Smart Dairy Cattle Farming and In-Heat Detection through the Internet of Things (IoT)

Abstract

Smart farming is the practice of intelligent agricultural management based on technological data gathered from farm practice for the purpose of increased levels of quality, production and environmental protection. The Internet of Things (IoT) technology is revolutionized in various aspects of agriculture around the world and its application found success in some countries. In this paper, a non-invasive and non-contact estrus detection system integrated IoT technology to improve the detection efficiency of standing-heat behaviors of cows is proposed. The outcome of this study will improve the farm’s management practices through the integration of IoT technology that can remotely monitor the activities of the cows and provide data for analysis and evaluation, will increase the likelihood of future estrus instances for every correct prediction, and will improve the fertility and milk production rates of the cows. Such benefits can contribute to the growth and development of the dairy cattle industry in the Philippines. This study also shows the application of IoT in improving the detection efficiency of standing-heat behaviors of cows through automated detection using Pan-tilt-zoom (PTZ) cameras and a Python-driven Web Application. The dimensions of the barn are measured, and the Cameras' Field of Views (FOVs) is pre-calculated for the strategic positions of the cameras atop of the cowshed. The program detects the cows and any estrus events through the surveillance cameras. The result is sent to the cloud server to display on the web application for analysis. The web app allows updates on cow information, inseminations, pregnancy, and calving records, estimate travel time from the user's geolocation to the farm, provide live monitoring and remote camera accessibility and control through the cameras and deliver reliable cross-platform push-notification and call alerts on the user's device(s) whenever an estrus event is detected. The system initially and correctly detected 4 standing-heat signs, but with 2 false predictions and identifications leading to 2 “True Positive” and 2 “False Positive” results, attaining a 50% detection efficiency. Based on the results, the program performed satisfactorily at 50% detection efficiency.

Feature extraction for artificial intelligence enabled food supply chain failure mode prediction

“…An interesting outcome from the synthetic data is that global features such as network complexity and uncertainty which were motivated by literature [10] are not as important as case specific ones. This is promising for the relative importance of the incorporation of IoT technologies into food supply chains, including that such applications are the focus of considerable research (e.g., [76][77][78][79][80][81][82]). However, some of the more granular features such as Viable Logistics (VL) should be decomposed into critical nodes, and in relevant cases, intra-and inter-organizational variables [10].…”

“…Dairy supply chains are increasingly adopting IoT and other Industry 4.0 technologies [76,77]. A wide array of sensor devices that can connect to the IoT are available including smart collars for cow behavior monitoring [78] and estrus events [79]. A low-cost IoT solution for milk-quality monitoring has been proposed [80] as well as IoT-based disease diagnosis in cows [81].…”

Birth of dairy 4.0: Opportunities and challenges in adoption of fourth industrial revolution technologies in the production of milk and its derivatives

“…There are many types of data that IoT can collect and share, such as temperature, heart rate and cow movement, which provide information on the health, position and behaviour of the cows, such as standing, lying down, eating or oestrus events ( Pratama et al, 2019 ; Arago et al, 2022 ). These data can help farmers to make more informed decisions, for instance regarding the cow health, feeding and milking frequency.…”

Transforming Philippine Agriculture Through Data-driven Innovation: A Quantitative Landscape Assessment to Prioritize Technological Solutions

“…Agricultural innovations refer to the range of technologies, techniques, systems, and smart farming practices that harness cutting-edge tools like AI, and IoT (Espineli and Lewis, 2021; Tagle et al, 2018;Arago et al, 2022), advanced sensors (Cruz et al, 2018;Bacsa et al, 2019), robotics (De Padua et al, 2021) and data analytics (Lauguico et al, 2020: Velasco, 2020 to solve various e ciency, sustainability, productivity, and decision-making challenges in agriculture (Dhanaraju et al, 2022;Abashidze, 2023).…”