholders throughout the force chain. Our findings demonstrate the eventuality of deep knowledge in automating and optimizing tomato quality assessment processes, thus enhancing overall productivity and consumer experience.
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