A Study on the Development of Livestock Odor (Ammonia) Monitoring System Using ICT (Information and Communication Technology)

Abstract

Recently, environmental problems due to livestock odor have emerged in Korea, and technologies to solve these problems are being developed. This study developed a livestock odor monitoring system using ICT technology and an ammonia sensor. This system consists of a sensor, communication equipment, server, database management system (DBMS), and user operating program. The requirements for each component were presented and applied to the system. The livestock odor monitoring system is divided into Livestock Odor Management System (LOMS) and Livestock Odor Control System (LOCS). LOMS comprises a sensor that can measure ammonia in livestock farms or treatment facilities and communication equipment that transmits and receives data. LOCS consists of a server that can receive data and a program to check real-time data. This system will be used for trend analysis using livestock odor data and to plan to reduce livestock odor in the future.

Structure Optimization and Data Processing Method of Electronic Nose Bionic Chamber for Detecting Ammonia Emissions from Livestock Excrement Fermentation

“…Under the same gas concentration, the gas flow on the sensor surface varies, resulting in varied sensor responses. Previous studies [ 31 , 32 , 33 , 34 , 35 ] did not consider the electronic nose gas chamber’s effect on the output of the system. In this system, four groups of sensors with cross-sensitivity provide various response signals to the same gas concentration when activated via a bionic chamber.…”

“…Four different VOCs gases were classified using support vector machine (SVM) algorithms, and the accuracy rate was 97.5%. In a different approach, researchers developed a livestock odor monitoring system incorporating information and communications technology (ICT) and ammonia sensors [ 34 ], enabling trend analysis of collected odor data. Furthermore, a study applied electronic nose technology [ 35 ] in pigsties by integrating a deep learning algorithm optimized using particle swarm optimization to enhance detection accuracy, achieving a determination coefficient R 2 over 0.94.…”

Peningkatan Produktivitas Ayam dengan Formulasi Ransum Padat Nutrisi Berbahan Baku DDGS di Lahan Pantai

“…Demonstrasi penduduk tidak dapat dihindari dikarenakan aroma yang ditimbulkan dari usaha peternakan. Beberapa alasan untuk menutup perusahaan peternakan unggas antara lain, bau yang tidak sedap (Yoon et al, 2022), banyak lalat dan sumber berbagai penyakit (Gerber et al, 2007). Selain itu persaingan lahan peternakan dengan pemukiman penduduk (Abbas, 2011), maka dari itu peternak perlu memanfaatkan lahan yang jarang dipakai oleh penduduk salah satunya lahan pantai atau daerah pinggiran pantai.…”

Long-term performance validation of NH3 concentration prediction model for virtual sensor application in livestock facility

“…Therefore, NH 3 concentrations need to be controlled according to the guidelines set [ 17 ]. However, because NH 3 is alkaline and toxic, it can cause corrosion in detection sensors [ 18 ].…”