Improved YOLOv8 Model for Lightweight Pigeon Egg Detection.

In response to the high breakage rate of pigeon eggs and the significant labor costs associated with egg-producing pigeon farming, this study proposes an improved YOLOv8-PG (real versus fake pigeon egg detection) model based on YOLOv8n. Specifically, the Bottleneck in the C2f module of the YOLOv8n backbone network and neck network are replaced with Fasternet-EMA Block and Fasternet Block, respectively. The Fasternet Block is designed based on PConv (Partial Convolution) to reduce model parameter count and computational load efficiently. Furthermore, the incorporation of the EMA (Efficient Multi-scale Attention) mechanism helps mitigate interference from complex environments on pigeon-egg feature-extraction capabilities. Additionally, Dysample, an ultra-lightweight and effective upsampler, is introduced into the neck network to further enhance performance with lower computational overhead. Finally, the EXPMA (exponential moving average) concept is employed to optimize the SlideLoss and propose the EMASlideLoss classification loss function, addressing the issue of imbalanced data samples and enhancing the model's robustness. The experimental results showed that the F1-score, mAP50-95, and mAP75 of YOLOv8-PG increased by 0.76%, 1.56%, and 4.45%, respectively, compared with the baseline YOLOv8n model. Moreover, the model's parameter count and computational load are reduced by 24.69% and 22.89%, respectively. Compared to detection models such as Faster R-CNN, YOLOv5s, YOLOv7, and YOLOv8s, YOLOv8-PG exhibits superior performance. Additionally, the reduction in parameter count and computational load contributes to lowering the model deployment costs and facilitates its implementation on mobile robotic platforms.

Estimation of Weight and Body Measurement Model for Pigs Based on Back Point Cloud Data.

Pig farming is a crucial sector in global animal husbandry. The weight and body dimension data of pigs reflect their growth and development status, serving as vital metrics for assessing their progress. Presently, pig weight and body dimensions are predominantly measured manually, which poses challenges such as difficulties in herding, stress responses in pigs, and the control of zoonotic diseases. To address these issues, this study proposes a non-contact weight estimation and body measurement model based on point cloud data from pig backs. A depth camera was installed above a weighbridge to acquire 3D point cloud data from 258 Yorkshire-Landrace crossbred sows. We selected 200 Yorkshire-Landrace sows as the research subjects and applied point cloud filtering and denoising techniques to their three-dimensional point cloud data. Subsequently, a K-means clustering segmentation algorithm was employed to extract the point cloud corresponding to the pigs' backs. A convolutional neural network with a multi-head attention was established for pig weight prediction and added RGB information as an additional feature. During the data processing process, we also measured the back body size information of the pigs. During the model evaluation, 58 Yorkshire-Landrace sows were specifically selected for experimental assessment. Compared to manual measurements, the weight estimation exhibited an average absolute error of 11.552 kg, average relative error of 4.812%, and root mean square error of 11.181 kg. Specifically, for the MACNN, incorporating RGB information as an additional feature resulted in a decrease of 2.469 kg in the RMSE, a decrease of 0.8% in the MAPE, and a decrease of 1.032 kg in the MAE. Measurements of shoulder width, abdominal width, and hip width yielded corresponding average relative errors of 3.144%, 3.798%, and 3.820%. In conclusion, a convolutional neural network with a multi-head attention was established for pig weight prediction, and incorporating RGB information as an additional feature method demonstrated accuracy and reliability for weight estimation and body dimension measurement.

CAT-CBAM-Net: An Automatic Scoring Method for Sow Body Condition Based on CNN and Transformer.

Sow body condition scoring has been confirmed as a vital procedure in sow management. A timely and accurate assessment of the body condition of a sow is conducive to determining nutritional supply, and it takes on critical significance in enhancing sow reproductive performance. Manual sow body condition scoring methods have been extensively employed in large-scale sow farms, which are time-consuming and labor-intensive. To address the above-mentioned problem, a dual neural network-based automatic scoring method was developed in this study for sow body condition. The developed method aims to enhance the ability to capture local features and global information in sow images by combining CNN and transformer networks. Moreover, it introduces a CBAM module to help the network pay more attention to crucial feature channels while suppressing attention to irrelevant channels. To tackle the problem of imbalanced categories and mislabeling of body condition data, the original loss function was substituted with the optimized focal loss function. As indicated by the model test, the sow body condition classification achieved an average precision of 91.06%, the average recall rate was 91.58%, and the average F1 score reached 91.31%. The comprehensive comparative experimental results suggested that the proposed method yielded optimal performance on this dataset. The method developed in this study is capable of achieving automatic scoring of sow body condition, and it shows broad and promising applications.

Sow Farrowing Early Warning and Supervision for Embedded Board Implementations.

Sow farrowing is an important part of pig breeding. The accurate and effective early warning of sow behaviors in farrowing helps breeders determine whether it is necessary to intervene with the farrowing process in a timely manner and is thus essential for increasing the survival rate of piglets and the profits of pig farms. For large pig farms, human resources and costs are important considerations in farrowing supervision. The existing method, which uses cloud computing-based deep learning to supervise sow farrowing, has a high equipment cost and requires uploading all data to a cloud data center, requiring a large network bandwidth. Thus, this paper proposes an approach for the early warning and supervision of farrowing behaviors based on the embedded artificial-intelligence computing platform (NVIDIA Jetson Nano). This lightweight deep learning method allows the rapid processing of sow farrowing video data at edge nodes, reducing the bandwidth requirement and ensuring data security in the network transmission. Experiments indicated that after the model was migrated to the Jetson Nano, its precision of sow postures and newborn piglets detection was 93.5%, with a recall rate of 92.2%, and the detection speed was increased by a factor larger than 8. The early warning of 18 approaching farrowing (5 h) sows were tested. The mean error of warning was 1.02 h.

Light from heat lamps affects sow behaviour and piglet salivary melatonin levels.

The light environment regulates animal physiology and behaviour. As widely used supplementary heat sources in creep areas, the effect of visible light radiated by infrared heat lamps on pigs is worth investigating. To investigate the effects of light from heat lamps on the behaviour of sows and piglets and possible endocrine mechanisms, 24 primiparous sows were randomly assigned to three supplementary heat source treatments: (1) 250 W non-luminous ceramic heat lamps (CE, n = 8), (2) 175 W red heat lamps (RL, n = 8), and (3) 175 W transparent heat lamps (TL, n = 8). All heat lamps were turned off on Day 15 postpartum. Piglets were weighed on days 3 and 21 postpartum. The number and duration of suckling within 24 h were analysed via video recordings on days 4, 8, and 16 postpartum. Sow posture changes during the day and night were detected using the YOLOv4 target detection network model. One marked piglet from six litters randomly selected from each treatment was used for saliva collection. Saliva samples were collected at 0800, 1400, 2000, and 0200 (+1 d) on days 10 and 20 postpartum. The results showed that the mean postural change frequency of TL sows was higher than that of CE sows (P < 0.05), while that of RL sows was not different from that of CE and TL sows. However, the duration of the sows being in each posture was not affected by the treatment. The total suckling duration of TL piglets was significantly longer than that of CE piglets, but there was no significant difference in the performance of the piglets. The melatonin concentrations in the saliva of piglets at 10 and 20 days of age in the three treatments showed different diurnal rhythms, but there was no significant difference in the levels of melatonin in TL piglets between night and day. Differences in salivary cortisol levels only appeared between the CE and RL groups at 20 days of age. Based on the present results, the illuminance and spectrum of the transparent heat lamps were sufficient to stimulate sow activity and inhibit melatonin levels in piglets. However, the stimulating effect on suckling was not sufficient to significantly improve the performance of piglets. Exposure to red heat lamps, rather than ceramic lamps, resulted in the strongest circadian rhythm of salivary melatonin in piglets.

Effects of heat stress on posture transitions and reproductive performance of primiparous sows during late gestation.

This study aimed to investigate the effects of heat stress on posture transitions of perinatal primiparous sows around the parturition period from 72 h prepartum to 24 h postpartum. The reproductive performance of sows was measured, and the relationship between posture transitions and reproductive performance was also analyzed. Ten primiparous sows were randomly assigned to thermoneutral (TN) (18-22 °C; n = 5) or heat stress (HS) (28-32 °C; n = 5) treatments. Posture transitioning, including the frequency of posture change, duration of dynamic posture (DP), and lateral lying with udder to the piglet creep box (PCB) during three periods (72 h prepartum, sub-partum, and 24 h postpartum, respectively), were recorded. Posture change frequency was significantly increased, starting from 24 h prepartum to the onset of farrowing in both the TN (P < 0.05) and HS (P < 0.01) groups. Moreover, the peak value of posture change frequency in the TN group was concentrated during the 12 h prepartum period, positively correlated with the quantities of head-first birth piglets and sub-partum duration, respectively. DP duration increased during the period of 24 h prepartum and then decreased sharply (P < 0.001 and P < 0.05 for TN and HS groups, respectively). The duration of facing the udder to the PCB increased during sub-partum and postpartum TN (P < 0.001). The duration of sub-partum (P < 0.05) and delivery time of single piglets (P < 0.01) in the HS group was prolonged, and piglets from the HS group had a lower weight gain than the TN group both at d10 (P < 0.001) and weaning time (P < 0.001). In conclusion, HS had obvious adverse effects on nursery behavior and reproductive abilities in perinatal primiparous sows, which resulted in poor growth performance of lactating piglets.