The feasibility of using optical sensors and non-nitrogen limiting reference crops to determine post-emergent nitrogen fertilizer requirements of canola was evaluated. Normalized difference vegetation index was well suited for estimating yield potential and nitrogen status. Although sensor-based nitrogen management was generally agronomically feasible for canola, the economic benefits of doing so remain uncertain because of the added cost of applying post-emergent nitrogen.

The popularity of variable-rate technology (VRT) has grown. However, the limitations and errors ofthis technology are generally unknown. Therefore, a spatial data model was developed to generate "asapplied"surfaces to advance precision agricultural (PA) practices. A test methodology based on ASAEStandard S341.2 was developed to perform uniform-rate (UR) and variable-rate (VR) tests to characterizedistribution patterns testing four VRT granular applicators (two spinner spreaders and two pneumaticapplicators). Single-pass UR patterns exhibited consistent shapes for three of the applicators with patternsshifts observed for the fourth applicator. Simulated overlap analysis showed that three of the applicatorsperformed satisfactorily with most CVs less than 20% while one applicator performed poorly (CVs andgt;25%). The spinner spreaders over-applied at the margins but the pneumatic applicators under-appliedsuggesting a required adjustment to the effective swath spacing. Therefore, it is recommended that CVsaccompany overlap pattern plots to ensure proper calibration of VRT application.Quantification of the rate response characteristics for the various applicators illustrated varying delayand transition times. Only one applicator demonstrated consistent delay and transition times. A sigmoidalfunction was used to model the rate response for applicators. One applicator exhibited a linear responseduring a decreasing rate change. Rate changes were quicker for the two newer VR control systemssignifying advancement in hydraulic control valve technology. This research illustrates the need forstandard testing protocols for VRT systems to help guide VRT software developers, equipmentmanufacturers, and users.The spatial data model uses GIS functionality to merge applicator descriptive patterns with a spatialfield application file (FAF) to generate an 'as-applied' surface representing the actual distribution ofgranular fertilizer. Field data was collected and used to validate the "as-applied" spatial model.Comparisons between the actual and predicted application rates for several fields were madedemonstrating good correlations for one applicator (several R2 andgt; 0.70), moderate success for anotherapplicator (0.60 andlt; R2 andlt; 0.66), and poor relationships for the third applicator (R2 andlt; 0.49). A comparison ofthe actual application rates to the prescription maps generated R2 values between 0.16 and 0.81demonstrating inconsistent VRT applicator performance. Thus, "as-applied" surfaces provide a means toproperly evaluate VRT while enhancing researchers' ability to compare VR management approaches.

Yield monitors have become an indispensable part of precision agriculture systemsbecause of their ability to measure the yield variability. Accurate yield monitor data availabilityis essential for the assessment of farm practices. The current technology of measuring grainyields is prone to errors that can be attributed to mass flow variations caused by the mechanismswithin a grain combine. Because of throughput variations, there are doubts regarding thecorrelation between the mass flow measurement and the actual grain volume produced at aspecific location. Another inaccuracy observed in yield monitor data can be attributed to inexactcut-widths values entered by the machine operator.To effectively address these yield monitor errors, two crop mass flow sensing deviceswere developed and used to correct yield monitor data. The two quantities associated with cropmaterial mass flow that were sensed were tension on the feeder housing drive chain and thehydraulic pressure on the threshing cylinder's variable speed drive. Both sensing approacheswere capable of detecting zero mass flow conditions better than the traditional grain mass flowsensor. The alternative sensors also operate without being adversely affected by materialtransport delays. The feeder housing-based sensor was more sensitive to variations in cropmaterial throughput than the hydraulic pressure sensor. Crop mass flow is not a surrogate forgrain mass flow because of a weak relationship (R2 andlt; 0.60) between the two quantities. The cropmass flow signal does denote the location and magnitude of material throughput variations intothe combine. This delineation was used to redistribute grain mass flow by aligning grain andcrop mass flow transitions using sensor fusion techniques. Significant improvements (?? = 0.05)in yield distribution profile were found after the correction was applied.To address the cut-width entry error, a GIS-based post-processing algorithm wasdeveloped to calculate the true harvest area for each yield monitor data point. Based on theresults of this method, a combine operator can introduce yield calculation errors of 15%. Whenthese two correction methods applied to yield monitor data, the result is yield maps withdramatically improved yield estimates and enhanced spatial accuracy.

In the recent past precision farming has become increasingly popular amongfarmers. However, little has been done to study the business aspect of precision farming,with most research focusing on the production side. This purpose of this thesis is tostudy the feasibility of successfully opening and operating a precision farming firm inKentucky. To determine the feasibility of such a venture a computer model was createdand a producer survey was designed and distributed to farmers in Western and CentralKentucky.The purpose of the computer model was to determine the factors that wouldinfluence the successful operation of a precision farming firm including number of acresserviced per year, pricing of services, the cost of capital to borrow money, and manyother factors. A break-even analysis was performed to determine what kind of annualincreases in business would be required, what price range services should be in, and atwhat interest rate money could be borrowed and a simulated precision farming firm couldstill operate successfully.The producer survey was mailed to 336 farmers in Western and Central Kentuckybecause of their geographical locations and the type of crops that are grown there. Thesurvey response rate was 20 percent and of the 66 surveys that were returned 59 wereappropriate and useful for research. After compiling the results of the surveys,regressions were run to determine any correlation between dependent and independentvariables that affect the adoption rate of precision farming techniques. The results foundthat a negative correlation exists between age adoption rates of precision farming and thata positive correlation exists between farm size and adoption rates of precision farming.After conducting the research, it is believed that given the right economicconditions and management a precision farming firm is very capable of thriving inKentucky. However, the workforce must be very motivated and capable of constantlyrecruiting new clients to adopt precision farming.

The paper describes major features of an unmanned aerial vehicle, designed undersafety and performance requirements for missions of aerial photography and remotesensing in precision agriculture. Unmanned aerial vehicles have vast potential asobservation and data gathering platforms for a wide variety of applications. The goalof the project was to develop a small, low cost, electrically powered, unmanned aerialvehicle designed in conjunction with a payload of imaging equipment to obtainremote sensing images of agricultural fields. The results indicate that this conceptwas feasible in obtaining high quality aerial images.

Through the work of researchers and the development of commercially availableproducts, automated guidance has become a viable option for agricultural producers.Some of the limitations of commercially available technologies are that they onlyautomate one function of the agricultural vehicle and that the systems are proprietary toa single machine model.The objective of this project was to evaluate a controller area network (CAN bus)as the basis of an automated guidance system. The prototype system utilized severalmicrocontroller-driven nodes to act as control points along a system wide CAN bus.Messages were transferred to the steering, transmission, and hitch control nodes from atask computer. The task computer utilized global positioning system data to determinethe appropriate control commands.Infield testing demonstrated that each of the control nodes could be controlledsimultaneously over the CAN bus. Results showed that the task computer adequatelyapplied a feedback control model to the system and achieved guidance accuracy levelswell within the range sought. Testing also demonstrated the system's ability tocomplete normal field operations such as headland turning and implement control.

This thesis examines the economic and environmental impacts that precision agriculture technologies (PATs) can have on the carbon footprint of a grain farm. An analysis is offered using two manuscripts. The first examines the impacts of three PATs and compares the findings to a conventional farming method. It was found that all three PATs investigated showed a potential Pareto improvement over conventional farming. The second manuscript expanded the model used previously to in order to develop a process to construct a carbon efficient frontier (CEF). The model employed examined uniform and variable rate technologies. In addition to the CEF, a marginal abatement cost curve was constructed. Using these curves in a complementary fashion, more accurate information on the adaptive behavior of farmer technology adoption can be gleaned. the information gleaned for the two manuscripts can give both producers and policy makers the analytical tools needed to make more information decisions with regard to economic and environmental feasibility of PATs.

Dirvožemio elektrinio laidumo (EC) matavimas yra vienas iš perspektyviausių ir dažniausiai naudojamų tiksliosios žemdirbystės tyrimų būdų. Tyrimais yra nustatyta tiesioginė jo priklausomybė su dirvožemio granuliometrine sudėtimi. Apibendrinant galima teigti, kad EC yra priemonė, leidžianti išsiaiškinti dirvožemio savybes, nuo kurių priklauso ne tik žemės ūkio, bet ir energetinių augalų, skirtų atsinaujinančios energijos gamybai, derlingumo dėsningumai. Dirvožemio elektrinis laidumas nustatytas mobiliu įrenginiu „Veris 3150 MSP“ (JAV, Veris Technogies Ltd.). Jame įrengta navigacinė sistema. Matavimai atlikti dviejuose dirvožemio pjūviuose (paviršiniame ir giluminiame): nuo dirvožemio paviršiaus iki 30 cm gylio ir 0–90 cm gylyje. Dirvožemio savybių žemėlapiai sudaryti naudojant kompiuterinę programą „SMS Advanced“ JAV, AgLeader Ltd.). Atliktų dirvožemio elektrinio laidumo tyrimų patikimumą įrodo gauta tiesinė tarpusavio priklausomybė (R2 = 0,91) tarp elektrinio laidumo nustatyto įrenginiu „Veris 3150 MSP“ ir elektrinio laidumo nustatyto laboratorijoje. Atlikus dirvožemio granuliometrinės sudėties tyrimus nustatyta, kad didėjant molio (< 2 m) ir dulkių (2–50 m) daliai dirvožemyje, jo elektrinis laidumas didėja. Labai smulkaus smėlio (50–100 m) dalelės įtakos dirvožemio elektriniam laidumui įtakos neturi, o dirvožemyje didėjant dar didesnių (> 100 m) smėlio dalelių daliai – jo elektrinis laidumas mažėja. / Soil electrical conductivity (EC) measurement is one of the most perspective and widely used research methods of precision agriculture. Various studies proved its direct correlation to soil texture. In summary, it can be stated that EC is a measure that allows defining such characteristics of soil that do influence the productivity laws not only of agricultural, but also of energy crops. Soil electrical conductivity was measured using a mobile unit Veris 3150 MSP (USA, Veris Technogies Ltd.) equipped with GPS system. Measurements were performed in two sections of soil depth (shallow and deep): from the surface up to 30 cm depth and 0–90 cm depth. Soil characteristic maps were created using computer program SMS Advanced (USA, AgLeader Ltd.). The reliability of soil electric conductivity research is proved by direct relationship (R2 = 0.91) between electrical conductivity obtained using Veris MSP and that in the laboratory. The analysis of soil texture showed that the increasing part of clay (< 2 m) and silk (2–50 m) in soil increases electrical conductivity of soil. Extremely small particles of sand (50–100 m) do not influence soil electrical conductivity, while higher amount of larger particles (> 100 m) of sand reduces its electrical conductivity.

The feasibility of using optical sensors and non-nitrogen limiting reference crops to determine post-emergent nitrogen fertilizer requirements of canola was evaluated. Normalized difference vegetation index was well suited for estimating yield potential and nitrogen status. Although sensor-based nitrogen management was generally agronomically feasible for canola, the economic benefits of doing so remain uncertain because of the added cost of applying post-emergent nitrogen.

The overall goal of this research is to develop ground-based technologies for disease detection and mapping which can maximize the effectiveness and efficiency of cotton root rot (CRR) treatments. Accurately mapping CRR could facilitate a much more economical solution than treating entire fields. Three cotton fields around CRR-prone areas of Texas have been the sites for three years of data collection. A complete soil apparent electrical conductivity (ECa) survey was conducted for each field with an EM38DD sensor. Multiple linear regression was used to relate physical and chemical soil properties to the ECa values obtained from the EM38DD. The variability in soil ECa measurements can be best accounted for using calcium carbonate levels as well as clay and sand contents in the soil. T-tests were used to determine that soil pH, clay, sand, and inorganic carbon content were significantly related to CRR incidence as determined by aerial images of each location. Spectral data were obtained for freshly picked cotton leaves from healthy, disease-stressed, and dying or dead plants using an ASD VisNIR spectroradiometer. The leaf spectra were evaluated using linear discriminant analysis (LDA), the receiver operator characteristic, and wavelet analysis to relate them to classifications of infection level. It was determined that healthy and infected leaves can be correctly classified 85% of the time based on the spectral data. The results from this study suggest that differences in soil characteristics may not be pronounced enough to accurately map CRR in the soil; however, the precision treatment of CRR may possible using an optoelectronic sensor to diagnose infected plants based on leaf reflectance.