Accurate recognition of food items along with quality assessment is of paramount importance in the agricultural industry. Such automated systems can speed up the wheel of the food processing sector and save tons of manual labor. In this connection, the recent advancement of Deep learning-based architectures has introduced a wide variety of solutions offering remarkable performance in several classification tasks. In this work, we have exploited the concept of Densely Connected Convolutional Neural Networks (DenseNets) for fruit quality assessment. The feature propagation towards the deeper layers has enabled the network to tackle the vanishing gradient problems and ensured the reuse of features to learn meaningful insights. Evaluating on a dataset of 19,526 images containing six fruits having three quality grades for each, the proposed pipeline achieved a remarkable accuracy of 99.67%. The robustness of the model was further tested for fruit classification and quality assessment tasks where the model produced a similar performance, which makes it suitable for real-life applications.
翻译:在农业产业中,对食品的准确认识以及质量评估至关重要,这种自动化系统可以加快食品加工部门的车轮,节省大量体力劳动。在这方面,最近深学习型结构的进步引入了多种解决方案,在几项分类任务中表现显著。我们在工作中利用了 " 连通性紧密神经神经网络 " 的概念来进行水果质量评估。向更深层传播特征使网络能够解决渐渐消失的梯度问题,并确保特征的再利用以了解有意义的洞察力。对包含6种水果的数据集(每个水果有3个质量等级)进行了评估,拟议的管道达到了99.67%的惊人精确度。模型的稳健性进一步测试了水果分类和质量评估任务,模型产生了类似的性能,因此适合实际应用。