Application of image processing techniques to automate the seed vigor assessment process in soybean seedlings

Seed vigor is a quantitative or qualitative value that describes a seedlot quality based on their growth, giving information of importance in commercial and research areas. A study was performed in order to automate the seed vigor analysis on soybean seedlings, based on digital images captured by a scanner, using the HSV color mode, because it works better than RGB model, thresholding and removing seed area from seedling. The method of transition point identification between hypocotyl and primary root of soybean seedlings showed good results. The work continues in order to find a better way to remove the seed area from seedlings.FAPESP - Proc 06/57900-

Determining the Selectivity of Herbicides and Assessing Their Effect on Plant Roots - A Case Study with Indaziflam and Glyphosate Herbicides

This chapter explores the general aspects of herbicide selectivity on plants, describing the various aspects of the topic, especially the action of herbicides on root crops and presenting a case study with the suggestion of a methodology to evaluate herbicide action on roots in perennial culture and thus determine their selectivity. This study was carried out under field conditions, over a period of four years, where the effect of indaziflam and glyphosate herbicides on roots of Coffee and Citrus plants was evaluated. The results demonstrate that the methodology used to assess the effect of herbicides on the roots was important to validate and qualify safe herbicide selectivity towards crops. Thus, this analysis should be indicated as a routine method for studies to assess the selectivity of herbicides to crops

Distribuição radicular de videiras irrigadas por gotejamento e microaspersão

Grape (Vitis vinifera L.) yield and its quality are dependent of the status of the root system. Root distribution information is also valuable for soil and water management. An analysis of methods to evaluate the root distribution of grapevines for both, drip and microsprinkler irrigation in a Typic Acrustox is presented for the table grape cv. Italia grafted on the rootstock IAC-313, in northeastern Brazil. Measured root parameters using the monolith method were root dry weight (Dw) and root length density (Lv), while root area (Ap) was estimated using the soil profile method in combination with digital image analysis. For both irrigation systems, roots were present to the 1 m soil depth and extended laterally to 1 m distance from the trunk, but grapevines irrigated by microsprinkler showed greater root presence as the distance from the trunk increased. Values of Ap were reasonably well correlated to Dw and Lv. However, correlation values were higher when fractional root distribution was used. The soil profile method in combination with image analysis techniques, allows proper grapevine root distribution evaluation.A produção de uva (Vitis vinifera L.) em termos quantitativos e qualitativos depende do estado das raízes. Além disso, informações sobre a distribuição radicular são úteis para o manejo de solo e água. Por isso, uma análise de métodos para a avaliação da distribuição radicular de videiras cv. Itália / IAC 313 num Latossolo Vermelho Amarelo irrigadas por gotejamento e microaspersão foi realizada em Petrolina – PE e Juazeiro - BA, no Vale do São Francisco. Os parâmetros medidos pelo método do monolito foram a matéria seca (Dw) e densidade de comprimento de raízes (Lv), enquanto a área de raízes (Ap) foi estimada pelo método do perfil de solo combinado com a análise de imagens digitais. Para ambos os sistemas de irrigação, as raízes estiveram presentes até 1 m de profundidade e estenderam-se lateralmente até 1 m de distância do tronco, mas as videiras irrigadas por microaspersão apresentaram uma maior presença de raízes com o aumento da distância do tronco. Os valores de Ap apresentaram uma boa correlação com Dw e Lv, mas essa correlação foi maior quando se utilizou a distribuição fracional de cada parâmetro. O método do perfil auxiliado pela análise de imagem digital permite a avaliação da distribuição radicular

APRENDIZAGEM DE MÁQUINA PARA IDENTIFICAÇÃO DE PLANTAS DE SOJA SOB ATAQUE DE INSETOS USANDO DADOS HIPERESPECTRAIS

A integração entre as áreas de sensoriamento remoto e machine learning tem permitido um avanço na forma de mapeamento de campos agrícolas e monitoramento de culturas. Este trabalho investiga a capacidade de algoritmos de aprendizagem de máquina em classificar plantas de soja sob ataque de insetos, utilizando medidas de espectroscopia de refletância coletadas ao nível foliar. Para tanto, desenvolveu-se testes com diferentes algoritmos utilizando um conjunto de 991 curvas espectrais referentes à planta de soja saudável e sob ataque de pragas, coletadas em oito dias consecutivos. Essas curvas foram medidas pela equipe da EMBRAPA, usando um espectrorradiômetro portátil, que registra no intervalo de 350 a 2500 nm. Tais curvas foram, inicialmente, pré-processadas para a remoção das regiões de absorção atmosférica pelo vapor d’água, e em seguida subdividida em conjunto de treino, validação e teste dos algoritmos de aprendizagem de máquina. Utilizou-se o interpretador Google Collabs e os algoritmos foram inscritos em linguagem Python, utilizando bibliotecas, como a Skit Sklearn. Dentre os algoritmos utilizados, tem-se Random Forest, Decision Tree, Support Vector Machine, Logistic Regression e Extra-Tree. O Extra-tree tem melhor desempenho (F1-score = 80,40%; precision = 81%; recall = 80%) na tarefa proposta. Conclui-se que é possível processar medidas de espectroscopia de refletância com algoritmos de aprendizagem de máquina para se monitorar o ataque por insetos em plantas de soja. Recomenda-se que a abordagem aplicada seja testada em outras culturas

APRENDIZAGEM DE MÁQUINA PARA IDENTIFICAÇÃO DE PLANTAS DE SOJA SOB ATAQUE DE INSETOS USANDO DADOS HIPERESPECTRAIS

A integração entre as áreas de sensoriamento remoto e machine learning tem permitido um avanço na forma de mapeamento de campos agrícolas e monitoramento de culturas. Este trabalho investiga a capacidade de algoritmos de aprendizagem de máquina em classificar plantas de soja sob ataque de insetos, utilizando medidas de espectroscopia de refletância coletadas ao nível foliar. Para tanto, desenvolveu-se testes com diferentes algoritmos utilizando um conjunto de 991 curvas espectrais referentes à planta de soja saudável e sob ataque de pragas, coletadas em oito dias consecutivos. Essas curvas foram medidas pela equipe da EMBRAPA, usando um espectrorradiômetro portátil, que registra no intervalo de 350 a 2500 nm. Tais curvas foram, inicialmente, pré-processadas para a remoção das regiões de absorção atmosférica pelo vapor d’água, e em seguida subdividida em conjunto de treino, validação e teste dos algoritmos de aprendizagem de máquina. Utilizou-se o interpretador Google Collabs e os algoritmos foram inscritos em linguagem Python, utilizando bibliotecas, como a Skit Sklearn. Dentre os algoritmos utilizados, tem-se Random Forest, Decision Tree, Support Vector Machine, Logistic Regression e Extra-Tree. O Extra-tree tem melhor desempenho (F1-score = 80,40%; precision = 81%; recall = 80%) na tarefa proposta. Conclui-se que é possível processar medidas de espectroscopia de refletância com algoritmos de aprendizagem de máquina para se monitorar o ataque por insetos em plantas de soja. Recomenda-se que a abordagem aplicada seja testada em outras culturas

A Review on Deep Learning in UAV Remote Sensing

Deep Neural Networks (DNNs) learn representation from data with an impressive capability, and brought important breakthroughs for processing images, time-series, natural language, audio, video, and many others. In the remote sensing field, surveys and literature revisions specifically involving DNNs algorithms' applications have been conducted in an attempt to summarize the amount of information produced in its subfields. Recently, Unmanned Aerial Vehicles (UAV) based applications have dominated aerial sensing research. However, a literature revision that combines both "deep learning" and "UAV remote sensing" thematics has not yet been conducted. The motivation for our work was to present a comprehensive review of the fundamentals of Deep Learning (DL) applied in UAV-based imagery. We focused mainly on describing classification and regression techniques used in recent applications with UAV-acquired data. For that, a total of 232 papers published in international scientific journal databases was examined. We gathered the published material and evaluated their characteristics regarding application, sensor, and technique used. We relate how DL presents promising results and has the potential for processing tasks associated with UAV-based image data. Lastly, we project future perspectives, commentating on prominent DL paths to be explored in the UAV remote sensing field. Our revision consists of a friendly-approach to introduce, commentate, and summarize the state-of-the-art in UAV-based image applications with DNNs algorithms in diverse subfields of remote sensing, grouping it in the environmental, urban, and agricultural contexts.Comment: 38 pages, 10 figure

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Search for heavy resonances decaying to two Higgs bosons in final states containing four b quarks

A search is presented for narrow heavy resonances X decaying into pairs of Higgs bosons (H) in proton-proton collisions collected by the CMS experiment at the LHC at root s = 8 TeV. The data correspond to an integrated luminosity of 19.7 fb(-1). The search considers HH resonances with masses between 1 and 3 TeV, having final states of two b quark pairs. Each Higgs boson is produced with large momentum, and the hadronization products of the pair of b quarks can usually be reconstructed as single large jets. The background from multijet and t (t) over bar events is significantly reduced by applying requirements related to the flavor of the jet, its mass, and its substructure. The signal would be identified as a peak on top of the dijet invariant mass spectrum of the remaining background events. No evidence is observed for such a signal. Upper limits obtained at 95 confidence level for the product of the production cross section and branching fraction sigma(gg -> X) B(X -> HH -> b (b) over barb (b) over bar) range from 10 to 1.5 fb for the mass of X from 1.15 to 2.0 TeV, significantly extending previous searches. For a warped extra dimension theory with amass scale Lambda(R) = 1 TeV, the data exclude radion scalar masses between 1.15 and 1.55 TeV

Search for supersymmetry in events with one lepton and multiple jets in proton-proton collisions at root s=13 TeV

Peer reviewe