Analysis of pig posture detection in group-housed pigs using deep learning-based mask scoring instance segmentation.

Pig posture is closely linked with livestock health and welfare. There has been significant interest among researchers in using deep learning techniques for pig posture detection. However, this task is challenging due to variations in image angles and times, as well as the presence of multiple pigs in a single image. In this study, we explore an object detection and segmentation algorithm based on instance segmentation scoring to detect different pig postures (sternal lying, lateral lying, walking, and sitting) and segment pig areas in group images, thereby enabling the identification of individual pig postures within a group. The algorithm combines a residual network with 50 layers and a feature pyramid network to extract feature maps from input images. These feature maps are then used to generate regions of interest (RoI) using a region candidate network. For each RoI, the algorithm performs regression to determine the location, classification, and segmentation of each pig posture. To address challenges such as missing targets and error detections among overlapping pigs in group housing, non-maximum suppression (NMS) is used with a threshold of 0.7. Through extensive hyperparameter analysis, a learning rate of 0.01, a batch size of 512, and 4 images per batch offer superior performance, with accuracy surpassing 96%. Similarly, the mean average precision (mAP) exceeds 83% for object detection and instance segmentation under these settings. Additionally, we compare the method with the faster R-CNN object detection model. Further, execution times on different processing units considering various hyperparameters and iterations have been analyzed.

Ovarian follicle transcriptome dynamics reveals enrichment of immune system process during transition from small to large follicles in cyclic Indian Ghoongroo pigs.

Ovarian follicular development is a critical determinant of reproductive performance in litter bearing species like pigs, wherein economic gains depend on litter size. The study aimed to gain insight into the differentially expressed genes (DEGs) and signalling pathways regulating follicular growth and maturation in Ghoongroo pigs. Transcriptome profiling of porcine small follicles (SF) and large follicles (LF) was conducted using NovaSeq600 sequencing platform and DEGs were identified using DESeq2 with threshold of Padj. < 0.05 and log2 fold change cut off 0.58 (LF vs. SF). Functional annotations and bioinformatics analysis of DEGs were performed to find out biological functions, signalling pathways and hub genes regulating follicular dynamics. Transcriptome analysis revealed 709 and 479 genes unique to SF and LF stages, respectively, and 11,993 co-expressed genes in both the groups. In total, 507 DEGs (284 upregulated and 223 downregulated) were identified, which encoded for diverse proteins including transcription factors (TFs). These DEGs were functionally linked to response to stimulus, lipid metabolic process, developmental process, extracellular matrix organisation along with the immune system process, indicating wide-ranging mechanisms associated with follicular transition. The enriched KEGG pathways in LF stage consisted of ovarian steroidogenesis, cholesterol and retinol metabolism, cell adhesion molecules, cytokine receptor interaction and immune signalling pathways, depicting intra-follicular control of varied ovarian function. The hub gene analysis revealed APOE, SCARB1, MMP9, CYP17A1, TYROBP as key regulators of follicular development. This study identified candidate genes and TFs providing steroidogenic advantage to LFs which makes them fit for selection into the ovulatory pool of follicles.