Parallel processing applied to image mosaic generation

The automatic construction of large mosaics obtained from high resolution digital images is an area of great importance, with applications in different areas. In agriculture, the requirements of cartographic accuracy of mosaics of annual or perennial crops are not so high, but the speed in obtaining them is the most critical factor. The efficiency in decision making is related to the obtaining faster and more accurate information, especially in the control of pests, diseases or fire control. This project proposes a methodology based on SIFT Transform and parallel processing to build mosaics automatically, using high resolution agricultural aerial images. Build mosaics with high resolution images requires high computational effort for processing them. To treat the problem of computational effort, the standard OpenMP of parallel processing was used to accelerate the process and results are presented for a computer with 2, 4 and 8 threads

Seasonal variation and host sex affect bat–bat fly interaction networks in the Amazonian savannahs

Bats are the second-most diverse group of mammals in the world, and bat flies are their main parasites. However, significant knowledge gaps remain regarding these antagonistic interactions, especially since diverse factors such as seasonality and host sex can affect their network structures. Here, we explore the influence of such factors by comparing species richness and composition of bat flies on host bats, as well as specialization and modularity of bat–bat fly interaction networks between seasons and adult host sexes. We captured bats and collected their ectoparasitic flies at 10 sampling sites in the savannahs of Amapá State, northeastern region of the Brazilian Amazon. Despite female bats being more parasitized and recording greater bat fly species richness in the wet season, neither relationship was statistically significant. The pooled network could be divided into 15 compartments with 54 links, and all subnetworks comprised >12 compartments. The total number of links ranged from 27 to 48 (for the dry and wet seasons, respectively), and female and male subnetworks had 44 and 41 links, respectively. Connectance values were very low for the pooled network and for all subnetworks. Our results revealed higher bat fly species richness and abundance in the wet season, whereas specialization and modularity were higher in the dry season. Moreover, the subnetwork for female bats displayed higher specialization and modularity than the male subnetwork. Therefore, both seasonality and host sex contribute in different ways to bat–bat fly network structure. Future studies should consider these factors when evaluating bat–bat fly interaction networksP.M. was supported by a master’s scholarship and currently, is supported by doctoral scholarships from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Brazil (process number 88887.662021/2022- 00). B.S.X. was supported by doctoral scholarships from CAPES, Brazil. W.D.C. was supported by post-doctoral funding (PNPD/CAPES) until early 2020. Currently, W.D.C. is supported by “Ayudas Maria Zambrano” (CA3/ RSUE/2021-00197), funded by the Spanish Ministry of Universities. G.L.U. was supported by Paraiba State Research Foundation (FAPESQ) under a doctoral scholarship from Grant No. 518/18 and by PDPG-Amazônia Legal (process number 88887.834037/2023-00). G.G. was supported by CNPq (process number 306216/2018) and Universidade Federal de Mato Grosso do Sul. J.J.T. received a research productivity scholarship from CNPq (process number 316281/2021-22

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Dados importantes para o acompanhamento de uma área agrícola podem ser avaliados através de imagens aéreas. Dentre estes, destaca-se como um dos mais significativos, a identificação e a classificação da cobertura do solo. A grande dificuldade reside na não disponibilidade de metodologias apropriadas para a análise e a classificação dos padrões de cobertura, principalmente para monitoramento de pequenas propriedades. Imagens de cobertura são imagens complexas, com padrões dificeis de serem definidos. Os padrões variam para cada tipo de solo, dependem dascondições a& condições de iluminação ambiente, da resolução da imagem, do tipo de planta e resíduos orgânicos sobre o solo, dentre outros fatores. A extração de atributos de cada pixel é de extrema importância na diferenciação das regiões. Neste trabalho, apresenta-se uma revisão das principais técnicas de segmentação de imagens digitais que serviram de base para a escolha dos métodos utilizados. A cor foi a característica discriminante utilizada com o objetivo de segmentar de forma automática diferentes padrões de cobertura do solo. Foram testados métodos clássicos de análise como a transformada de Hotelling e o discriminante linear de Mahalanobis. Também foram estudadas técnicas não convencionais, como as Redes Neurais, principalmente pela possibilidade de implementação em hardware específico de alto desempenho. Foram selecionados modelos de redes supervisionadas e não supervisionadas. Os resultados obtidos indicam a viabilidade de utilização das técnicas avaliadas neste trabalho na segmentação de imagens aéreas e mostram suas limitações e vantagens principais.Important data for the monitoring of agricultural areas can be obtained from acrial images. One of the most valuable data is the classification and measurement of soil. covering. The main problem is the lack of proper methodology for the analysis and classification of soil covering patterns, mainly for the monitoring of small farms. Images used for sou covering analysis are complex, with patterns ofdifficult identification. Patterns depend on, among other variables, soil type, lighting conditions, image resolution, crop type and type oforganic material over the sou. The recognition of single pixel properties is of niaximum importance to identify regions on an image. In this work, the main techniques used to segment digital images are revisited, some ofthem implemented and tested. Color is the discriminating characteristic of the image used in this work to automatical!y segment the different patterns of soil covering. Classical methods, such as the Hotelling transform and the linear discriminator of Mahalanobis, were evaluated. Also have been evaluated non-conventional techniques such as Neural Networks, both supervised and nonsupervised modeis. Neural Networks have special importance because they can be implemented using custom-designed high performance hardware. Results show that the techniques used in this work are adequate to segment aerial images and make clear its main advantages and limitations

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Dados importantes para o acompanhamento de uma área agrícola podem ser avaliados através de imagens aéreas. Dentre estes, destaca-se como um dos mais significativos, a identificação e a classificação da cobertura do solo. A grande dificuldade reside na não disponibilidade de metodologias apropriadas para a análise e a classificação dos padrões de cobertura, principalmente para monitoramento de pequenas propriedades. Imagens de cobertura são imagens complexas, com padrões dificeis de serem definidos. Os padrões variam para cada tipo de solo, dependem dascondições a& condições de iluminação ambiente, da resolução da imagem, do tipo de planta e resíduos orgânicos sobre o solo, dentre outros fatores. A extração de atributos de cada pixel é de extrema importância na diferenciação das regiões. Neste trabalho, apresenta-se uma revisão das principais técnicas de segmentação de imagens digitais que serviram de base para a escolha dos métodos utilizados. A cor foi a característica discriminante utilizada com o objetivo de segmentar de forma automática diferentes padrões de cobertura do solo. Foram testados métodos clássicos de análise como a transformada de Hotelling e o discriminante linear de Mahalanobis. Também foram estudadas técnicas não convencionais, como as Redes Neurais, principalmente pela possibilidade de implementação em hardware específico de alto desempenho. Foram selecionados modelos de redes supervisionadas e não supervisionadas. Os resultados obtidos indicam a viabilidade de utilização das técnicas avaliadas neste trabalho na segmentação de imagens aéreas e mostram suas limitações e vantagens principais.Important data for the monitoring of agricultural areas can be obtained from acrial images. One of the most valuable data is the classification and measurement of soil. covering. The main problem is the lack of proper methodology for the analysis and classification of soil covering patterns, mainly for the monitoring of small farms. Images used for sou covering analysis are complex, with patterns ofdifficult identification. Patterns depend on, among other variables, soil type, lighting conditions, image resolution, crop type and type oforganic material over the sou. The recognition of single pixel properties is of niaximum importance to identify regions on an image. In this work, the main techniques used to segment digital images are revisited, some ofthem implemented and tested. Color is the discriminating characteristic of the image used in this work to automatical!y segment the different patterns of soil covering. Classical methods, such as the Hotelling transform and the linear discriminator of Mahalanobis, were evaluated. Also have been evaluated non-conventional techniques such as Neural Networks, both supervised and nonsupervised modeis. Neural Networks have special importance because they can be implemented using custom-designed high performance hardware. Results show that the techniques used in this work are adequate to segment aerial images and make clear its main advantages and limitations

Delineating Management Zones with Different Yield Potentials in Soybean–Corn and Soybean–Cotton Production Systems

The delineation of management zones is one of the ways to enable the spatially differentiated management of plots using precision agriculture tools. Over the years, the spatial variability of data collected from soil and plant sampling started to be replaced by data collected by proximal and orbital sensors. As a result, the variety and volume of data have increased considerably, making it necessary to use advanced computational tools, such as machine learning, for data analysis and decision-making support. This paper presents a methodology used to establish management zones (MZ) in precision agriculture by analyzing data obtained from soil sampling, proximal sensors and orbital sensors, in experiments carried out in four plots featuring soybean–cotton and soybean–corn crops, in Mato Grosso and Paraná states, Brazil. Four procedures were evaluated, using different input data sets for the MZ delineation: (I) soil attributes, including clay content, apparent electrical conductivity or fertility, along with elevation, yield maps and vegetation indices (VIs) captured during the peak crop biomass period; (II) soil attributes in conjunction with VIs demonstrating strong correlations; (III) solely VIs exhibiting robust correlation with soil attributes and yield; (IV) VIs selected via random forests to identify the importance of the variable for estimating yield. The results showed that the VIs derived from satellite images could effectively replace other types of data. For plots where the natural spatial variability can be easily identified, all procedures favor obtaining MZ maps that allow reductions of 40% to 70% in yield variance, justifying their use. On the other hand, in plots with low natural spatial variability and that do not have reliable yield maps, different data sets used as input do not help in obtaining feasible MZ maps. For areas where anthropogenic activities with spatially differentiated treatment are already present, the exclusive use of VIs for the delineation of MZs must be carried out with reservations

Distribuição radicular, estado nutricional e produção de colmos e de açúcar em soqueiras de dois cultivares de cana-de-açúcar em solos distintos Root distribution, plant nutritional status, and stalk and sugar yield in two genotypes of sugarcane in distinct soils

O presente trabalho de pesquisa foi elaborado com o objetivo de comprovar a hipótese de que o cultivar de cana-de-açúcar RB83-5486, comparado ao RB83-5089, apresenta melhor distribuição radicular mediante cultivo em solo com menor teor de argila. O estudo foi realizado a partir de dois experimentos de campo desenvolvidos no município de Porto Feliz, SP. Os solos foram classificados como Nitossolo Vermelho eutroférrico latossólico (NV) e Latossolo Vermelho-Amarelo distrófico psamítico (LVA). No período entre a terceira e a quinta soca (2002 a 2004), os cultivares de cana-de-açúcar RB83-5486 e RB83-5089 foram avaliados a partir de estudos de atributos químicos e físicos do solo, do estado nutricional da cultura, da distribuição de raízes no perfil do solo e da produtividade e qualidade tecnológica da cana-de-açúcar. A distribuição de raízes foi avaliada utilizando-se o método da trincheira ou parede do perfil. Os resultados indicaram diferença na concentração de hidrogênio dos solos relacionada com a atividade dos cultivares na absorção de nutrientes. O teor foliar de potássio e enxofre foi maior para o cultivar RB83-5486. No LVA, ambos os cultivares apresentaram maior quantidade de raízes concentrando-se até os 18 cm de distância a partir das linhas de cana e até os 54 cm de profundidade. No NV, o cultivar RB83-5486 apresentou maior quantidade de raízes concentrando-se até 23 cm de distância a partir das linhas de cana e até os 46 cm de profundidade. Já o cultivar RB83-5089 mostrou distribuição do sistema radicular mais uniforme no NV. A produção de colmos foi maior no cultivar RB83-5089; contudo, a produção de açúcar foi maior em RB83-5486.<br>Two field experiments were carried out in Porto Feliz, SP, Brazil, to test the hypothesis that in soils with low clay content, sugarcane genotype RB83 5486 has a better root system distribution than genotype RB83-5089. The two soils used were classified as Typic Kandiudalf and Typic Hapludox. Between the third and fifth ratoon (2002 to 2004), the development of the sugarcane genotypes RB83-5486 and RB83-5089 was evaluated by determining the chemical and physical soil attributes, crop nutritional status, root distribution in the soil profile, and stalk and sugar yield. Root distribution in the soil profile was evaluated by the trench method. Results show differences in hydrogen ion concentrations in both soils. These differences can be related to differences in the nutrient uptake of the studied genotypes. Potassium and sulphur contents in sugarcane leaves were higher in genotype RB83-5486. In the Typic Hapludox most part of the roots of both genotypes were concentrated at 0 to 18 cm from the cane rows and in the upper 54 cm. In the Typic Kandiudalf, most part of the roots of genotype RB83 5486 was concentrated at a distance of 0 to 23 cm from the sugarcane rows and in the upper 46 cm. The root system of genotype RB83-5089 was more evenly distributed in the Typic Kandiudalf. Stalk yield was higher for genotype RB83 5089, while sugar yield was higher for genotype RB83-5486