Height and Pressure Test for Improving Spray Application

Pesticide application in agricultural fields affects a little over a million acres each year (USDA 2012). Current spray application equipment can automatically adjust nozzle flow rates in reaction to speed changes to maintain consistent application rates across the field. Uniform distribution of pesticides from the spray boom is critical to ensure proper crop care while minimizing negative environmental effects. Boom pressure and height are two primary factors that affect proper spray uniformity; however information on the combined effects of these factors are limited. The goal of this study was to provide end users with quantified data regarding the effects of combined nozzle pressure and height variability on spray uniformity for three common spray nozzles. Specific objectives of this project were to 1) determine a suitable operating envelope (i.e., nozzle pressure and height) to meet current performance standards for the nozzles tests, 2) determine errors between theoretical spray distributions (from nozzle manufacturer flow and spacing data) to laboratory patternator data collected at different nozzle pressures, and 3) compare nozzle distribution errors (theoretical versus patternator data) with coefficient of variation (CV), a current spray uniformity performance metric. A laboratory patternator was used to collect nozzle distribution in 25 mm increments across the spray pattern while varying height and pressure for the spray nozzles tested. The operating envelope for different combinations of pressure and height was considered acceptable if the CV values were less than 10%. CV values were compared to root mean squared error (RMSE) for the AIXR 11003 nozzles operated at a height of 51 cm and four operating pressures to evaluate potential differences when accuracy is considered (i.e., RMSE). In some configurations the data exceeded 10% CV resulting in a more constricted operating envelope for each individual nozzle type. The CV values show more variance versus RMSE values. For the AIXR11003, as pressure increased the RMSE decreased in value, meaning the experimental pattern became closer to the ideal pattern as pressure increased. The CV values decreased as pressure increased until a threshold is reached; CV values focus on precision but not accuracy, showing the spray pattern was consistent but not necessarily accurate, indicating the CV disregards the theoretical values and does not indicate error in the values. Thus accuracy of spray pattern distribution may not be considered in the manufacturer’s nozzle report

Recalibration Methodology to Compensate for Changing Fluid Properties in an Individual Nozzle Direct Injection Systems

Limited advancement of direct injection pesticide application systems has been made in recent years, which has hindered further commercialization of this technology. One approach to solving the lag and mixing issues typically associated with injection-based systems is high-pressure individual nozzle injection. However, accurate monitoring of the chemical concentrate flow rate can pose a challenge due to the high pressure, low flow, and changing viscosities of the fluid. A methodology was developed for recalibrating high-pressure chemical concentrate injectors to compensate for fluid property variations and evaluate the performance of this technique for operating injectors in an open-loop configuration. Specific objectives were to (1) develop a method for continuous recalibration of the chemical concentrate injectors to ensure accurate metering of chemicals of varying viscosities and (2) evaluate the recalibration method for estimating individual injector flow rates from a system of multiple injectors to assess potential errors. Test results indicated that the recalibration method was able to compensate for changes in fluid kinematic viscosity (e.g., from temperature changes and/or product variation). Errors were less than 3.4% for the minimum injector duty cycle (DCi) (at 10%) and dropped 0.2% for the maximum DCi (at 90%) for temperature changes of up to 20°C. While larger temperature changes may be expected, these test results showed that the proposed method could be successfully implemented to meet desired injection rates. Because multiple injectors would be used in commercial deployment of this technology, a method was developed to calculate the desired injector flow rate using initial injector calibration factors. Using this multi-injector recalibration method, errors ranged from 0.23% to 0.66% between predicted and actual flow rates for all three injectors

Recalibration Methodology to Compensate for Changing Fluid Properties in an Individual Nozzle Direct Injection Systems

Limited advancement of direct injection pesticide application systems has been made in recent years, which has hindered further commercialization of this technology. One approach to solving the lag and mixing issues typically associated with injection-based systems is high-pressure individual nozzle injection. However, accurate monitoring of the chemical concentrate flow rate can pose a challenge due to the high pressure, low flow, and changing viscosities of the fluid. A methodology was developed for recalibrating high-pressure chemical concentrate injectors to compensate for fluid property variations and evaluate the performance of this technique for operating injectors in an open-loop configuration. Specific objectives were to (1) develop a method for continuous recalibration of the chemical concentrate injectors to ensure accurate metering of chemicals of varying viscosities and (2) evaluate the recalibration method for estimating individual injector flow rates from a system of multiple injectors to assess potential errors. Test results indicated that the recalibration method was able to compensate for changes in fluid kinematic viscosity (e.g., from temperature changes and/or product variation). Errors were less than 3.4% for the minimum injector duty cycle (DCi) (at 10%) and dropped 0.2% for the maximum DCi (at 90%) for temperature changes of up to 20°C. While larger temperature changes may be expected, these test results showed that the proposed method could be successfully implemented to meet desired injection rates. Because multiple injectors would be used in commercial deployment of this technology, a method was developed to calculate the desired injector flow rate using initial injector calibration factors. Using this multi-injector recalibration method, errors ranged from 0.23% to 0.66% between predicted and actual flow rates for all three injectors

Precision Agriculture Usage and Big Agriculture Data

Agricultural producers have quickly adopted precision agriculture technologies in recent years. With the availability of global positioning system (GPS) signals and other technology, producers can track yields, steer and control equipment, monitor field conditions, and manage inputs at very precise levels across a field, offering the potential to substantially increase productivity and profitability

Precision Agriculture Usage and Big Agriculture Data

Agricultural producers have quickly adopted precision agriculture technologies in recent years. With the availability of global positioning system (GPS) signals and other technology, producers can track yields, steer and control equipment, monitor field conditions, and manage inputs at very precise levels across a field, offering the potential to substantially increase productivity and profitability

The Impact of Different Data Processing Methods on Site-specific Management Recommendation

Precision agriculture has the potential to enhance farming profitability substantially via site-specific management of fields. One of the promising ways of generating such profitability-enhancing input is to a use recommendation map is on-farm randomized trials. The process of generating an input (say nitrogen) using a recommendation map typically involves the following steps: 1. Design and implement randomized input use trial 2. Collect yield data along with other field characteristics (Slope, Electrical Conductivity, and Organic Matter) 3. Process the data for statistical analysis 4. Conduct regression analysis to estimate production function (how the input affect crop yield) 5. For each of the management units, find the input rate that maximizes profit for that uni

The Impact of Different Data Processing Methods on Site-specific Management Recommendation

Precision agriculture has the potential to enhance farming profitability substantially via site-specific management of fields. One of the promising ways of generating such profitability-enhancing input is to a use recommendation map is on-farm randomized trials. The process of generating an input (say nitrogen) using a recommendation map typically involves the following steps: 1. Design and implement randomized input use trial 2. Collect yield data along with other field characteristics (Slope, Electrical Conductivity, and Organic Matter) 3. Process the data for statistical analysis 4. Conduct regression analysis to estimate production function (how the input affect crop yield) 5. For each of the management units, find the input rate that maximizes profit for that uni

Factors Influencing Producer Propensity for Data Sharing & Opinions Regarding Precision Agriculture and Big Farm Data

With its tremendous success by notable companies in varying industries, “big data” has become a hard-to-miss phrase and many believe its usage in agriculture is the future of the industry. However, the potential benefits of using big data come with just as many challenges, ranging from not knowing how to make use of it, to the debate over who owns and has access to it. A survey asking for producers’ opinions on precision agriculture technologies and big farm data was distributed to a sample of agricultural producers across Nebraska. A Poisson regression was used to determine the factors influencing propensity for data sharing and frequency tables were used to examine producer opinions on the topic. Older producers and those not using irrigation in their operation were found to have a lower propensity for sharing their farm-level data. In general, producer understanding of what big data is and how to use it is lacking. Precision agriculture users mostly believe they have seen increases in profits and efficiency due to use, but producers expressed concern over not knowing how to interpret and make use of the data as well as the overall affordability and cost of the technologies producing the data

Factors Influencing the Adoption of Precision Agriculture Technologies by Nebraska Producers

An ever-increasing world population and increasingly-volatile commodity prices have charged producers with the task of becoming more efficient. To combat this, precision agriculture technologies aimed at increasing production efficiency are continually being developed, but their adoption is not yet widespread. A survey regarding the usage of these technologies was distributed to a sample of row crop producers across the state of Nebraska and a Poisson regression was used to determine the factors influencing adoption. Results of the study indicate that larger, more tech-savvy producers and those using irrigation are more likely to adopt a higher number of precision agriculture technologies, while operator age and gross farm income were found to be non-influential factors

Guidance Directrix Generation Using Laser Sensors

A sensor array consisting of two laser sensors was utilized to determine the guidance directrix (offset distance-d, heading angle-ø) that are required as reference inputs for an automated guidance system. The sensor array was evaluated in both laboratory and field conditions. Under laboratory conditions the sensor array replicated the physical profile of the target surface with a 4% error in determining the heading angle. Field tests were conducted in two types of crops; corn and alfalfa. The sensor array identified the cut-crop edge profile ahead of the tractor and replicated distinct shapes of the cut-crop edge. RMSE values in determining the offset distances and heading angles of the cut-crop edge in corn were within 5.5 cm and 4.39°. In the case of alfalfa cut-crop edge the RMSE values were within 6.6 cm and 4.32°