Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review

Animals play a profoundly important and intricate role in our lives today. Dogs have been human companions for thousands of years, but they now work closely with us to assist the disabled, and in combat and search and rescue situations. Farm animals are a critical part of the global food supply chain, and there is increasing consumer interest in organically fed and humanely raised livestock, and how it impacts our health and environmental footprint. Wild animals are threatened with extinction by human induced factors, and shrinking and compromised habitat. This review sets the goal to systematically survey the existing literature in smart computing and sensing technologies for domestic, farm and wild animal welfare. We use the notion of \emph{animal welfare} in broad terms, to review the technologies for assessing whether animals are healthy, free of pain and suffering, and also positively stimulated in their environment. Also the notion of \emph{smart computing and sensing} is used in broad terms, to refer to computing and sensing systems that are not isolated but interconnected with communication networks, and capable of remote data collection, processing, exchange and analysis. We review smart technologies for domestic animals, indoor and outdoor animal farming, as well as animals in the wild and zoos. The findings of this review are expected to motivate future research and contribute to data, information and communication management as well as policy for animal welfare

Field Trial and Performance Evaluation of IoT Poultry Farm Monitoring System

IoT technology revolutionizes poultry farming by enabling real-time data collection and analysis. Traditional manual methods for monitoring temperature, humidity, and AC voltage are being replaced with automated systems. The IoT setup includes three sensor nodes, CCTV, an IoT gateway, and a web server. Temperature ranges from 27 to 35˚C in off-fattening periods and consistently above 30˚C during fattening. Humidity fluctuates between 60% to 90% in both periods. The CPU temperature remains within safe limits. Uplink data rates exceed 2 Mbps, while AC voltage initially falls below standards but improves over time

Cloud-Based Iot Monitoring System for Poultry Farming in Nigeria

The monitoring of environmental parameters of poultry farm using IoT applications is no longer a new research area in the field of engineering. However, the cost of implementing most of the reviewed research work seams unaffordable to rural farmers in Nigeria. This could limit the adoption and usage of such devices. In this paper, we present a cost-effective cloud-based IoT monitoring system for poultry farming. The system uses two vital weather parameters- temperature and humidity. The methodology adopted, employed the use of DHT11 sensor (a temperature and humidity sensor) to note every change in temperature and humidity data of the farm environment. The sensed data were extracted, sampled and processed by the microcontroller before transmitting the data to a remote cloud server through the WiFi module. The cloud server (Thingspeak) received the sensed data, analysed the data and plot the data graphically. The plotted graph is viewed from a computer or any smart devices. The result indicates that temperature and humidity values range between 33-38°C and 31-33mmHg respectively. Furthermore, the results show that the device is efficient in monitoring the two environmental parameters. Therefore, the efficiency of the system will no doubt provide much quicker and accurate information about change in temperature and humidity data of farm environment

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

AIoT-Driven Edge Computing for Rural Small-Scale Poultry Farming: Smart Environmental Monitoring and Anomaly Detection for Enhanced Productivity

The growing demand for chicken production has emphasized the importance of maintaining optimal conditions to improve quality and productivity.The integration of Artificial Intelligence (AI) and the Internet of Things(IoT) is recommended for the efficient management of the farm's environment. A potential solution is presented in this paper, utilizing IoT-based sensor nodes with ARM Cortex M3 - LPC 1769 and LORA technology to monitor chicken farms across diverse regions.The proposed solution incorporates a low-cost edge computing server-Jetson Nano device equipped with a machine learning model to categorize and monitor live environmental conditions in poultry farms. Real-time data from various branches is collected and analyzed using machine learning classification techniques including logistic regression, K nearest neighbors, and support vector machines.The performance of these algorithms is compared to identify the most effective approach. Upon evaluation, the K nearest neighbors emerges as the superior performer, achieving an impressive accuracy of 99.72% and an execution duration of 0.087 seconds on the Jetson Nano edge computing device. This cost-effective technology is tailored for small businesses in regions where farmers can gain valuable insights from data-driven decisions and closely monitor their operations. By incorporating AIoT into farm management, the challenges faced by small-scale poultry farming can be addressed, empowering farmers with enlightened techniques to improve overall productivity and quality

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

Closed House Chicken Barn Climate Control Using Fuzzy Inference System

The hazardous gases in chicken barn such as Ammonia (NH3) and hydrogen sulfide (H2S) are the health threats to the farm animals and workers which influenced by climate changes. The chicken barn requires real-time control to maintain the barn climate and monitor hazardous gases. The outdated on-off and proportional control are not so efficient in energy saving and productivity. The solution to monitor environment of the chicken barn is using wireless electronic nose (e-nose) and Short Messaging System (SMS). The e-nose system is used for the barn’s temperature and humidity data acquisition. The chicken barn climate control is utilizing fuzzy interface system. MATLAB software was used for the model which is developed based on Mamdani fuzzy interface system. The membership functions of fuzzy were generated, as well as the simulation and analysis of the climate control system. Results show that the performance of the fuzzy method can improve the system to control the barn’s climate. This system also provides real-time alerts to farmers based on specific limit value for the climate. It makes it easier for farmers to follow up on-site or remotely control the environmental conditions in the barn by using the SMS system

Cloud-based data management system for automatic real-time data acquisition from large-scale laying-hen farms

: Management of poultry farms in China mostly relies on manual labor. Since such a large amount of valuable data for the production process either are saved incomplete or saved only as paper documents, making it very difficult for data retrieve, processing and analysis. An integrated cloud-based data management system (CDMS) was proposed in this study, in which the asynchronous data transmission, distributed file system, and wireless network technology were used for information collection, management and sharing in large-scale egg production. The cloud-based platform can provide information technology infrastructures for different farms. The CDMS can also allocate the computing resources and storage space based on demand. A real-time data acquisition software was developed, which allowed farm management staff to submit reports through website or smartphone, enabled digitization of production data. The use of asynchronous transfer in the system can avoid potential data loss during the transmission between farms and the remote cloud data center. All the valid historical data of poultry farms can be stored to the remote cloud data center, and then eliminates the need for large server clusters on the farms. Users with proper identification can access the online data portal of the system through a browser or an APP from anywhere worldwide

Renovating the iPMU via Internet of Things for Pollutant Emission Estimations in Poultry Facilities

The emissions of ammonia (NH3), particulate matter (PM2.5), and carbon dioxide (CO2) are major concerns in poultry facilities. They can pose environmental concerns and nuances. Robust and affordable measurement systems are needed to accurately measure in-barn concentrations and quantify the emissions. The Intelligent Portable Monitoring Unit (iPMU or PMU3) developed in 2016 was reconstructed into PMU4 to include upgraded NH3 and PM2.5 sensors and wireless connectivity for a low-cost, robust, and accurate air quality monitoring device with contactless data transfer using the concept of Internet of Things (IoT). In addition, a user-friendly web-based interface was developed for 1) real-time and historical data visualization, and 2) estimation of NH3 and PM2.5 emissions with suitable ventilation measurement. The PMU4 device developed in this project features back-up data stores and leverages cloud computing for data analysis and visualization. It was designed to collect and store sensor data (temperature, relative humidity, NH3, PM2.5, and CO2) on an SD card, and simultaneously send the data to a secure server. The PMU4 device uses Wi-Fi for internet access, but it was programmed to tolerate internet outages by queueing data and automatically transmitting the data queue to the secure server when the internet restores. The PMU4 device was deployed in the Robert T. Hamilton Poultry Teaching & Research Facility (Iowa State University, Ames, IA) for 13 days (September 15 – September 27, 2023) for field evaluation. The results showed that the mean temperature and relative humidity were 23.2 ± 1.8 ˚C and 62.1 ± 11.8% respectively. The mean concentrations of NH3, PM2.5, and CO2 were 0.47 ± 0.27 ppm, 12.0 ± 12.6 µg/m3, and 581.3 ± 134.2 ppm. The NH3 and PM2.5 per bird emission rates for the monitoring period were estimated using facility temperature, NH3 and PM2.5 concentrations, atmospheric barometric pressure obtained from the nearest airport, and ventilation and chicken inventory obtained from the facility manger. The mean per bird emission rates for NH3 and PM2.5 were 0.23 g/d/hen and 7.44 mg/d/hen, respectively. Advisor: Yijie Xion