Thorough and precise estrus detection plays a crucial role in the fertility of dairy cows. Farmers commonly used direct visual monitoring in recognizing estrus signs which demands time and effort and causes misinterpretations. The primary sign of estrus is the standing heat, where the dairy cows stand to be mounted by other cows for a few seconds. Through the years, researchers developed various detection methods, yet most of these methods involve contact and invasive approaches that affect the estrus behaviors of cows. So, the proponents developed a non-invasive and non-contact estrus detection system using image processing to detect standing heat behaviors. Through the TensorFlow Object Detection API, the proponents trained two custom neural network models capable of visualizing bounding boxes of the predicted cow objects on image frames. The proponents also developed an object overlapping algorithm that utilizes the bounding box corners to detect estrus activities. Based on the conducted tests, an estrus event occurs when the centroids of the detected objects measure a distance of less than 360px and have two interior angles with another fixed point of less than 25° and greater than 65° for Y and X axes, respectively. If the conditions are met, the program will save the image frame and will declare an estrus activity. Otherwise, it will restart its estrus detection and counting. The system observed 17 cows, a carabao, and a bull through the cameras installed atop of a cowshed, and detects the estrus events with an efficiency of 50%.
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.
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