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Fruit Quality Assessment with Densely Connected Convolutional Neural Network

Md. Samin MorshedSabbir AhmedTasnim AhmedMuhammad Usama IslamA. B. M. Ashikur Rahman
Dec 2022
摘要
Accurate recognition of food items along with quality assessment is ofparamount importance in the agricultural industry. Such automated systems canspeed up the wheel of the food processing sector and save tons of manual labor.In this connection, the recent advancement of Deep learning-based architectureshas introduced a wide variety of solutions offering remarkable performance inseveral classification tasks. In this work, we have exploited the concept ofDensely Connected Convolutional Neural Networks (DenseNets) for fruit qualityassessment. The feature propagation towards the deeper layers has enabled thenetwork to tackle the vanishing gradient problems and ensured the reuse offeatures to learn meaningful insights. Evaluating on a dataset of 19,526 imagescontaining six fruits having three quality grades for each, the proposedpipeline achieved a remarkable accuracy of 99.67%. The robustness of the modelwas further tested for fruit classification and quality assessment tasks wherethe model produced a similar performance, which makes it suitable for real-lifeapplications.
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