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.

The collection of crop yield data has become much easier with the introduction of technologies such as the Global Positioning System (GPS), ground-based yield sensors, and Geographic Information Systems (GIS). This explosive growth and widespread use of spatial data has challenged the ability to derive useful spatial knowledge. In addition, outlier detection as one important pre-processing step remains a challenge because the technique and the definition of spatial neighbourhood remain non-trivial, and the quantitative assessments of false positives, false negatives, and the concept of region outlier remain unexplored. The overall aim of this study is to evaluate different spatial outlier detection techniques in terms of their accuracy and computational efficiency, and examine the performance of these outlier removal techniques in a site-specific management context. In a simulation study, unconditional sequential Gaussian simulation is performed to generate crop yield as the response variable along with two explanatory variables. Point and region spatial outliers are added to the simulated datasets by randomly selecting observations and adding or subtracting a Gaussian error term. With simulated data which contains known spatial outliers in advance, the assessment of spatial outlier techniques can be conducted as a binary classification exercise, treating each spatial outlier detection technique as a classifier. Algorithm performance is evaluated with the area and partial area under the ROC curve up to different true positive and false positive rates. Outlier effects in on-farm research are assessed in terms of the influence of each spatial outlier technique on coefficient estimates from a spatial regression model that accounts for autocorrelation. Results indicate that for point outliers, spatial outlier techniques that account for spatial autocorrelation tend to be better than standard spatial outlier techniques in terms of higher sensitivity, lower false positive detection rate, and consistency in performance. They are also more resistant to changes in the neighbourhood definition. In terms of region outliers, standard techniques tend to be better than spatial autocorrelation techniques in all performance aspects because they are less affected by masking and swamping effects. In particular, one spatial autocorrelation technique, Averaged Difference, is superior to all other techniques in terms of both point and region outlier scenario because of its ability to incorporate spatial autocorrelation while at the same time, revealing the variation between nearest neighbours. In terms of decision-making, all algorithms led to slightly different coefficient estimates, and therefore, may result in distinct decisions for site-specific management. The results outlined here will allow an improved removal of crop yield data points that are potentially problematic. What has been determined here is the recommendation of using Averaged Difference algorithm for cleaning spatial outliers in yield dataset. Identifying the optimal nearest neighbour parameter for the neighbourhood aggregation function is still non-trivial. The recommendation is to specify a large number of nearest neighbours, large enough to capture the region size. Lastly, the unbiased coefficient estimates obtained with Average Difference suggest it is the better method for pre-processing spatial outliers in crop yield data, which underlines its suitability for detecting spatial outlier in the context of on-farm research.

Doctor of Philosophy / Precision Agriculture (PA) strives towards holistic production and environmental management. A fundamental research challenge is the continuous expansion of ideas about how PA can contribute to sustainable agriculture. Some associated pragmatic research challenges include quantification of spatio-temporal variation of crop yield; crop growth simulation modelling within a PA context and; evaluating long-term financial and environmental outcomes from site-specific crop management (SSCM). In Chapter 1 literature about managing whole farms with a mind towards sustainability was reviewed. Alternative agricultural systems and concepts including systems thinking, agro-ecology, mosaic farming and PA were investigated. With respect to environmental outcomes it was found that PA research is relatively immature. There is scope to thoroughly evaluate PA from a long-term, whole-farm environmental and financial perspective. Comparatively, the emphasis of PA research on managing spatial variability offers promising and innovative ways forward, particularly in terms of designing new farming systems. It was found that using crop growth simulation modelling in a PA context is potentially very useful. Modelling high-resolution spatial and temporal variability with current simulation models poses a number of immediate research issues. This research focused on three whole farms located in Australia that grow predominantly grains without irrigation. These study sites represent three important grain growing regions within Australia. These are northern NSW, north-east Victoria and South Australia. Note-worthy environmental and climatic differences between these regions such as rainfall timing, soil type and topographic features were outlined in Chapter 2. When considering adoption of SSCM, it is essential to understand the impact of temporal variation on the potential value of managing spatial variation. Quantifying spatiotemporal variation of crop yield serves this purpose; however, this is a conceptually and practically challenging undertaking. A small number of previous studies have found that the magnitude of temporal variation far exceeds that of spatial variation. Chapter 3 of this thesis dealt with existing and new approaches quantifying the relationship between spatial and temporal variability in crop yield. It was found that using pseudo cross variography to obtain spatial and temporal variation ‘equivalents’ is a promising approach to quantitatively comparing spatial and temporal variation. The results from this research indicate that more data in the temporal dimension is required to enable thorough analysis using this approach. This is particularly relevant when questioning the suitability of SSCM. Crop growth simulation modelling offers PA a number of benefits such as the ability to simulate a considerable volume of data in the temporal dimension. A dominant challenge recognised within the PA/modelling literature is the mismatch between the spatial resolution of point-based model output (and therefore input) and the spatial resolution of information demanded by PA. This culminates into questions about the conceptual model underpinning the simulation model and the practicality of using point-based models to simulate spatial variability. iii The ability of point-based models to simulate appropriate spatial and temporal variability of crop yield and the importance of soil available water capacity (AWC) for these simulations were investigated in Chapter 4. The results indicated that simulated spatial variation is low compared to some previously reported spatial variability of real yield data for some climate years. It was found that the structure of spatial yield variation was directly related to the structure of the AWC and interactions between AWC and climate. It is apparent that varying AWC spatially is a reasonable starting point for modelling spatial variation of crop yield. A trade-off between capturing adequate spatio-temporal variation of crop yield and the inclusion of realistically obtainable model inputs is identified. A number of practical solutions to model parameterisation for PA purposes are identified in the literature. A popular approach is to minimise the number of simulations required. Another approach that enables modelling at every desired point across a study area involves taking advantage of high-resolution yield information from a number of years to estimate site-specific soil properties with the inverse use of a crop growth simulation model. Inverse meta-modelling was undertaken in Chapter 5 to estimate AWC on 10- metre grids across each of the study farms. This proved to be an efficient approach to obtaining high-resolution AWC information at the spatial extent of whole farms. The AWC estimates proved useful for yield prediction using simple linear regression as opposed to application within a complex crop growth simulation model. The ability of point-based models to simulate spatial variation was re-visited in Chapter 6 with respect to the exclusion of lateral water movement. The addition of a topographic component into the simple point-based yield prediction models substantially improved yield predictions. The value of these additions was interpreted using coefficients of determination and comparing variograms for each of the yield prediction components. A result consistent with the preceding chapter is the importance of further validating the yield prediction models with further yield data when it becomes available. Finally, some whole-farm management scenarios using SSCM were synthesised in Chapter 7. A framework that enables evaluation of the long-term (50 years) farm outcomes soil carbon sequestration, nitrogen leaching and crop yield was established. The suitability of SSCM across whole-farms over the long term was investigated and it was found that the suitability of SSCM is confined to certain fields. This analysis also enabled identification of parts of the farms that are the least financially and environmentally viable. SSCM in conjunction with other PA management strategies is identified as a promising approach to long-term and whole-farm integrated management.

Made available in DSpace on 2014-06-11T19:26:55Z (GMT). No. of bitstreams: 0 Previous issue date: 2009-06-29Bitstream added on 2014-06-13T20:27:33Z : No. of bitstreams: 1 bedutti_ad_me_sjrp.pdf: 1999751 bytes, checksum: 437383410c3f4cc28c116c28e9f7054a (MD5) / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / O controle de plantas daninhas constitui um dos principais desafios no cultivo de área agrícolas. Quando presentes em quantidades descontroladas, estas plantas geram a diminuição na produtividade e ocasionam perdas significativas e indesejáveis. As perdas, aliadas ao alto custo de controle, motivam o desenvolvimento de ferramentas no auxílio a tomada de decisão, como mapas da distribuição de daninhas, visando o manejo localizado de herbicidas. Neste trabalho, considera-se a aplicação de um modelo espaço-temporal para a construção de mapas da distribuição de sementes de plantas daninhas em uma área agrícola de plantação de milho (Zea mays). Foram analisados dados reais, para as espécies Digitaria ciliaris, Euphorbia heterophilla L., Cenchrus echinatus L. e Bidens Pilosa L. e tamb´em dados simulados. O modelo envolve a combinação de estimação por krigagem e o filtro de Kalman. / The control of weeds is a major challenge in cultivation of agricultural areas. When present in uncontrolled quantities, these plants generate a decrease in productivity and cause significant and undesirable losses. The losses, combined with the high cost of control, motivate the development of tools to aid in taking decision, as maps of distribution of weed, to located handling of herbicides. In this work, was considered the application of a spatial-temporal model for construction of distribution maps of seed weeds in an agricultural area of corn plantation (Zea mays). Were analyzed real data, for the species Digitaria ciliaris, Euphorbia heterophilla L., Cenchrus echinatus L. and Bidens Pilosa L., and also simulated data. The model involves a combination of kriging estimation and Kalman filter.

Made available in DSpace on 2015-03-26T13:32:18Z (GMT). No. of bitstreams: 1 texto completo.pdf: 3277635 bytes, checksum: d8f87471e2b1a001fcaef930bd261f59 (MD5) Previous issue date: 2013-02-28 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Agribusiness covers a vast chain of production activities. Can be noted as one of the activities of this branch precision agriculture that relies often georeferenced information to describe through the spatial maps of certain areas. One technique that has high importance in this context is the Geostatistics, it provides obtaining thematic maps, considering the spatial dependence structure of the phenomenon under study. So this study presents a procedure to identify subareas for planting considering the spatial dependence of the variable of interest without the need for categorization. For both methodologies are addressed map algebra with the intention of incorporating measures of statistical association locally. In this way, we used the method of ordinary kriging in this Geostatistics and to measure the agreement of thematic maps used the Kappa index and linear correlation coefficient global and local. As main results can be noted that the agreement between the maps of localized form performed by Pearson's correlation shows that the spatial dependence models used ordinary kriging in place exerts influence in defining the thematic maps used in precision agriculture, aspect one that is not picked up used (in case agricultural) encodings of interpolated values (management zones) and draw up these global indices of agreement. / O agronegócio abrange uma vasta cadeia de atividades produtivas. Pode-se destacar como uma das atividades deste ramo a agricultura de precisão que se vale frequentemente de informações georreferenciadas para descrever por meio de mapas a variabilidade espacial de determinadas áreas. Uma técnica que apresenta relevada importância para este contexto é a Geoestatística, pois proporciona a obtenção de mapas temáticos, considerando a estrutura de dependência espacial do fenômeno em estudo. Assim este estudo apresenta um procedimento para identificar subáreas destinadas ao plantio considerando a dependência espacial da variável de interesse sem a necessidade de categorização. Para tanto são abordadas metodologias de álgebra de mapas com a intenção de incorporar medidas de associação estatística de forma local. Desde modo, foi utilizado o método de Krigagem Ordinária presente na Geoestatística e para medir a concordância dos mapas temáticos utilizou-se o índice Kappa e a coeficiente de correlação linear global e local. Como resultados principais pode-se destacar que a concordância entre os mapas de forma localizada evidencia que os modelos de dependência espacial utilizados na Krigagem Ordinária exercem influência local na definição dos mapas temáticos utilizados na agricultura de precisão, aspecto esse que não é captado se utilizadas (para o caso agrícola), codificações dos valores interpolados (zonas de manejo) e destas extrair-se índices globais de concordância.