论文标题
通过密集连接的卷积神经网络评估水果质量评估
Fruit Quality Assessment with Densely Connected Convolutional Neural Network
论文作者
论文摘要
在农业产业中，准确认识食品以及质量评估至关重要。这样的自动化系统可以加快食品加工部门的车轮并节省大量的手动劳动。在这方面，基于深度学习的架构的最新进步引入了各种各样的解决方案，在多个分类任务中提供了出色的性能。在这项工作中，我们利用了密集连接的卷积神经网络（登录）的概念进行水果质量评估。对更深层的特征传播使网络能够解决消失的梯度问题，并确保重新使用功能以学习有意义的见解。在一个数据集上评估19,526张图像，其中包含六个果实，每个水果都有三个质量等级，提议的管道的准确性为99.67％。该模型的鲁棒性进一步测试了该模型产生类似性能的水果分类和质量评估任务，这使其适合于现实生活中的应用。
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.
