In response to the market regulation requirements for detecting adulterated egg white powder, based on the near-infrared spectroscopy (NIRS) data of pure and adulterated egg white powder samples with varying adulterant types and concentrations, this study constructed a dual model for the identification and quantitative prediction of adulterants using an improved one-dimensional convolutional neural network (1D-CNN). The qualitative model, which required no spectral preprocessing, exhibited accuracy rates (A_AR) of 98.19%, 99.38%, and 94.79% for bulking agents, nitrogen-rich compounds, and mixed adulterants, respectively. The overall A_AR reached 98.11%, with the lowest recognition concentrations (L_LRC) of 1%, 1%, and 5% for the three types of adulterants, respectively, and an average time spent (A_ATS) of 0.0177 s. For the quantitative model, detrending (DT) was used for spectral preprocessing to predict the concentration of bulking agents, while multiplicative scatter correction (MSC) was used for the concentration prediction of nitrogen-rich compounds and mixed adulterants. The determination coefficient of prediction (R_p²) of all three test sets exceeded 0.9, and the residual predictive deviation (RPD) was above 2.5, meeting the requirements of market regulation. The dual detection model provides key technical support for the development of portable near-infrared spectroscopy-based detectors.

Robot technology has been gradually applied in modern agriculture in recent years. Among them, a duck egg is one of the most important agricultural products during food processing. However, the current collection of duck eggs can usually require a large amount of manual labor, leading to the time-consuming and labor-intensive task. A smart robot has been developed to automatically collect the duck eggs, in order to improve the production efficiency for the harvesting cost-saving. Specifically, an important technical challenge of harvesting robots can be the rapid and accurate identification and positioning of duck eggs, especially under complex environments, such as occlusion, crowding, and darkness. In this study, duck egg detection was proposed for complex environments using an improved YOLOv7 model. A convolutional attention module (CBAM) was added to the backbone network. The network information transmission was enhanced for better sensitivity to the specific features, while the interference of complex environments was reduced on the duck egg recognition. At the same time, the depth-wise separable convolution (DSC) was utilized to adjust the spatial pyramid pooling (SPP), in order to reduce the number of model parameters and operation costs. A high accuracy was achieved to identify and locate the duck eggs under complex environments, thus providing technical support for the development of harvesting robots. Some materials (such as feathers, straw, and sediment) were also used to simulate the complex environment of the duck house. A duck egg image collection platform was then constructed to evaluate the accuracy and environmental adaptability of the model. Actual duck eggs were photographed in the duck house of Wuhan Yujia Bay Duck Farm using an Honor HLK-AL10 camera for both simulated and actual conditions. The duck egg image data was collected, including the multiple angles, positions, different distances and occlusion forms. A total of 2 600 JPG format images were divided into the training set (1 560 images), validation set (520 images), and test set (520 images), according to a 6:2:2 ratio. At the same time, data augmentation was performed on the training set, in order to improve the robustness and generalization ability of the model. The following procedures were used: 1) To add 12% Gaussian noise. 2) To add 2.5% salt and pepper noise. 3) To set the image gains a=0.3 and a=0.5 to change image brightness. After that, the improved YOLOv7 model was trained with an iteration number of 150. Test results show that the improved YOLOv7 model increased the F1 score by 8.3, 10.1, 8.7, and 7.6 percentage points, respectively, and the F1 score reached 95.5%, compared with the common detection models, such as SSD, YOLOv4, YOLOv5_M, and YOLOv7. The occupied memory space was only 68.7 M, while the average time was 0.022 s for the single-image detection, and the average precision value (mAP, Mean Average Precision) was 85.2%. There were no missed or false detections for the feather occlusion or clustering of duck eggs, with an average confidence level of 93.6% and 85.7%, respectively. The more accurate detection was achieved in the improved YOLOv7 model under the complex environments, indicating superior performance.

An improved one-dimensional convolutional neural network (1D-CNN) model for the authenticity detection of egg white powder was constructed based on near-infrared spectroscopy (NIRS). This model required no spectral preprocessing. To enhance its ability to extract spectral features, an efficient channel attention module (ECA) and a one-dimensional global average pooling (1D-GAP) layer were singly or together incorporated into the model, consequently reducing noise interference. The experimental results indicated that the improved model integrating ECA and 1D-GAP, EG-1D-CNN, could distinguish between authentic and adulterated egg white powder samples, with a detection rate of 97.80% for adulterated samples and an overall accuracy rate (A_AR) of 98.93%. The lowest recognition concentrations (L_LRC) for single adulterants such as starch, soy protein isolate, melamine, urea, and glycine were 1%, 5%, 0.1%, 1%, and 5%, respectively, and those for multiple adulterants ranged from 0.1% to 1%. The average time spent (A_ATS) for the detection was 0.0044 seconds. Compared with traditional 1D-CNN network structure and other improved algorithms, the EG-1D-CNN model exhibited higher accuracy, faster detection speed, and smaller model footprint, thus making it more suitable for deployment on embedded devices. This research provides a theoretical foundation for the development of portable near-infrared spectroscopy-based detectors for egg powder quality testing.