The objective of this whole study was to evaluate a LiDAR sensor for high-resolution remote sensing in agriculture. A linear motion system was developed to precisely control the dynamics of LiDAR sensor in effort to remove uncertainty in the LiDAR position/velocity while under motion. A user control interface was developed to operate the system under different velocity profiles and log LiDAR data synchronous to the motion of the system. The LiDAR was then validated using multiple test targets with five different velocity profiles to determine the effect of sensor velocity and height above a target on measurement error. The results indicated that the velocity of the LiDAR was a significant factor affecting the error and standard deviation of the LiDAR measurements, although only by a small margin. Then the concept of modeling the alfalfa using the linear motion system was introduced. Two plots of alfalfa were scanned and processed to extract height and volume and was compared with photogrammetric and field measurements. Insufficient alfalfa plots were scanned which prevented any statistical analysis from being used to compare the different methods. However, the comparison between LiDAR and photogrammetric data showed some promising results which may be further replicated in the future.

Orientador: Kleber Pereira Lanças / Banca: Ulisses Rocha Antuniassi / Banca: Maura Seiko Tsutsui Esperancini / Banca: Antonio Gabriel Filho / Banca: Alberto Kazushi Nagaoka / Abstract: Precision agriculture is the integration among several technologies in order to reduce the agricultural losses, increasing the economical return and reducing the environmental impacts. So, the computer based data acquisition and storage for field data is very important for precision agriculture development. The automatic data storage allows to eliminate the human reading and type errors, to reduce data losses and no synchronism readings among different sensors, in addition assure different frequency readings with precise intervals This present research had for main objective to develop and evaluate a data acquisition and storage system based on personal computer for analogical sensor (load cells and potentiometer) and GPS receiver (Global Positional System). The main objective of this research was to develop and evaluate a low cost acquisition and storage data system for analogical sensors (load cells and potentiometer) and GPS receiver and also realize a comparative economical analysis between the actual data acquisition system (Micrologger CR10X) and the proposal system. / Doutor

This research develops a methodology for determining farm scale land managementunits (LMUs) using soil sampling data, high resolution digital multi-spectral imagery (DMSI) and a digital elevation model (DEM). The LMUs are zones within a paddock suitable for precision agriculture which are managed according to their productive capabilities. Soil sampling and analysis are crucial in depicting landscape characteristics, but costly. Data based on DMSI and DEM is available cheaply and at high resolution.The design and implementation of a two-stage methodology using a spatiallyweighted multivariate classification, for delineating LMUs is described. Utilising data on physical and chemical soil properties collected at 250 sampling locations within a 1780ha farm in Western Australia, the methodology initially classifies sampling points into LMUs based on a spatially weighted similarity matrix. The second stage delineates higher resolution LMU boundaries using DMSI and topographic variables derived from a DEM on a 10m grid across the study area. The method groups sample points and pixels with respect to their characteristics and their spatial relationships, thus forming contiguous, homogenous LMUs that can be adopted in precision agricultural applications. The methodology combines readily available and relatively cheap high resolution data sets with soil properties sampled at low resolution. This minimises cost while still forming LMUs at high resolution.The allocation of pixels to LMUs based on their DMSI and topographic variables has been verified. Yield differences between the LMUs have also been analysed. The results indicate the potential of the approach for precision agriculture and the importance of continued research in this area.

Soil landscape characterization into landform elements for precision agriculture has become an important issue. As soil properties and crop yields change over the landscape, delineating landform elements as a basis for site-specific application of crop inputs has become a reality. Two different methods of delineating landform elements from agricultural fields were tested and compared. The first method delineated landform elements from digital elevation maps with the use of the LandMapR(tm) software, the second method delineated classes from IKONOS high resolution panchromatic images using an unsupervised classification algorithm. The LandMapR(tm) model delineated landform elements from true elevation data collected in the field and was considered the reference dataset to which the image classification maps were compared to. The IKONOS imagery was processed using a combination of one filtering algorithm and one unsupervised classification method prior to being compared to the classified DEM. A total of 20 filtering algorithms and two unsupervised methods were used for each of the five study sites. The study sites consisted of four agricultural fields covered with crop stubble and one field in summer fallow. Image classification accuracy assessment was reported as overall, producer’s and user’s accuracy as well as Kappa statistic. Results showed that filtering algorithms and classification methods had no effects on image classification accuracies. Highest classification accuracy of image map to landform element map comparison achieved for all study sites was 17.9 %. Classification accuracy was affected by the heterogeneity of the ground surface cover found in each field. However, the classification accuracy of the fallow field was not superior to the stubble fields. / May 2006

Soil landscape characterization into landform elements for precision agriculture has become an important issue. As soil properties and crop yields change over the landscape, delineating landform elements as a basis for site-specific application of crop inputs has become a reality. Two different methods of delineating landform elements from agricultural fields were tested and compared. The first method delineated landform elements from digital elevation maps with the use of the LandMapR(tm) software, the second method delineated classes from IKONOS high resolution panchromatic images using an unsupervised classification algorithm. The LandMapR(tm) model delineated landform elements from true elevation data collected in the field and was considered the reference dataset to which the image classification maps were compared to. The IKONOS imagery was processed using a combination of one filtering algorithm and one unsupervised classification method prior to being compared to the classified DEM. A total of 20 filtering algorithms and two unsupervised methods were used for each of the five study sites. The study sites consisted of four agricultural fields covered with crop stubble and one field in summer fallow. Image classification accuracy assessment was reported as overall, producer’s and user’s accuracy as well as Kappa statistic. Results showed that filtering algorithms and classification methods had no effects on image classification accuracies. Highest classification accuracy of image map to landform element map comparison achieved for all study sites was 17.9 %. Classification accuracy was affected by the heterogeneity of the ground surface cover found in each field. However, the classification accuracy of the fallow field was not superior to the stubble fields.

Soil landscape characterization into landform elements for precision agriculture has become an important issue. As soil properties and crop yields change over the landscape, delineating landform elements as a basis for site-specific application of crop inputs has become a reality. Two different methods of delineating landform elements from agricultural fields were tested and compared. The first method delineated landform elements from digital elevation maps with the use of the LandMapR(tm) software, the second method delineated classes from IKONOS high resolution panchromatic images using an unsupervised classification algorithm. The LandMapR(tm) model delineated landform elements from true elevation data collected in the field and was considered the reference dataset to which the image classification maps were compared to. The IKONOS imagery was processed using a combination of one filtering algorithm and one unsupervised classification method prior to being compared to the classified DEM. A total of 20 filtering algorithms and two unsupervised methods were used for each of the five study sites. The study sites consisted of four agricultural fields covered with crop stubble and one field in summer fallow. Image classification accuracy assessment was reported as overall, producer’s and user’s accuracy as well as Kappa statistic. Results showed that filtering algorithms and classification methods had no effects on image classification accuracies. Highest classification accuracy of image map to landform element map comparison achieved for all study sites was 17.9 %. Classification accuracy was affected by the heterogeneity of the ground surface cover found in each field. However, the classification accuracy of the fallow field was not superior to the stubble fields.

This thesis concerns Precision Agriculture (PA) technology which involves methods developed to optimise grain yield by examining data quality and modelling protein/yield relationship of wheat and sorghum fields in central and southern Queensland. An important part of developing strategies to optimisise grain yield is the understanding of PA technology. This covers major aspects of PA which includes all the components of Site- Specific Crop Management System (SSCM). These components are 1. Spatial referencing, 2. Crop, soil and climate monitoring, 3. Attribute mapping, 4. Decision suppport systems and 5. Differential action. Understanding how all five components fit into PA significantly aids the development of data analysis methods. The development of PA is dependent on the collection, analysis and interpretation of information. A preliminary data analysis step is described which covers both non-spatial and spatial data analysis methods. The non-spatial analysis involves plotting methods (maps, histograms), standard distribution and statistical summary (mean, standard deviation). The spatial analysis covers both undirected and directional variogram analyses. In addition to the data analysis, a theoretical investigation into GPS error is given. GPS plays a major role in the development of PA. A number of sources of errors affect the GPS and therefore effect the positioning measurements. Therefore, an understanding of the distribution of the errors and how they are related to each other over time is needed to complement the understanding of the nature of the data. Understanding the error distribution and the data give useful insights for model assumptions in regard to position measurement errors. A review of filtering methods is given and new methods are developed, namely, strip analysis and a double harvesting algoritm. These methods are designed specifically for controlled traffic and normal traffic respectively but can be applied to all kinds of yield monitoring data. The data resulting from the strip analysis and double harvesting algorithm are used in investigating the relationship between on-the-go yield and protein. The strategy is to use protein and yield in determining decisions with respect to nitrogen managements. The agronomic assumption is that protein and yield have a significant relationship based on plot trials. We investigate whether there is any significant relationship between protein and yield at the local level to warrent this kind of assumption. Understanding PA technology and being aware of the sources of errors that exist in data collection and data analysis are all very important in the steps of developing management decision strategies.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Soy is one of the products of most relevance to the Brazilian economy. Estimating soybean productivity through remote sensing is a potential tool for precision farming, qualifying and quantifying the productive potential of crops. The main objective of the work was to relate the data obtained through field from radiometric dates with the productivity of soybean cultivation and validate the data obtained through remote sensing platforms orbital (CBERS and LANDSAT) with the use of vegetation index. The study area is located at the Federal University of Santa Maria, with a total area of 16.14 hectares. Readings were made in each of the 15 points of working with the grid Espectrorradiometer. With the field data and Satellite images of vegetation indices were calculated. In 2009/2010 the best multiple regression models found to have been for the groups of vegetation Indices 1 (CRI, Near-Infraredt B4, REP VARI and WBI), 4 (CRI, REP, NDMI, VARI and SAVI) and 11 (Red B3, SAVI, REP and VARI) where the coefficients of determination and determination adjusted reached 97.70% and values 96.40%; 98.00% and 96.30% and 97.72% and 96.41% for groups 1, 4 and 11 respectively, and have low values of standard deviation. Showing that the combination of vegetation index of the groups in question can be used to estimate crop with good accuracy. It is important to highlight that all groups had good correlations with soybean productivity with 43 days after planting. The multiple regression analysis and Stepwise Backward with the vegetation Indices calculated with data from LANDSAT images of dates 24/01, 09/14/02 and 04, 2010, did not show significant values for any regressions. / A soja é um dos produtos de maior relevância para a economia brasileira. A estimativa de produtividade de soja por meio de sensoriamento remoto é uma ferramenta potencial para agricultura de precisão, qualificando e quantificando o potencial produtivo da lavoura. O objetivo principal do trabalho foi relacionar os dados obtidos através de radiometria de campo com a produtividade do cultivo da soja e validar os dados obtidos através de plataformas de sensoriamento remoto orbital (CBERS e LANDSAT) com a utilização de índices de vegetação. A área de estudo situa-se na Universidade Federal de Santa Maria, com área total de 16,14 hectares. Foram feitas leituras com o Espectrorradiômetro, em cada um dos 15 pontos da grade de trabalho. Com os dados de campo e das imagens dos Satélites foram calculados os Índices de Vegetação. Na Safra 2009/2010 os melhores modelos encontrados para Regressão Múltipla foram para os grupos de Índices de Vegetação 1 (CRI, IV Próximo B4, REP VARI e WBI) , 4 (CRI, REP, NDMI, VARI e SAVI) e 11 (Vermelho B3, SAVI, REP e VARI) onde os coeficientes de determinação e de determinação ajustado chegaram a valores de 97,70% e 96,40%; 98,00% e 96,30% e 97,72% e 96,41% para os grupos 1, 4 e 11, respectivamente, e apresentaram valores baixos de desvio padrão. Mostraram que a combinação dos índices de vegetação dos grupos em questão pode ser utilizada para estimativa de safra com boa precisão. É importante destacar que todos os grupos tiveram boas correlações com a produtividade para soja com 43 dias após o plantio. As análises de Regressão Múltipla e Stepwise Backward com os Índices de Vegetação, calculados com os dados das imagens do LANDSAT das datas 24/01, 09/02 e 14/04 de 2010, não apresentaram valores significativos para nenhuma das regressões.

Soybean [Glycine max (L.) Merrill] is the most important crop grown in Brazil and worldwide. Nevertheless, the yield is still limited by several issues in which the damages by caterpillars and stink bugs are main ones if not controlled efficiently. The pest control is usually achieved by insecticide application, which results in an increased production cost, disturb biological control, and causes contamination in harvest and environment, so it is necessary to develop news techniques aiming to reduce the amount of sprayed insecticides. The preset study was carried out in five soybean grown areas during 2010/11 and 2011/12. This experiment aimed to analyze the spatial and temporal distribution in order to perform site-specific control of caterpillars and stink bugs by using agriculture-precision tools for mapping and control of insects. In addition, the technical and economic effects of this approach were surveyed. The first chapter shows the site-specific control for caterpillars while the chapter two for stink bugs. The population of soybean caterpillars can be suppressed by site-specific approach where the population exceeds the economic threshold and therefore saving insecticide. The site-specific control for stink bugs in soybean allows save insecticide and decreases the bugs population, but causes outbreaks far beyond the economic threshold. Economically speaking, the site-specific control of caterpillars and stink bugs is possible, but requires further studies to optimize the sampling procedure. / A soja [Glycine max (L.) Merrill] é uma cultura de grande expressão no Brasil e no mundo, no entanto a sua produtividade ainda é limitada por diversos fatores, onde destacam-se os danos significativos causados pelas lagartas e percevejos quando não manejadas eficientemente. O controle destes insetos é geralmente feito com aplicações de inseticidas, o que ocasiona o aumento dos custos da lavoura, prejudica o controle biológico pela morte de inimigos naturais, e deixa resíduos tóxicos no produto colhido e no ambiente, por isso, se faz necessário desenvolver técnicas visando a sua utilização mais eficiente, que resulte em menores quantidades aplicadas. Este estudo foi realizado em cinco áreas de cultivo com soja nas safras 2010/11 e 2011/12 objetivando analisar a distribuição espacial e temporal e realizar o controle localizado de lagartas e percevejos em soja, utilizando técnicas de agricultura de precisão para o mapeamento e controle dos insetos. Também foi realizada a análise técnica e econômica deste sistema de manejo. O primeiro capítulo apresenta o estudo do controle localizado de lagartas e o segundo capítulo o estudo do controle localizado de percevejos em soja. A população de lagartas da soja pode ser controlada de forma localizada nos pontos onde a população ultrapassa o nível de controle, proporcionando economia de inseticida. O controle localizado de percevejos em soja permite economia de inseticida e reduz a população, mas não impede a reinfestação da área acima do nível de controle. Em termos econômicos, o controle localizado de lagartas e percevejos é viável, porém são necessários estudos para aperfeiçoar o sistema de amostragem.

Orientador: Morel de Passos e Carvalho / Banca: Marcelo Andreotti / Banca: Zigomar Menezes de Souza / Resumo: A cana-de-açúcar, principal fonte de matéria prima destinada à produção do etanol combustível utilizado na frota automobilística, desempenha imprescindível papel frente à matriz energética nacional. Assim, durante o ano agrícola 2008/09, na Fazenda Caiçara, pertencente à Usina Vale do Paraná S/A Álcool e Açúcar (20°28'10'' S; 50°49'20'' W), no município de Suzanápolis (SP), foram estudadas as correlações, de Pearson e espaciais, da produtividade de cana-de-açúcar com atributos físico-químicos de um ARGISSOLO VERMELHO Eutrófico (Tropustalf típico). O objetivo foi analisar aquela que melhor explicasse o aumento da referida produtividade agrícola. A variedade de cana-de-açúcar pesquisada foi a SP 79 1011, instalando-se uma rede geoestatística com 120 pontos amostrais, num talhão de 14,60 ha (418m x 349 m) e declive uniforme de 0,056 m m-1. Os atributos pesquisados foram: a) da planta: produtividade de colmos por hectare (PRO), volume de colmos por hectare (VOL), população de plantas por metro quadrado (POP), açúcares totais recuperáveis (ATR), sólidos solúveis totais (BRI), sacarose (POL), pureza (PUR) e o teor de fibras (FIB); e b) do solo: resistência mecânica à penetração (RP), umidade gravimétrica (UG), P, MO, pH, K, Ca, Mg, H+Al, Al, S, T, V%, m%, K/T, Ca/T e Mg/T, coletados nas profundidades de 0-0,20 m e 0,20-0,40 m. Pela análise das correlações diretas, da produtividade de cana-de-açúcar com a população de plantas e com o teor de matéria orgânica do solo, pôde-se concluir que deverá haver uma população de 14,6 plantas por metro quadrado, assim como, o teor de 23 g dm-3 de matéria orgânica, para que se obtenha a produtividade máxima de 104 t ha-1. Por outro lado, a correlação negativa entre o teor de matéria orgânica e a resistência à penetração indicou a necessidade de se empregar um manejo do solo que proporcione... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The sugar-cane, principal source of raw material destined to ethylic alcohol fuel of the motor-cars fleet, it has an important role in the Brazilian energy power generation. The relationships (Pearson and spatial) between the sugar-cane productivity and physic-chemical attributes were analyzed, in the agriculture year 2008/09, in a Tropustalf Typic of the Caiçara Farm / Usina Vale do Paraná S/A Álcool e Açúcar, in Suzanápolis County, Sao Paulo State, Brazil (20°28'10'' S; 50°49'20'' W). The purpose was to study that better one will explain the improve of the agricultural productivity. The sugar-cane variety analyzed was the SP 79 1011, in a geostatistical grid with 120 sampling points, in an area of 14.60 ha (418 m x 349 m) and homogeneous slope of 0.056 m m-1. The researched attributes were: a) plant: productivity of stem per hectare (PRO), volume of stem per hectare (VOL), stand of plants per square meter (STP), recoverable total sugar (ATR), total soluble solid (BRI), sucrose (POL), pureness (PUR), and fibre contents (FIB); and b) soil: cone index (CON), gravimetric moisture (GRM), P, OM, pH, K, Ca, Mg, H+Al, Al, BS, CEC, V%, m%, K/CEC, Ca/CEC and Mg/CEC, collected in the soil layers of 0-0.20 m and 0.20-0.40 m. Through investigation of the direct relationships, among the sugar-cane productivity with the stand of plant and the soil organic matter, it was concluded that will must been a stand with 14.6 plants per square meter, and the organic matter content of 23 g dm -3, at to get the maximum productivity of 104 t ha-1. Otherwise, the negative relationship between the organic matter and the cone index showed to be necessary to utilize a soil management that improve this organic clay fraction of soil and, thus, to preserve a good environmental to the sugar-cane. Spatially, the soil pH showed to be a potent indicator of its chemical quality. So, with the purpose to support... (Complete abstract click electronic access below)