Research note: A method for recognizing and evaluating typical behaviors of laying hens in a thermal environment.

Automatically identifying abnormal behaviors of caged laying hens in a thermal environment improves manual management efficiency. It also provides reference indicators for breeding heat-tolerant hens. In this study, we propose a deep learning-based method for automatic recognition and evaluation of typical heat stress behaviors in hens. We developed a lightweight object detection algorithm, YOLO-HGP, based on the YOLOv8n as the baseline model. YOLO-HGP achieves Precision (P), Recall (R), and mean average precision (mAP) of 95.952%, 94.127%, and 97.667%, respectively, effectively detecting typical heat stress behaviors in hens. Compared to the original YOLO v8n, YOLO-HGP improves R, and mAP by 6.257%, and 1.963%, respectively. The FLOPs (floating point operations) and parameter count of YOLO-HGP are 4.3G and 1.729M, reducing by 47.56% and 42.58% compared to the original model. Additionally, we introduce the "ORC-ratio" (The ratio of the combined frequency of open-beak breathing and retching behaviors to the frequency of closed-beak behaviors.) as an evaluation indicator for the frequency of typical heat stress behaviors in hens and combine it with the Hybrid-SORT multiobject tracking algorithm to achieve tracking detection of individual hens. The study demonstrates that the proposed model effectively identifies and quantitatively evaluates typical behaviors of hens in a thermal environment, providing an effective approach for the automated recognition of heat stress behaviors in hens.

FOXL2 regulates RhoA expression to change actin cytoskeleton rearrangement in granulosa cells of chicken pre-ovulatory follicles†.

Forkhead box L2 (FOXL2) is an indispensable key regulator of female follicular development, and it plays important roles in the morphogenesis, proliferation, and differentiation of follicle granulosa cells, such as establishing normal estradiol signaling and regulating steroid hormone synthesis. Nevertheless, the effects of FOXL2 on granulosa cell morphology and the underlying mechanism remain unknown. Using FOXL2 ChIP-seq analysis, we found that FOXL2 target genes were significantly enriched in the actin cytoskeleton-related pathways. We confirmed that FOXL2 inhibited the expression of RhoA, a key gene for actin cytoskeleton rearrangement, by binding to TCATCCATCTCT in RhoA promoter region. In addition, FOXL2 overexpression in granulosa cells induced the depolymerization of F-actin and disordered the actin filaments, resulting in a slowdown in the expansion of granulosa cells, while FOXL2 silencing inhibited F-actin depolymerization and stabilized the actin filaments, thereby accelerating granulosa cell expansion. RhoA/ROCK pathway inhibitor Y-27632 exhibited similar effects to FOXL2 overexpression, even reversed the actin polymerization in FOXL2 silencing granulosa cells. This study revealed for the first time that FOXL2 regulated granulosa cell actin cytoskeleton by RhoA/ROCK pathway, thus affecting granulosa cell expansion. Our findings provide new insights for constructing the regulatory network of FOXL2 and propose a potential mechanism for facilitating rapid follicle expansion, thereby laying a foundation for further understanding follicular development.

Enhancing ion mobility spectrometry performance through a programmable ion swarm shaping method based on Bradbury-Nielsen gates.

The ion gate is a critical element in drift tube ion mobility spectrometry (IMS) as it directly influences the resolving power and sensitivity of the system. However, the conventional Bradbury-Nielsen gate (BNG) often leads to deformation of the ion swarm shape, resulting in reduced resolving power and significant discrimination effects. To address these limitations, we propose a novel method that incorporates a cutting phase following the gate opening. This approach effectively reduces trailing edge deformation, resulting in a maximum resolving power of over 100 and increased signal intensity. Additionally, this method maintains high resolving power even during longer gate opening times. Remarkably, this method not only significantly reduces the mobility discrimination effect but also enables the achievement of reverse discrimination by adjusting the duration of the cutting phase. Consequently, it demonstrates the potential to selectively amplify the peak height of target ions. Our method offers straightforward implementation across all IMS systems utilizing the BNG, thereby significantly improving system performance.

A longan yield estimation approach based on UAV images and deep learning.

Longan yield estimation is an important practice before longan harvests. Statistical longan yield data can provide an important reference for market pricing and improving harvest efficiency and can directly determine the economic benefits of longan orchards. At present, the statistical work concerning longan yields requires high labor costs. Aiming at the task of longan yield estimation, combined with deep learning and regression analysis technology, this study proposed a method to calculate longan yield in complex natural environment. First, a UAV was used to collect video images of a longan canopy at the mature stage. Second, the CF-YD model and SF-YD model were constructed to identify Cluster_Fruits and Single_Fruits, respectively, realizing the task of automatically identifying the number of targets directly from images. Finally, according to the sample data collected from real orchards, a regression analysis was carried out on the target quantity detected by the model and the real target quantity, and estimation models were constructed for determining the Cluster_Fruits on a single longan tree and the Single_Fruits on a single Cluster_Fruit. Then, an error analysis was conducted on the data obtained from the manual counting process and the estimation model, and the average error rate regarding the number of Cluster_Fruits was 2.66%, while the average error rate regarding the number of Single_Fruits was 2.99%. The results show that the method proposed in this paper is effective at estimating longan yields and can provide guidance for improving the efficiency of longan fruit harvests.

A novel approach for estimating the flowering rate of litchi based on deep learning and UAV images.

Litchi flowering management is an important link in litchi orchard management. Statistical litchi flowering rate data can provide an important reference for regulating the number of litchi flowers and directly determining the quality and yield of litchi fruit. At present, the statistical work regarding litchi flowering rates requires considerable labour costs. Therefore, this study aims at the statistical litchi flowering rate task, and a combination of unmanned aerial vehicle (UAV) images and computer vision technology is proposed to count the numbers of litchi flower clusters and flushes in a complex natural environment to improve the efficiency of litchi flowering rate estimation. First, RGB images of litchi canopies at the flowering stage are collected by a UAV. After performing image preprocessing, a dataset is established, and two types of objects in the images, namely, flower clusters and flushes, are manually labelled. Second, by comparing the pretraining and testing results obtained when setting different training parameters for the YOLOv4 model, the optimal parameter combination is determined. The YOLOv4 model trained with the optimal combination of parameters tests best on the test set, at which time the mean average precision (mAP) is 87.87%. The detection time required for a single image is 0.043 s. Finally, aiming at the two kinds of targets (flower clusters and flushes) on 8 litchi trees in a real orchard, a model for estimating the numbers of flower clusters and flushes on a single litchi tree is constructed by matching the identified number of targets with the actual number of targets via equation fitting. Then, the data obtained from the manual counting process and the estimation model for the other five litchi trees in the real orchard are statistically analysed. The average error rate for the number of flower clusters is 4.20%, the average error rate for the number of flushes is 2.85%, and the average error for the flowering rate is 1.135%. The experimental results show that the proposed method is effective for estimating the litchi flowering rate and can provide guidance regarding the management of the flowering periods of litchi orchards.