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Precision agriculture is about viewing and treating the agricultural process as a system and incorporating information available from all its parts to improve its performance. In order to do so, new enabling processes, tools, and technologies had to be developed to enable the observation and measurement of important variables, facilitate the study and assessment of these variables to extract relevant information and knowledge, and use this knowledge to control the agricultural process and its inputs/outputs. This book is a compilation of contributions, breakthroughs, and impactful research done by leading female researchers and scholars from various fields and from around the world toward making precision agriculture a reality. These researchers are creating new technological advances that are revolutionizing agriculture and providing innovative solutions to some of today’s most challenging global food problems, paving the way for a smarter, more precise, more efficient, and more profitable agriculture for the twenty-first century. This is the only known book focused on advances in precision agriculture for both land and livestock, led by women researchers and scholars, hence providing a unique woman’s perspective in a field primarily dominated by men. This chapter presents a holistic overview of the field, highlighting relevant technologies, decision-making strategies, practices, applications, economics, opportunities, and challenges for both land and livestock applications.

This chapter highlights significant milestones achieved by the author’s research program towards using biotelemetry to build precision poultry environmental controllers that respond directly and in real time to the needs of the birds. The discussion is presented in three sections: The first section focuses on biotelemetry and its use to monitor poultry deep body temperature (DBT) responses to various environmental conditions, the second section deals with DBT modeling efforts to date, and the third section presents results of the first poultry environmental controller prototype which responds to poultry DBT responses in real time.

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.

Blueberry mechanical harvesters cause bruise damage to the fruit. The goal of this study was to test a custom-made sensor (berry impact recording device) to measure the quality and magnitude of mechanical impacts created by three major types of commercial blueberry mechanical harvesters (rotary, slapper, and sway). The sensor was mounted on blueberry bushes (cultivated) and harvested at standard operating conditions such that the sensor was detached and experienced the impact forces typically found during a mechanical harvesting process. The data collected by the sensor revealed that the slapper and sway harvesters generated not only larger number but also higher magnitude impacts than the rotary. Our analyses suggest that these disparities were mostly caused by different agitating mechanisms, contacting surface materials, and designs between the three harvesters. Results indicated that most impacts lasted 5-7ms in all three harvesters. The distribution of the impacts showed that 90% of impacts from the rotary were less than 190g and 90% of impacts from the slapper and sway were less than 250g. Corresponding measures were identified to reduce potential bruise damage in the harvesters. The information could be useful to select harvesters that create the least impacts and to improve current mechanical harvester designs.

Southern highbush blueberries (Vaccinium corymbosum interspecific hybrids) are predominantly for the fresh market and, with a few exceptions, are hand harvested. Though mechanical harvesting systems are available for processed blueberries, low harvest efficiency and high fruit damage have limited their use for picking blueberries for fresh market. To improve the current machine harvester design, it is apparent that the interaction between the machine harvester and fruit should be well understood. The goal of this study was, therefore, to provide such an understanding by using a custom-made miniature instrumented sphere. The miniature Berry Impact Recording Device (BIRD) was used to measure the mechanical impacts created by a rotary mechanical harvester during fruit harvesting. A closeup video recorded the harvesting to pinpoint critical control points where most impacts were created. The results showed that the catch plates on the rotary harvester accounted for over 30% of all mechanical impacts imposed on the BIRD, followed by the empty fruit collection box (lug) (>20%). Impacts created by the conveyer belt and shaking rods combined accounted for only 25% of mechanical impacts. Thus, the most significant reduction in bruising could be achieved through modifications in the catch plates and lugs. The impact of three contacting surfaces (the catch plates, conveyer belt, and steel tunnel) on the harvester and three commercial padding materials (Cellular Silicone, Slow Rebound, and No Bruze) were evaluated. Harvester surface evaluations revealed that the catch plate was the hardest surface. Among the three padding materials evaluated, the Cellular Silicone provided the best cushioning. This study proved the efficacy of using a customized miniature instrumented sphere in measuring mechanical impacts created by various machine parts during harvesting. It provided a better understanding of how the berries interact with different machine parts of a rotary harvester.

This paper reports a complete impact data acquisition, processing, and analyzing software system that applies on the hardware platform of the Berry Impact Recording Device (BIRD). The software has three major sections that correspond to the hardware: The BIRD sensor program, the interface box program, and the computer software i-BIRD. The sensor program samples acceleration data from three axes and records them as single impacts with a maximum sampling rate of 3.0 kHz. Users can configure the sensor via the i-BIRD computer software, with different options of sampling frequencies (682–3050 Hz) and thresholds (0–205 g , where g is the gravitational acceleration). The data recorded can be downloaded, processed and graphically displayed on the computer. A real time clock was created using the interrupt service routine provided by the microcontroller. The accuracy of the sensor’s clock was calibrated with an error of 0.073%, which was adequate to record impact data in this application. The shape of impact curves recorded by the BIRD sensor at three sampling frequencies (682, 998, and 1480 Hz) matched well with the curves recorded by a high frequency (10 kHz) data logger with the maximum root mean squared error of 4.4 g. The velocity change had a relative error less than 5%. With confirmation of all those performances, the software system enabled the BIRD to be a useful tool to collect impact data during small fruit (such as blueberry) mechanical harvest.

In the past decade, farmers around the globe have witnessed what some consider an agricultural revolution. Driven by economic and environmental pressures, precision agriculture has emerged as a flurry of technological enhancements to traditional farm machinery and management tools. One does not have to closely examine a field to notice that the crop often thrives in certain areas while growing poorly in others. An intuitive approach to addressing this problem is to apply different amounts of seed, fertilizer, or other inputs according to the specific needs of the particular area of the field. For a very small field, manual control of inputs may be possible; however, for larger fields, this approach is unrealistic, if not impossible. Furthermore, this approach is subjective because control decisions are based on the operator's opinion and have no quantitative basis. In recent years, several technologies have emerged and made their way into the agricultural realm. These technologies provide the basis to realize the precision farming vision and to objectively manage agricultural land according to its spatially variable needs. Traditionally, whole-field application rates of inputs such as fertilizer and pesticide have been determined according to the maximum needs within a field. Precision farming practices significantly improve farm production in terms of net profit and limit environmental pollution potential.

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.

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 50.C showed a standard deviation of 0.5.C and a max error of 1.5.C. 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.

A popular notion among researchers is that management of animal environments could be improved with the use of physiological data collected in real time. This is a sort of “listen to the patient” approach to climate control. What variables to monitor and just how to use them is the subject of much research. Deep body temperature (DBT) has been pointed out in the literature as a potential indicator of heat stress. In this paper, we describe a number of investigations into the responsiveness and on-line measurability of DBT in commercial broilers. A biotelemetry system monitored DBT responses of broilers to changes in ambient temperature and relative humidity. Birds were less able to maintain their DBT when increases of ambient temperature occurred at faster rates. These results indicate that DBT is quite responsive to changes in the ambient environment. This together with the fact that responses could be measured on-line, make DBT a good stress indicator in poultry.

This article evaluates the effectiveness of using a telemetric deep body temperature (DBT) measurement system in 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°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, 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 an 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 on 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.

A yield monitoring system, which uses load cells mounted below the basket, was developed for application to existing and new peanut combines. A tank weighing assembly (free movement in both lateral directions) mount was used with double ended shear beam load cells in a 90° configuration to create the most flexibility with least opportunity for binding at the mounts. Slopes less than 2° did not appreciably affect the load cell response. Moving average and regression methods were used to translate the continuous loading of the basket to incremental loading rates. The system was capable of reproducing input loading rates to within a 1.5 to 7.3% error over an assumed ground area of 20 m 2 to 10 m 2 (220 ft 2 to 110 ft 2 ), respectively (under static operating conditions). The larger the area (number of values) included in the average, the smaller the error, but the less sensitivity to changing yield conditions. Additional integration with position signals, improved filtering, and some method of adjusting for pod moisture and trash are necessary to fully develop the system for commercial application.

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.

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

The use of implanted biotelemetry sensors for measuring poultry deep body temperature responses to ambient temperatures was investigated. The measurement procedure was validated in a two part study. In the first part, the ambient temperature was held constant and measurements of deep body temperature were collected over 24 h periods to detect diurnal rhythms. In the second part, ambient temperature was increased above normal growing conditions and deep body temperature responses to these increases were recorded. The telemetry measurement system was successful in detecting deep body temperature variations due to the diurnal rhythm and increases in ambient temperature. These results are promising for future use of biotelemetric measurements of deep body temperature as early indicators of heat stress.

A key step to enhance the efficiency of the highbush blueberry production is to improve current mechanical harvesting technologies which create excessive bruising and make blueberries unmarketable to the fresh market. The overall goal of this study was to quantitatively measure the dynamic interactions between the blueberry and the mechanical harvester and to understand how bruising is created during the harvesting process. A custom-made miniature instrumented sphere, known as blueberry impact recording device (BIRD), was used to measure the mechanical impacts created by a rotary mechanical harvester (Korvan 8000, Oxbo International, Lynden, WA). A close-up, visual recording of the harvesting process was made with a hand-held digital camcorder to pinpoint critical control points that create most impacts on the harvester. Four contacting surfaces (the catch pan, conveyer belt, steel tunnel, and empty lug box) on the harvester were evaluated by the BIRD sensor with regard to the impact they created. The results showed that the catch pans of the rotary harvester accounted for over 30% of all mechanical impacts imposed on a blueberry, followed by the lug (>20%), conveyer belt (13%), and shaking rod (13%). However, the high number of impacts in the lug might be overestimated by measuring only empty lugs. Thus, the most significant reduction in bruising could be achieved through improvements of the catch pans, conveyor belts, and lugs. Harvester surface evaluation confirmed that the catch pan was the hardest surface in the mechanical harvester. This study has shed light on how to reduce blueberry bruising by improving current mechanical harvesters, which will be invaluable to enhance blueberry production efficiency in the long run.

Bruising caused by the impact damage can occur frequently during mechanical harvest process for highbush blueberries. The overall goal of this study is to develop a sensor prototype to quantitatively measure the impact forces occurred on individual berry fruit during the mechanical harvest process. This information can be used to reduce bruising of blueberries during mechanical harvest. The sensing system developed in this study has three essential components: the sensor, the interface box and the software program on computer. The round circuit board of sensor is less than one inch (19.4 mm). It includes three Micro-Electro-Mechanical System (MEMS) accelerometers, one microcontroller along with other electronic components that have low power consumption. The sensor board and rechargeable battery are casted into a one inch (25.4 mm) sphere mold, using silicone rubber. The interface box serves as the intermediate communication platform to connect the sensor and the computer. The PC-software can acquire data via the RS232 communication and analyze data. The sensor was calibrated using centrifuge. The accuracy of the sensor output is -0.53% and 0.33% of the output span (±500 g). The resolution of the output is 1.465 (g) per digital count, with precision error 0.63%. By simulating the berry fruits under actual field situations, the sensor prototype could be a useful tool for evaluating the blueberry’s bruising damage during the mechanical harvest process.

Phase detection is important for supervision and control of fermentation processes. A new approach for phase detection is developed and applied to simulations of a Gluconic acid fermentation. The method uses fuzzy clustering to identify culture phases off-line. Using clustering results, neural networks are trained and used for on-line phase detection. The fermentation industry is becoming increasingly important as many

Real-time control is becoming an integral part of modern machine systems for efficient agricultural production. Maintaining consistently high quality agricultural production in the face of growing labor shortages is a challenge (Kisu, 1985). Providing a work place for laborers that meets increasingly rigorous safety requirements and environmental constraints is likewise a challenge (Grisso, et al. 1988). More appropriate energy management and soil compaction 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 chemical and physical soil states. Labor shortages and unfriendly environmental constraints coupled with the reality of spatial variability within agricultural production areas readily explain the move towards real-time control of agricultural equipment. A machine system in the context of this chapter is any combination of driving and driven elements that exerts force upon or is resisted by the soil. Machine systems include primary movers such as tractors, wide span vehicles, and other devices that provide propulsion for soil contacting implements. Self-propelled and dependent implements are also included in this definition. The scope of the following review is mainly limited to those primary driver functions that involve soil contact. In the caseof implements, the review goes somewhat beyond soil contact to include other functions such as chemical spraying because these control objectives could conceivably be readily transferred to soil engaging systems. Sensing and control systems currently used in the rapid characterization of machine state and soil variables are reviewed, with primary emphasis toward the soil state and secondary interest toward the machine state. Sensing systems with potential for inclusion in future machine systems are also reviewed. Since mapping is a relevant concern of what is now commonly referred to as site-specific field management or precision farming, sensing systems for mapping various soil state indicators are also discussed. For both control and mapping, sensing systems capable of on-the-go measurement are of major interest. The overall control objective in farming systems is to provide an adequate supply of food and fiber while maintaining profitability and sustainability. Much remains to be learned about the deterministic aspects of generic farming systems. A reasonably rigorous statement of the objective functions incorporating the prediction of physical and chemical soil properties is still beyond our grasp. How to handle stochastic (or chaotic) aspects of agricultural production in field environments is also a major challenge. This review will be confined to more immediate sensing and control objectives such as machine speed, draft, and energy management. Implement controllers are also reviewed. These immediate control objectives directly impact profitability and can indirectly and directly impact sustainability.

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 from 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 a field 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.