Abstract:
Gracilaria tenuisfipitata var. Liui, a red seaweed cultivated at Cox’s Bazar, Bangladesh, is utilized as a human diet supplement and for industrial applications. This study evaluates its potential to reduce enteric methane (CH4) emissions in the cattle industry. Materials and Methods: A feeding trial was conducted at Pranisheba R&D cattle farm, Savar, Dhaka. The methane levels were monitored using a sensor-based IoT device. Clinical parameters like feed intake, body temperature, and feces consistency were also tracked. Results: Seaweed composition was 24.09% crude protein, 0.18% crude fiber, and 21.31% moisture, with a total energy of 2,615 kcal/kg. Adding 1% seaweed to cattle feed reduced ambient CH4 from 48.30±4.45 to 41.02±3.41 ppm (p <0.05), with no changes in feed intake or body temperature. Conclusion: Gracilaria tenuisfipitata var. Liui shows potential in reducing enteric methane emissions from cattle.

Abstract:
To ensure cow comfort and maintain farm hygiene for improved animal welfare and productivity in Bangladesh through IoT technology. praniSheba Prohori is an IoT-based system that monitors environmental parameters like methane, ammonia, humidity, and temperature in real-time using a WiFi/GSM network. The system sends alerts via SMS/email when conditions deviate from set thresholds. The system prevents hygiene-related diseases by notifying farmers of substandard conditions, reducing the incidence of mastitis and other illnesses. Prohori supports farmers in maintaining cleanliness and enables them to access loans and insurance more easily by proving compliance with farm hygiene standards. Prohori ensures cattle farm hygiene, contributing to the welfare of animals and economic benefits for farmers.

Abstract:
To develop an AI-based system for cattle identification, enabling insurance claims and financial protection for farmers by utilizing the unique characteristics of cattle muzzles. The system uses images of cattle muzzles (20,000 images from 1,000 cattle), processed using CLAHE and sharpening filters to remove noise. YOLO was employed for muzzle detection, and FaceNet was used for learning embeddings from muzzle images. The system achieved an accuracy of 95.41%, with a false acceptance rate of 0.003 and an F1 score of 92%, enabling farmers to insure their cattle and access financial protection. The muzzle-based identification system solves the challenge of cattle insurance, helping farmers secure their investments and boosting the rural economy.

Abstract:
To provide accessible, real-time veterinary services to remote and rural areas in Bangladesh using 4IR technologies. praniSheba Vet offers services through SMS, email, and social media, providing farmers with veterinary support within 15 minutes. It features disease tracking, mapping, and farm status reporting, with data securely stored for future analysis. As of now, praniSheba Vet has provided 964 prescriptions for 1,257 animals and 290 consultancy solutions, serving 823 households. The system supports ruminants, poultry, pets, and other animals. praniSheba Vet revolutionizes veterinary services in remote areas, enhancing livestock health management and enabling profitable, safe animal protein production.

Abstract:
To ensure the safe production of animal protein using 4IR technologies, including veterinary services, cattle identification systems, and farm hygiene monitoring. The praniSheba platform integrates ERP systems, IoT-based remote monitoring, and AI for cattle identification to optimize farm management and ensure hygiene. Farmers receive notifications to take immediate action if environmental conditions are unfavorable. The platform has served over 1,229 farmers, insured more than 600 cattle, and supervised 804 farmers in producing safe animal products. Its user-friendly interface is built with technologies like Vue.js, PHP, and MySQL. praniSheba provides innovative solutions for animal protein production, helping farmers improve profitability and produce safe food while achieving sustainable development goals.

Abstract:
To explore recent advancements in artificial intelligence for precision livestock farming, focusing on opportunities for enhancing productivity and animal welfare. This study reviews the application of AI in livestock farming, particularly in areas such as disease detection, environmental monitoring, and animal identification. Case studies from various AI implementations in livestock farming are discussed. AI has enabled precision in managing livestock health and optimizing farm operations, reducing costs, and increasing productivity. These advancements are particularly beneficial for smallholder farmers in rural areas. AI presents significant opportunities for transforming livestock farming, making it more efficient and accessible, especially in remote communities.