Traditional environmental control methods for poultry housing which rely solely on environmental factors fall short in meeting thermal and physiological needs of the animals. New methods are needed that factor in the physiological needs and responses of the animals in order to maximize well-being of the animals and minimize heat stress. Deep body temperature (DBT) has been shown in the literature to be a strong indicator of heat stress, therefore studies are needed that help us gain a deeper understanding of the relationship between this variable and environmental conditions. The aim of this study was to identify the order of the dynamic response of poultry DBT to large step changes in ambient temperature (AT). Temperature steps had to be big enough to take the chickens out of their homeothermic zone. A total of 46 DBT/AT data sets with 23 upward AT steps and 23 downward AT steps were obtained using a biotelemetry system, and involving three chickens. DBT responses of individual chickens to step changes in AT were found to have a 0.88 average Pearson correlation suggesting consistency in chickens' responses to the same stimuli (p<0.0005). The data indicated that DBT responses to AT followed a first order behavior in most cases with an average time constant of 1.6 h, and the curve fitting method was used to validate this observation. There was a 0.88 average correlation between DBT model and measured data (p<0.0005). These results indicate statistical significance in the data used and the model derived from it. In conclusion, it is reasonable to assume that the dynamic response of poultry DBT to large step changes in ambient temperature follows a first order model. Although further studies are needed to more fully derive the model, this study provided a stepping-stone towards gaining a better understanding of the relationship between DBT and AT, therefore taking us one step closer towards making optimal management and risk assessment decisions that are based on physiological needs of the chickens.

Tunnel ventilation has been adopted as an effective approach to combatting heat stress in poultry. Setting tunnel air velocity to levels that ensure bird comfort, while optimizing performance is an important goal. In recent years, biotelemetry has provided a way to effectively evaluate the impact of management practices on poultry physiology. In this study, we present an approach for evaluating the effects of heat stress and tunnel ventilation on poultry deep body temperature (DBT) using biotelemetry. Three consecutive experiments were conducted using 6 broilers, each at the ages of 8.6, 9.0, and 9.4 wk. Experiments spanned approximately 12 h each and led to 18 data sets. DBT responses of birds under no ventilation rose by as much as 3°C as a result of step increases in ambient temperature. Birds exposed to tunnel ventilation maintained a lower DBT by as much as 0.9°C. During experiment days, birds exposed to tunnel ventilation consistently gained weight with a percentage weight gain ranging from 1% to 11%. Birds not exposed to tunnel ventilation behaved less consistently with some gaining as much as 14% while others lost as much as 9%. Although further studies are required to derive more comprehensive and more statistically significant results, this study provided preliminary data that is needed to warrant such studies, and a stepping stone for making optimal management and risk assessment decisions that are based on physiological needs of the birds.

Current poultry housing environmental controllers do not directly factor in the state of the birds themselves, such as their deep body temperature (DBT) responses. In this article, we propose and investigate the feasibility of a new approach for real-time closed-loop control of poultry DBT under heat stress conditions using variable air velocity. Using five commercial breed broilers, an experimental tunnel ventilation enclosure placed inside an environmentally controlled chamber, implanted radio telemetry sensors, and a programmable logic controller (PLC), four experimental trials were conducted using a proportional-integral type feedback controller. The results indicated that (1) air velocity has a measurable, dynamic, and almost immediate impact on DBT of birds under heat stress; and (2) DBT of heat-stressed broilers can be maintained below a setpoint by varying air velocity using feedback control. These preliminary results suggest that using DBT as a feedback variable to manipulate air velocity within poultry housing is a promising approach. This article represents a first step towards the design of the future poultry environmental controller that responds directly to the physiological needs of the birds.

Rapid developments in semiconductor, computing, and communication technologies have led to a new generation of sensors called "smart" sensors which are capable of wireless communication with a remote site, communication among themselves, as well as automated data processing such as detection of regularities and correlations in raw data, extraction of relevant features, and the establishment of cause and effect relationships. Sensors are also being tied to radio frequency identification (RFID) devices to provide remote sensing capabilities for "smart environment" applications. This paper provides an overview of smart sensor networks and illustrates their use through a prototype application using the Berkeley smart sensor "Mote".

Radio Frequency Identification (RFID) technology is commonly used for object or animal identification and tracking. In this article, we explore the feasibility of its use in a rapid solution to wireless real-time monitoring of soil properties. A lab prototype system for wireless measurement of temperature was developed using a commercially available 13.56-MHz RFID passive tag. Temperature is sensed by a thermometer Integrated Circuit (IC) that produces a Pulse Width Modulated (PWM) signal. An embedded Motorola 68HC11 microcontroller monitors this signal, produces averaged measurements, and sends them to the RFID "tag" or transponder unit, hence the "smart" feature of the sensor A receiving unit also called the "interrogator" emits an electromagnetic field, which when detected by the passive RFID tag causes it to transmit temperature data stored in its memory to the interrogator. The latter detects these measurements and sends them to a data collection PC. The architecture of the sensor allows for the addition of other transducers without alteration of the telemetry, channel or significant changes to the sensor design. In benchmarking tests using a water bath over the course of several days, measurement error over a range of 0 to 50degreesC showed a standard deviation of 0.5degreesC and a max error of 1.5degreesC. Measurements also showed a high correlation (greater than 99%) with those obtained using a thermocouple. The architecture of the developed wireless sensor prototype allows for additional soil transducers to be integrated into it without changes to the sensor design. Potential applications for this sensor could be in the area of precision farming where soil properties such as temperature might be monitored in a wireless manner Although limitations in transmission range (less than one meter) would require proximity, reading of the sensor using existing equipment that regularly pass over the field as a mount for the interrogator such as center pivot booms or sprayers, would increase feasibility of this telemetry strategy.

This paper reports on four microcontroller-based courses developed at the University of Georgia for a broad multidisciplinary undergraduate and graduate student body. The courses are Introduction to Robotics, Embedded Systems, Introduction to Microcontrollers, and Advanced Microcontrollers. These courses, which are taught in a hands-on manner, equip students with the necessary tools and know-how to make use of the powerful technology of microcontrollers within their own disciplines. This paper addresses some of the challenges encountered due to the diverse student backgrounds and how these challenges are met through various pedagogical methods such as teamwork, achieving the right balance between theory and practice, and giving students from various disciplines an 'industry-like' experience.

This paper reports on four microcontroller-based courses developed at the University of Georgia for a broad multidisciplinary undergraduate and graduate student body. The courses are Introduction to Robotics, Embedded Systems, Introduction to Microcontrollers, and Advanced Microcontrollers. These courses which are taught in a hands-on manner equip students with the necessary tools and know-how to make use of the powerful technology of microcontrollers within their own disciplines. The paper addresses some of the challenges encountered due to the diverse student backgrounds and how these challenges are met through various pedagogical methods such as team work, achieving the right balance between theory and practice, and giving students from various disciplines an “industry like” experience.

In this paper, the author discusses the problem of monitoring and control in enclosed animal production environments with emphasis on the poultry sector. First, he gives an overview of historic developments in this area and the different control strategies that are commonly used today. Then, he discusses reasons why it is becoming important to develop and adopt more effective monitoring and control techniques in the poultry industry and investigates the feasibility of using intelligent control for improved control of the poultry housing environment.

The most essential component of precision farming is the yield monitor, a sensor or group of sensors installed on harvesting equipment that dynamically measure spatial yield variability. Yield maps, which are produced using data fi-om yield monitors, are extremely useful in providing the farmer a color-coded visual image clearly showing the variability of yield across a field. University of Georgia scientists recently completed development work on PYMS, the Peanut Yield Monitoring System. PYMS uses load cells for instantaneous load measurements of harvested peanuts and has proven to be accurate to between 2% and 3% on a trailer-load basis and to approximately 1% on afield basis when using data collected during combine operation. PYMS data are accurate to around 1% on a basket-load basis when using data collected under static conditions. The instantaneous accuracy of PYMS was calculated to be 700 kg/ha. Basing management decisions on the yield of individual pixels of PYMS yield maps is not realistic. The strength of PYMS is in differentiating yield trends and evaluating management practices. The system was extensively and successfully field-tested over a 3-year period and evaluated by 11 users during 1999, all of whom were able to use the resulting yield maps to evaluate current management practices or to develop future management plans. The University of Georgia has submitted a patent application for PYMS, and the technology has been licensed.

Real-time control is becoming an integral part of modern machine systems for high-quality agricultural production. Maintaining consistently high-quality agricultural production while keeping up with growing labor shortages is a challenge. Providing a workplace for labourers which meets increasingly rigorous safety requirements and environmental constraints is likewise a challenge. More appropriate energy management and soil management have also motivated real-time control applications. Appropriate sensing and control systems can reduce labor requirements, function in difficult environments, and allow vehicles to adapt to varying soil chemical and physical states. Labor shortages and environmental constraints coupled with the reality of spatial variability of chemical and physical properties among and within agricultural production areas readily explain the migration toward real-time control of agricultural equipment. This paper presents a review of the most recent advances in the development of sensors and controllers for agricultural applications.

Precision farming describes the process of measuring and mapping land crop characteristics and then using these measurements to develop precise and intelligent application strategies that improve overall farm production. Yield monitoring is the phase of precision farming in which the crop yield variation within a field is measured and mapped. Yield maps from previous seasons can be used to determine the needed inputs in the field, whereas post harvest yield maps can be used to evaluate the implemented methods and make adjustments for the next season. Grain yield monitors are available, however there are few if any monitors for other crops. This paper examines the use of strain gauge load cells in a yield monitor for peanut combines and the development of methods for minimizing measurement noises thereby increasing reliability

Successful implementation of a transmission level harmonic measurement system requires accurate and reliable measurement of harmonic voltages and currents. Existing substation instrument transformers are designed for harmonic-free 60 Hz measurements. Hence, their use to measure harmonics leads to the introduction of resonance as well as saturation errors in the measurements. An online error correction method to correct for wound-type potential transformer measurement errors has been proposed in previous publications. The error correction was formulated as an output tracking problem where the distorted measurements were used along with the experimentally developed transformer model to reconstruct the transformer input. The scope of this paper is to develop a sensitivity analysis for the error correction method with respect to the transformer parameters. Results of this analysis indicate that the sensitivity of the method with respect to the transformer parameters is quite low.

In this article, the feasibility of using artificial neural network (ANN) models for predicting deep body temperature (DBT) responses of broilers to stressful step changes in ambient temperature was determined. Experiments were carried out using three different birds exposed to five AT schedules. DBT responses were measured using telemetric sensors, Various ANN architectures were tested and the Elman-Jordan was determined to be most suitable. A factor analysis was conducted to determine input variables most appropriate for the prediction. Although relative humidity (RH) was maintained almost constant, including it as art input to the network led to improved predictions, The ability of the developed models to predict DBT responses to AT schedules not used in training and/or responses from a bird not used in training was examined. The models performed reasonably well when predicting responses of a different bird to AT schedules used in training, The models performed well when predicting responses of a bird used in training to new AT schedules. However predictions of the models were less accurate when dealing with a different AT schedule oa a different bird. This latter result is not surprising considering that the network had to adapt to two new conditions with training based on a limited data set, Using a larger data set with more birds and more AT schedules would likely lead to improved DBT predictions, Results of this study indicate that neural networks could potentially be used for predicting the impact of heat stress conditions on bird physiology.

The successful implementation of a transmission level harmonic measurement system requires accurate and reliable measurement of harmonic voltages and currents. Existing substation instrument transformers are designed for 60 Hz measurements and they have been shown to cause resonance errors in the measurements. In this paper, we propose an on-line error correction method to correct for these resonance errors as well as possible saturation errors. The error correction is formulated as an output tracking problem where the distorted measurements are used along with the experimentally developed transformer model to reconstruct the transformer input. The method is generic, thereby permitting its use with any measurement system that utilizes a transducer with nonideal properties. It is also cost effective since it can be implemented on a personal computer or a digital signal processing chip.

Bioprocesses are highly nonlinear and they operate within a wide range of operating regimes. Proper modeling and control of these processes necessitate real-time identification of these regimes. In this paper, the authors introduce an approach for the development of a fuzzy NN model for a bioprocess based on decomposition of the process into its different regimes. The model consists of multiple linear local models, one for each regime, and its output is the interpolation of the outputs from the local models. Regime identification is performed using fuzzy clustering and neural networks. The outcome of this identification technique is a set of membership functions which indicate to what degrees the process is governed by the three operating regimes at any given point in time. The method is illustrated through the development of a real-time product estimation model for a simulated Gluconic acid batch fermentation.

This article evaluates the effectiveness of using a telemetric deep body temperature (DBT) measurement sq stem ill determining the effects of stressful combinations of ambient temperature (AT) and relative humidity (RH) conditions on poultry. Three levels of ambient temperature (31, 34, and 37 degrees C) and two levels of relative humidity (50 and 80%) were considered. Treatments were applied using a Latin square design and repeated measures of deep body temperature were made during 5 h exposures, Results showed that the measured responses were consistent among all birds, significantly, different for the different environmental conditions, and a change in response from one set of conditions to the other was clearly attributed to the change in AT and RH conditions and not to fluctuations in the measurement system or in between bird variation. This ability to detect DBT responses to different environmental conditions in real time could be used as the stepping stone for developing more optimal closed loop environmental controllers which use DBT responses as a feedback variable.

In this article, we discuss the problem of monitoring and control in the poultry housing environment. First, wegive an overview of historic developments in this area and review the different control strategies which have beenproposed in the literature. Then, we discuss reasons why it is becoming important to develop and adopt more effectivemonitoring and control techniques in the poultry industry and propose recommendations for improved control of thepoultry housing environment.

A prototype peanut yield monitoring system based on load cell transducers was evaluated for use in precision farming applications. Noise characteristics under simulated field conditions were examined, and the effect of mixing within the peanut combine during harvest was also investigated. Evaluation results showed that the system has potential for providing limited quality site-specific yield measurements for yield mapping applications.