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Simple Summary: The aim of this study was to improve the hatching efficiency of duck eggs by automatically assessing the hatching characteristics of early hatching eggs. The timely and accurate detection of fertile and infertile eggs is an important research topic in the breeder egg hatching industry. Detecting infertile eggs early in the hatching process not only improves the hatching efficiency of duck eggs, but also brings benefits to hatching companies. In recent years, the rapid development of deep learning and computer vision technology has brought us new ideas. We propose a lightweight multi-target detection method based on deep learning to evaluate the hatching characteristics of duck eggs. The results show that the method could meet the requirements for accuracy and real-time detection in industrial production. Since it is difficult to accurately identify the fertilization and infertility status of multiple duck eggs on an incubation tray, and due to the lack of easy-to-deploy detection models, a novel lightweight detection architecture (LDA) based on the YOLOX-Tiny framework is proposed in this paper to identify sterile duck eggs with the aim of reducing model deployment requirements and improving detection accuracy. Specifically, the method acquires duck egg images through an acquisition device and augments the dataset using rotation, symmetry, and contrast enhancement methods. Then, the traditional convolution is replaced by a depth-wise separable convolution with a smaller number of parameters, while a new CSP structure and backbone network structure are used to reduce the number of parameters of the model. Finally, to improve the accuracy of the network, the method includes an attention mechanism after the backbone network and uses the cosine annealing algorithm in training. An experiment was conducted on 2111 duck eggs, and 6488 duck egg images were obtained after data augmentation. In the test set of 326 duck egg images, the mean average precision (mAP) of the method in this paper was 99.74%, which was better than the 94.92% of the YOLOX-Tiny network before improvement, and better than the reported prediction accuracy of 92.06%. The number of model parameters was only 1.93 M, which was better than the 5.03 M of the YOLOX-Tiny network. Further, by analyzing the concurrent detection of single 3 × 5, 5 × 7 and 7 × 9 grids, the algorithm achieved a single detection number of 7 × 9 = 63 eggs. The method proposed in this paper significantly improves the efficiency and detection accuracy of single-step detection of breeder duck eggs, reduces the network size, and provides a suitable method for identifying sterile duck eggs on hatching egg trays. Therefore, the method has good application prospects. [ABSTRACT FROM AUTHOR]

• Our proposed model has a small number of parameters while maintaining high accuracy. • The PA-AFF module is used instead of PA-FPN, allowing the model to pay attention to features at different scales. • Our proposed model can initially detect unfertilized eggs as early as the fourth day. Early hatching information detection of duck eggs is a crucial step in the duck egg hatching industry. In this study, we proposed a lightweight deep learning model AFF-YOLOX to detect the hatching information of multiple duck eggs on the standard hatching trays, meeting the speed requirements of real production. Based on YOLOX of networks structure, we added the AFF to the PA-FPN. Additionally, the input size of the image was changed according to the hatching tray and remove the redundant part of the network. The results showed that AFF-YOLOX could significantly improve the detection effect compared with the original YOLOX. The mAP was improved from 74.30% to 97.55%, the number of parameters was reduced from 99 M to 0.49 M, and the latency per image was decreased from 556 ms to 178 ms. AFF-YOLOX can detect abnormal eggs on incubation day 4 and can completely identify all abnormal eggs on incubation day 6. The results demonstrated that the optimization and lightweight methods we are effective and provides a technical reference for automatic detection of hatching information of duck eggs in the hatchery industry. [ABSTRACT FROM AUTHOR]

To address the problem that duck egg mortality is not easily detected at mid-incubation, this paper explored a method to detect mid-incubation egg activity information based on temperature drop curve (TDC) features. In this paper, we used a thermal infrared camera to obtain continuous thermal images of death fertilized duck eggs (DFDE) on the 16th day of incubation and alive fertilized duck eggs (AFDE) hatched for 16–19 days in a 20 °C environment. By observing the temperature drop curve of egg surface, we extracted and visualized five features that could reflect the activity information of duck eggs. And we used K-Nearest Neighbor (KNN), Naive Bayesian (NB) and Support Vector Machine (SVM) to establish the activity information detection models for different incubation days. The results showed that KNN could better distinguish the activity of eggs at the 16th and the 17th day of incubation, with F1-score of 85.43% and 85.98%, respectively. The SVM showed better results at the 18th and the 19th day of incubation, with F1-score of 90.57% and 96.3%, respectively. The experimental results demonstrated that the activity detection method based on the temperature drop curve features in this paper could efficiently and nondestructively detect the activity information of mid-incubation duck eggs, which provided a technical foundation for detecting the activity information of duck eggs at mid-incubation. • We built a collection platform for capturing thermal images of duck eggs. • 5 features extracted from duck eggs' temperature drop curves. • KNN can detect the activity information on the 16th and the 17th incubation day. • SVM can detect the activity information on the 18th and the 19th incubation day. [ABSTRACT FROM AUTHOR]