{"id":392,"date":"2026-03-02T17:57:44","date_gmt":"2026-03-02T22:57:44","guid":{"rendered":"https:\/\/site.caes.uga.edu\/precisionpoultry\/?p=392"},"modified":"2026-03-02T17:57:44","modified_gmt":"2026-03-02T22:57:44","slug":"a-precision-poultry-weighing-system","status":"publish","type":"post","link":"https:\/\/site.caes.uga.edu\/precisionpoultry\/2026\/03\/a-precision-poultry-weighing-system\/","title":{"rendered":"A Precision Poultry Weighing System"},"content":{"rendered":"\n<div data-wp-interactive=\"core\/file\" class=\"wp-block-file\"><object data-wp-bind--hidden=\"!state.hasPdfPreview\" hidden class=\"wp-block-file__embed\" data=\"https:\/\/site.caes.uga.edu\/precisionpoultry\/files\/2026\/03\/PDF.pdf\" type=\"application\/pdf\" style=\"width:100%;height:600px\" aria-label=\"Embed of PDF.\"><\/object><a id=\"wp-block-file--media-58bfb75f-3db8-447c-9dfb-fadf880bff26\" href=\"https:\/\/site.caes.uga.edu\/precisionpoultry\/files\/2026\/03\/PDF.pdf\">PDF<\/a><a href=\"https:\/\/site.caes.uga.edu\/precisionpoultry\/files\/2026\/03\/PDF.pdf\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-58bfb75f-3db8-447c-9dfb-fadf880bff26\">Download<\/a><\/div>\n\n\n\n<p>Poultry welfare monitoring is an important aspect of modern animal husbandry, particularly in cage-free layer, broiler, and breeder production systems where birds have greater freedom of movement but are also exposed to a broader range of health risks. Poultry bodyweight is a key welfare indicator; its monitoring is critical for evaluating the growth and performance of a flock. The obtained bodyweight and uniformity of the flock are indicators of daily growth rate, feed-to-meat conversion ratio, health conditions, and marketing day prediction. The traditional protocol is to manually sample and weigh a certain ratio of a flock one by one (e.g., 2% of the flock population or 50 birds, whichever is larger. However, conventional methods (i.e., catching a number of birds periodically) is time consuming, labor intensive, and tend to increase stresses on birds. For instance, a commercial poultry house has about 20,000 \u2013 30,000 birds in broiler houses or 50,000 birds in cage-free pullet\/layer houses, it\u2019s hard to know body weight of birds in real-time. Automatic monitoring of chickens\u2019 body weight is important for precision poultry productions and animal welfare.&nbsp;<\/p>\n\n\n\n<p>Researchers at the University of Georgia recently designed a new Internet of things (IoT)-enabled weighing platform (Figure 1) integrating load cells, an ESP32-S3 microcontroller, a Raspberry Pi 5, and Node-RED for data acquisition, processing, and visualization. The system recorded weight measurements at 1 Hz, detected individual weighing sessions, and applied a rolling-median filter to produce stable weight estimates.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"654\" src=\"https:\/\/site.caes.uga.edu\/precisionpoultry\/files\/2026\/03\/Picture1-1024x654.png\" alt=\"\" class=\"wp-image-393\" srcset=\"https:\/\/site.caes.uga.edu\/precisionpoultry\/files\/2026\/03\/Picture1-1024x654.png 1024w, https:\/\/site.caes.uga.edu\/precisionpoultry\/files\/2026\/03\/Picture1-300x192.png 300w, https:\/\/site.caes.uga.edu\/precisionpoultry\/files\/2026\/03\/Picture1-768x491.png 768w, https:\/\/site.caes.uga.edu\/precisionpoultry\/files\/2026\/03\/Picture1.png 1448w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p><strong>Figure 1.<\/strong> Measurement using standard scale and the developed scale. Both scales were placed side by side on a flat surface.<\/p>\n\n\n\n<p>Validation was performed against a reference scale during two weighing sessions one week apart using 75 cage-free hens randomly selected from a flock of 750 Hy-Line W80 birds (Figure 2). Bland\u2013Altman analysis and a linear mixed-effects model indicated a small overestimation of approximately 6\u20139 g, with most measurements falling within the 95% limits of agreement, while overall mean absolute percentage error remained below 3%. Improved accuracy during the second session suggests that platform stability influenced performance. Overall, the system demonstrates strong potential for continuous, low-stress weight monitoring in poultry farms. Future improvements should focus on refining calibration methods, enhancing mechanical stability, and integrating bird identification and presence-detection mechanisms to further support flock management and welfare monitoring.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"620\" src=\"https:\/\/site.caes.uga.edu\/precisionpoultry\/files\/2026\/03\/Picture2-1024x620.jpg\" alt=\"\" class=\"wp-image-394\" srcset=\"https:\/\/site.caes.uga.edu\/precisionpoultry\/files\/2026\/03\/Picture2-1024x620.jpg 1024w, https:\/\/site.caes.uga.edu\/precisionpoultry\/files\/2026\/03\/Picture2-300x182.jpg 300w, https:\/\/site.caes.uga.edu\/precisionpoultry\/files\/2026\/03\/Picture2-768x465.jpg 768w, https:\/\/site.caes.uga.edu\/precisionpoultry\/files\/2026\/03\/Picture2.jpg 1042w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p><strong>Figure 2. <\/strong>Validation of the weighing platform inside a cage-free poultry facility.<\/p>\n\n\n\n<p>This study developed and tested an automated weighing platform for monitoring bird weights in cage-free poultry houses. The system combines load-cell sensing, an ESP32 microcontroller for data collection and wireless transmission, and a Raspberry Pi for processing, filtering, and displaying the measurements. The platform successfully detected weight differences between birds and demonstrated improved performance when installed on a stable surface. Future work will focus on improving the platform\u2019s mechanical stability, refining calibration, strengthening filtering algorithms, and addressing multi-bird events to improve system performance. Additional studies will examine how birds interact with the platform in commercial conditions, explore bird-level identification strategies, and incorporate edge-based analytics to support flock management and welfare monitoring.<\/p>\n\n\n\n<p>Further reading: Dhungana, A., Paneru, B., Dahal, S., Song, Z., &amp; Chai, L*. (2026). Development and Validation of Internet of Things-Enabled Weighing System for Cage-Free Poultry Houses.&nbsp;<em>Sensors<\/em>,&nbsp;<em>26<\/em>(4), 1279. <a href=\"https:\/\/doi.org\/10.3390\/s26041279\">https:\/\/doi.org\/10.3390\/s26041279<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Poultry welfare monitoring is an important aspect of modern animal husbandry, particularly in cage-free layer, broiler, and breeder production systems where birds have greater freedom of movement but are also exposed to a broader range of health risks. Poultry bodyweight is a key welfare indicator; its monitoring is critical for evaluating the growth and performance [&hellip;]<\/p>\n","protected":false},"author":837,"featured_media":394,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[9,5,4],"tags":[],"class_list":["post-392","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-poultry","category-poultry-monitoring","category-precision-animal-production"],"_links":{"self":[{"href":"https:\/\/site.caes.uga.edu\/precisionpoultry\/wp-json\/wp\/v2\/posts\/392","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/site.caes.uga.edu\/precisionpoultry\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/site.caes.uga.edu\/precisionpoultry\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/site.caes.uga.edu\/precisionpoultry\/wp-json\/wp\/v2\/users\/837"}],"replies":[{"embeddable":true,"href":"https:\/\/site.caes.uga.edu\/precisionpoultry\/wp-json\/wp\/v2\/comments?post=392"}],"version-history":[{"count":1,"href":"https:\/\/site.caes.uga.edu\/precisionpoultry\/wp-json\/wp\/v2\/posts\/392\/revisions"}],"predecessor-version":[{"id":396,"href":"https:\/\/site.caes.uga.edu\/precisionpoultry\/wp-json\/wp\/v2\/posts\/392\/revisions\/396"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/site.caes.uga.edu\/precisionpoultry\/wp-json\/wp\/v2\/media\/394"}],"wp:attachment":[{"href":"https:\/\/site.caes.uga.edu\/precisionpoultry\/wp-json\/wp\/v2\/media?parent=392"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/site.caes.uga.edu\/precisionpoultry\/wp-json\/wp\/v2\/categories?post=392"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/site.caes.uga.edu\/precisionpoultry\/wp-json\/wp\/v2\/tags?post=392"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}