The rapid growth of AI in poultry farming has highlighted the challenge of efficiently labeling large, diverse datasets. Manual annotation is time-consuming, making it impractical for modern systems that continuously generate data. This study explores semi-supervised auto-labeling methods, integrating active learning, and prompt-then-detect paradigm to develop an efficient framework for auto-labeling of large poultry datasets aimed at advancing AI-driven behavior and health monitoring. Viideo data were collected from broilers and laying hens housed at the University of Arkansas and the University of Georgia. The collected videos were converted into images, pre-processed, augmented, and labeled. Various machine learning models, including zero-shot models like Grounding DINO, YOLO-World, and CLIP, and supervised models like YOLO and Faster-RCNN, were utilized for broilers, hens, and behavior detection. The results showed that YOLOv8s-World and YOLOv9s performed better when compared performance metrics for broiler and hen detection under supervised learning, while among the semi-supervised model, YOLOv8s-ALPD achieved the highest precision (96.1%) and recall (99.0%) with an RMSE of 1.9. The hybrid YOLO-World model, incorporating the optimal YOLOv8s backbone, demonstrated the highest overall performance. It achieved a precision of 99.2%, recall of 99.4%, and an F1 score of 98.7% for breed detection, alongside a precision of 88.4%, recall of 83.1%, and an F1 score of 84.5% for individual behavior detection. Additionally, semi-supervised models showed significant improvements in behavior detection, achieving up to 31% improvement in precision and 16% in F1-score. The semi-supervised models with minimal active learning reduced annotation time by over 80% compared to full manual labeling. Moreover, integrating zero-shot models enhanced detection and behavior identification.

Address matching is an important task for many businesses especially delivery and take out companies which help them to take out a certain address from their data warehouse. Existing solution uses similarity of strings, and edit distance algorithms to find out the similar addresses from the address database, but these algorithms could not work effectively with redundant, unstructured, or incomplete address data. This paper discuss semantic Address matching technique, by which we can find out a particular address from a list of possible addresses. We have also reviewed existing practices and their shortcoming. Semantic address matching is an essentially NLP task in the field of deep learning. Through this technique We have the ability to triumph the drawbacks of existing methods like redundant or abbreviated data problems. The solution uses the OCR on invoices to extract the address and create the data pool of addresses. Then this data is fed to the algorithm BM-25 for scoring the best matching entries. Then to observe the best result, this will pass through BERT for giving the best possible result from the similar queries. Our investigation exhibits that our methodology enormously improves both accuracy and review of cutting-edge technology existing techniques.

SPRINGDALE, Ark.––Deboning livestock and slicing up chickens has long been hands-on labor. Low-paid workers using knives and saws work on carcasses moving steadily down production lines. It is labor-intensive and dangerous work. Those factory floors have been especially conducive to spreading coronavirus. In April and May, more than 17,300 meat and poultry processing workers in 29 states were infected and 91 died, according to the U.S. Centers for Disease Control and Prevention.

The grocery story business is beginning to use AI to try to gain a competitive edge. Salt Lake City-based Associated Food Stores (AFS), for example, has 500 stores in the western and southwestern US. It found itself dealing with a growing number of SKUs that stores managers were having difficulty tracking and prioritizing, according to an account in ChainStoreAge. AFS began using an AI solution from CB4 to analyze point of sale data, to identify when physical issues in a store are hold back sales. These could be products not easily visible and out of stock conditions.

The food industry has been taking heat from consumers and critics who are demanding healthier ingredients, transparency about where their meals come from and better treatment of animals. There is also a growing awareness of the harmful effect that food production can have on the environment. Now big food companies and entrepreneurs are taking advantage of advances in robotics and data science to meet those challenges--and the trend will likely continue as technology improves, and natural ingredients become easier to cultivate. It also helps that venture capitalists are flocking to the companies cooking up these innovations. This year is on pace to set a record for this decade for venture investment in food technology, according to the PitchBook Platform data provider.

Being able to see is a major frontier in robotics and automation--crossing it is key to autonomous vehicles that can navigate obstacles, humanoid robots that can more closely integrate with humans and drones that can fly more safely. Companies world-wide are investing in computer vision-based technology. Chip maker Intel Corp. bought Mobileye NV for $15.3 billion in March 2017, in part for the Israeli company's vision-based driver-assistance technology. In April, Chinese e-commerce giant Alibaba Group Holding Ltd. led a $600 million funding round in startup SenseTime Group Ltd., which specializes in facial- and image-recognition technology. The sensing and imaging market will grow about 10-fold to $18.5 billion by 2023, market-research firm Yole Développement forecasts.