Weed identification in sugarcane plantation through images taken from remotely piloted aircraft (RPA) and kNN classifier

The sugarcane is one of the most important crops in Brazil, the world´s largest sugar producer and the second largest ethanol producer. The presence of weeds in the sugarcane plantation can cause losses up to 90% of the production, caused by the competition for light, water and nutrients, between the crop and the weeds. Usually sugarcane plantations occupy large fields, and due to this, the weeds control is mostly chemical, which is more practical and cheaper than mechanical control. In the chemical control, the dosage and type of herbicides has been calculated by sampling, which causes problems of waste and misapplication of herbicides, since the degree of infestation may be variant from one location to another, as well as the species presents in the plantation. In order to avoid unnecessary waste in the herbicides application, there are some studies about weed identification using images taken from satellites, solution that have proved to have the advantage of covering the whole plantation, solving the problems of sample surveying, nevertheless, this method its dependent of a high weed density to ensure a good pattern recognition and its affected by the influence of clouds in the imagery quality. This work proposes a system for weed identification based on pattern recognition in imagery taken from a Remotely Piloted Aircraft (RPA). The RPA is able to fly at low altitude, so it is possible to take images closer to the plants and make the weed identification even in low infestation levels. In an initial evaluation, the system reached an overall accuracy of 83.1% and kappa coefficient of 0.775, using k-Nearest Neighbors (kNN) classifier.5621121

Fortalecimento de marca empresarial por meio de práticas sustentáveis, marketing digital e tecnologia Blockchain

A adoção de práticas sustentáveis pelas empresas, traz diferencial competitivo e fortalece a marca das organizações que adotam essas práticas, uma vez que a sociedade, sobretudo os consumidores, mas também, o governo, os investidores, os acionistas, os funcionários e os fornecedores, tem valorizado cada vez mais o papel exercido pelas empresas nas questões sociais e ambientais. Isto é resultado de uma sociedade cada vez mais preocupada com a preservação do meio ambiente e com a sustentabilidade, devido à ocorrência cada vez mais frequente de eventos extremos, como tempestades, furacões, inundações e secas. Consequentemente, valorizando as empresas que tem uma postura pró-ativa nas questões ambientais. Nesse sentido, informações sobre a qualidade dos produtos, processos produtivos sustentáveis e origem das matérias-primas utilizadas em sua produção podem agregar valor e auxiliar na abertura de mercados mais exigentes, que aceitam pagar mais por produtos ambientalmente corretos e socialmente justos. Sendo que a confiabilidade das informações apresentadas passa a ser fator primordial. A tecnologia blockchain apresenta-se como ótima solução no quesito integridade dos dados, principalmente pela sua característica de imutabilidade dos dados. Este trabalho descreve um sistema que utiliza tecnologia blockchain para armazenar de forma segura dados de qualidade, processos produtivos e rastreabilidade no intuito de agregar valor ao açúcar mascavo da Usina Granelli. Neste caso de uso, a disponibilização das informações é uma ferramenta de marketing digital para alavancar a venda do produto e fortelecer a marca Granelli

Weed mapping in sugarcane fields by remotely piloted aircraft (RPA)

Orientador: Barbara Janet Teruel MederosTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia AgrícolaResumo: A ocorrência de plantas daninhas afeta negativamente a produtividade das lavouras de cana-de-açúcar e a qualidade do produto colhido. Assim, o controle de plantas daninhas é de grande importância e hoje em dia, geralmente, feito por herbicidas pós-emergentes. No entanto, grandes volumes de aplicação de herbicidas para o controle de plantas daninhas podem contaminar o solo e as águas, prejudicar a saúde de tralhadores rurais, além de elevar os custos de produção. Considerando isso, o mapeamento espacial de plantas daninhas na lavoura pode ser uma ferramenta que auxilie em seu controle, trazendo três principais benefícios: retornos econômicos consideráveis, menor impacto ambiental e redução de risco de aparecimento de plantas resistentes aos herbicidas. No início, os primeiros mapeamentos de plantas daninhas foram feitos a partir de imagens fornecidas por satélites e aviões, embora esses métodos de sensoriamento remoto tenham a vantagem de cobrir grandes áreas, as resoluções espacial e temporal geralmente não são suficientemente boas para a identificação de plantas daninhas. Uma opção para o mapeamento de plantas daninhas é o uso de Aeronaves Remotamente Pilotadas, que fornece alta resolução espacial e temporal. Este trabalho propõe um sistema de mapeamento de plantas daninhas, baseado em técnicas de aprendizado de máquinas, usando imagens RGB tiradas a partir de Aeronaves Remotamente Pilotadas. O sistema de mapeamento de plantas daninhas foi inicialmente testado com três classificadores, sendo a Rede Neural Artificial o classificador de melhor desempenho, alcançando entre 71% e 76% de taxa de acerto e coeficiente Kappa entre 0,65 e 0,72. Os resultados obtidos podem ser considerados bons, uma vez que os coeficientes Kappa ficaram na faixa de boa concordância, ou seja, entre 0,6 e 0,8, devendo-se levar em consideração, ainda, que os experimentos foram realizados em condições reais de campo, nas quais, as plantas passaram por estresse hídrico e ataque de pragasAbstract: The occurrence of weeds in sugarcane fields affects negatively the sugarcane productivity and quality of the harvested product. Thus, the weed control is of great importance and nowadays usually made by post-emergence herbicides. Nevertheless, high volumes of herbicide application for weed control may contaminate the soil and water, harm the health of rural workers and also can raise the production costs. Considering this, a weed mapping can be a tool that aids the weed control and brings three benefits: considerable economic returns, less environmental impact and risk reduction of herbicide resistant weed appearance. In the begining, the weed mapping were provided by satellite and airborne imagery, although these remote sensing methods have the advantage of covering large areas, the spatial and temporal resolution are usually not enough for a good pattern recognition. An option for weed mapping is the use of Remotely Piloted Aircraft, which provides high spatial and temporal resolution. This work proposes a system for weed mapping, based on machine learning techniques using RGB images taken from a Remotely Piloted Aircraft. This weed mapping system were initially tested by three classifiers, where the best classifier was the Artificial Neural Network, which achieved an overall accuracy rates between 71% and 76% and kappa coefficient between 0.65 and 0.72. The results obtained can be considered good, considering that the Kappa coefficients were in the range of good agreement, that is, between 0.6 and 0.8, and it should be taken into account that the experiments were carried out under real field conditions, in which the plants underwent water stress and pest attackDoutoradoMaquinas AgricolasDoutor em Engenharia Agrícol

Computational biology tools for identifying specific ligand binding residues for novel agrochemical and drug design

The term "agrochemicals" is used in its generic form to represent a spectrum of pesticides, such as insecticides, fungicides or bactericides. They contain active components designed for optimized pest management and control, therefore allowing for economically sound and labor efficient agricultural production. A "drug" on the other side is a term that is used for compounds designed for controlling human diseases. Although drugs are subjected to much more severe testing and regulation procedures before reaching the market, they might contain exactly the same active ingredient as certain agrochemicals, what is the case described in present work, showing how a small chemical compound might be used to control pathogenicity of Gram negative bacteria Xylella fastidiosa which devastates citrus plantations, as well as for control of, for example, meningitis in humans. It is also clear that so far the production of new agrochemicals is not benefiting as much from the in silico new chemical compound identification/discovery as pharmaceutical production. Rational drug design crucially depends on detailed knowledge of structural information about the receptor (target protein) and the ligand (drug/agrochemical). The interaction between the two molecules is the subject of analysis that aims to understand relationship between structure and function, mainly deciphering some fundamental elements of the nanoenvironment where the interaction occurs. In this work we will emphasize the role of understanding nanoenvironmental factors that guide recognition and interaction of target protein and its function modifier, an agrochemical or a drug. The repertoire of nanoenvironment descriptors is used for two selected and specific cases we have approached in order to offer a technological solution for some very important problems that needs special attention in agriculture: elimination of pathogenicity of a bacterium which is attacking citrus plants and formulation of a new fungicide. Finally, we also briefly describe a workflow which might be useful when research requires that model structures of target proteins are firstly generated (starting from genome sequences), followed by identification of ligand-target sites at the surface of those modeled structures, then application of procedures that adequately prepare both protein and ligand structures (the latter also involving filtration that satisfies acceptable adsorption/desorption/metabolism/excretion/toxicity [ADMET] parameters) for virtual high throughput screening (involving docking of ligands to indicated sites) and terminating by ranking of best pairs: target protein with selected ligand.The term “agrochemicals” is used in its generic form to represent a spectrum of pesticides, such as insecticides, fungicides or bactericides. They contain active components designed for optimized pest management and control, therefore allowing for economi16870171