Search

The epidemic of avian influenza outbreaks, especially high-pathogenicity avian influenza (HPAI), which causes respiratory disease and death, is a disaster in poultry. The outbreak of HPAI in 2014–2015 caused the loss of 60 million chickens and turkeys. The most recent HPAI outbreak, ongoing since 2021, has led to the loss of over 50 million chickens so far in the US and Canada. Farm biosecurity management practices have been used to prevent the spread of the virus. However, existing practices related to controlling the transmission of the virus through wild birds, especially waterfowl, are limited. For instance, ducks were considered hosts of avian influenza viruses in many past outbreaks. The objectives of this study were to develop a machine vision framework for tracking wild birds and test the performance of deep learning models in the detection of wild birds on poultry farms. A deep learning framework based on computer vision was designed and applied to the monitoring of wild birds. A night vision camera was used to collect data on wild bird near poultry farms. In the data, there were two main wild birds: the gadwall and brown thrasher. More than 6000 pictures were extracted through random video selection and applied in the training and testing processes. An overall precision of 0.95 (mAP@0.5) was reached by the model. The model is capable of automatic and real-time detection of wild birds. Missed detection mainly came from occlusion because the wild birds tended to hide in grass. Future research could be focused on applying the model to alert to the risk of wild birds and combining it with unmanned aerial vehicles to drive out detected wild birds. [ABSTRACT FROM AUTHOR]

Seed germination is crucial for plant survival, crop stand establishment, and achieving optimal grain yield. The main objective of this review is to explore the physiological and molecular mechanisms governing rice seed germination under aerobic (water stress) and anaerobic (hypoxic) conditions in direct-seeded rice (DSR) systems. Moreover, it discusses the recent genomic advancements and innovations to improve rice seed germination. Here, we discuss how coleoptile and mesocotyl elongation plays a vital role in anaerobic germination (AG) and the function of raised antioxidants, including superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) in maintaining Reactive Oxygen Species (ROS), and malondialdehyde (MDA) homeostasis for stabilizing seed germination in water-scarce conditions. This study comprehensively highlights the functions and dynamics of phytohormones—GA (gibberellic acid) and ABA (abscisic acid)—key regulatory genes, transcription factors (TFs), key proteins, and regulatory metabolic pathways, including glycolysis, the pentose phosphate pathway (PPP), and the tricarboxylic acid cycle (TCA), in regulating seed germination under both conditions. Conventional agronomic and cultural practices, such as seed selection, seed priming, seed coating, and hardening, have proven to improve seed germination. Moreover, the utilization of molecular and novel approaches—such as clustered regularly interspaced short palindromic repeat (CRISPR-Cas9) mediated genome editing, marker-assisted selection (MAS), genome-wide associations studies (GWAS), single nucleotide polymorphisms (SNPs), multi-omics, RNA sequencing—combined with beneficial quantitative trait loci (QTLs) has expanded knowledge of crop genomics and inheritance. These advancements aid the development of specific traits for enhancing seed germination in DSR. [ABSTRACT FROM AUTHOR]