Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Tehran, Iran.
Department of Biosystems Engineering and Soil Science, University of Tennessee, Knoxville, TN, USA.
J Food Sci. 2022 Jan;87(1):289-301. doi: 10.1111/1750-3841.15995. Epub 2021 Dec 23.
Homogeneity of appearance attributes of bell peppers is essential for consumers and food industries. This research aimed to develop an in-line sorting system using a deep convolutional neural network (DCNN) which is considered the state-of-the-art in the field of machine vision-based classifications, for grading bell peppers into five classes. According to export standards, the crop should be graded based on maturity stage and size. For that, the fully connected layer in the ResNet50 architecture of DCNN was replaced with a developed classifier block, including a global average-pooling layer, dense layers, batch normalization, and dropout layer. The developed model was trained and evaluated through the five-fold cross-validation method. The required processing time to classify each sample in the proposed model was estimated as 4 ms which is fast enough for real-time applications. Accordingly, the DCNN model was integrated with a machine vision-based designed sorting machine. Then, the developed system was evaluated in the in-line phase. The performance parameters in the in-line phase include accuracy, precision, sensitivity, specificity, F1-score, and overall accuracies were 98.7%, 97%, 96.9%, 99%, 96.9%, and 96.9%, respectively. The total rate of sorting the bell pepper was also measured as approximately 3000 sample/h with one sorting line. The proposed sorting system demonstrates a very good capability that allows it to be used in industrial applications. PRACTICAL APPLICATION: A developed intelligent model was integrated with a machine vision-based designed sorting machine for bell peppers. The developed system can sort the crop according to export criteria with an accuracy of 96.9%. The proposed sorting system demonstrated a very good capability that allows it to be used in industrial applications.
辣椒外观属性的一致性对消费者和食品工业至关重要。本研究旨在开发一种使用深度卷积神经网络（DCNN）的在线分拣系统，该系统被认为是机器视觉分类领域的最新技术，可将辣椒分为五类。根据出口标准，作物应根据成熟阶段和大小进行分级。为此，DCNN 的 ResNet50 架构中的全连接层被一个开发的分类器块取代，该分类器块包括全局平均池化层、密集层、批量归一化和 dropout 层。开发的模型通过五折交叉验证方法进行训练和评估。在提出的模型中对每个样本进行分类所需的处理时间估计为 4 毫秒，足以满足实时应用的要求。因此，DCNN 模型与基于机器视觉设计的分拣机集成在一起。然后，在在线阶段评估开发的系统。在线阶段的性能参数包括准确率、精度、灵敏度、特异性、F1 分数和总体准确率分别为 98.7%、97%、96.9%、99%、96.9%和 96.9%。使用一条分拣线，分拣辣椒的总速率约为 3000 个样本/小时。所提出的分拣系统具有非常好的性能，可以在工业应用中使用。实际应用：将开发的智能模型与基于机器视觉设计的辣椒分拣机集成在一起。开发的系统可以根据出口标准以 96.9%的准确率对作物进行分拣。所提出的分拣系统具有非常好的性能，可以在工业应用中使用。
