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

Radiometria na avaliação da eficiência da reflexão do ultravioleta por diferentes mulching no controle do tripes-do-tomateiro, Frankliniella schultzei (Trybom) / Radiometry in the evaluation of the efficiency of ultraviolet reflection by different mulching in the control of tomato thrips, Frankliniella schultzei (Trybom)

A produção comercial de tomate (Lycopersicon esculentum Mill) é uma das mais importantes do mundo, para pequenos e médios agricultores. No entanto, pragas e doenças têm causado graves prejuízos econômicos. Os tripes estão entre as pragas mais relevantes. O método mais amplamente empregado no controle dessa praga no plantio de tomate é o método químico, com aplicações seqüenciais que podem atingir até quinze pulverizações por ciclo de cultura. No entanto, a resistência dos tripes aos inseticidas tem sido relatada em vários países. Outro método possível é usar a cobertura do solo (cobertura morta), pois pode modificar o balanço da radiação solar nas plantas, como resultado das características ópticas aplicadas. A cobertura morta mais comumente utilizada pelos agricultores de tomate é o plástico de polietileno nas cores branca ou preta, portanto, o objetivo desta pesquisa foi avaliar a eficiência do reflexo da radiação ultravioleta (UV-A) por vários materiais distintos usados como cobertura morta, para cultivar o tomateiro, empregando o método tradicional de análise suplementado por radiometria de reflexão difusa. O plástico na cor prata apresentou eficiência superior a 80% no controle da praga aos 14, 21 e 28 dias após o transplante. O plástico de polietileno na cor branca foi o que apresentou a maior incidência de tripes, sendo uma cor atraente para a colonização da praga. O uso da cobertura morta com plástico de polietileno de prata pode fornecer uma nova opção para o controle de tripes na cultura do tomate, contribuindo para a redução do custo de produção e a sustentabilidade ambiental. 

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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

A deep learning approach based on graphs to detect plantation lines

Identifying plantation lines in aerial images of agricultural landscapes is re-quired for many automatic farming processes. Deep learning-based networks are among the most prominent methods to learn such patterns and extract this type of information from diverse imagery conditions. However, even state-of-the-art methods may stumble in complex plantation patterns. Here, we propose a deep learning approach based on graphs to detect plantation lines in UAV-based RGB imagery, presenting a challenging scenario containing spaced plants. The first module of our method extracts a feature map throughout the backbone, which consists of the initial layers of the VGG16. This feature map is used as an input to the Knowledge Estimation Module (KEM), organized in three concatenated branches for detecting 1) the plant positions, 2) the plantation lines, and 3) the displacement vectors between the plants. A graph modeling is applied considering each plant position on the image as vertices, and edges are formed between two vertices (i.e. plants). Finally, the edge is classified as pertaining to a certain plantation line based on three probabilities (higher than 0.5): i) in visual features obtained from the backbone; ii) a chance that the edge pixels belong to a line, from the KEM step; and iii) an alignment of the displacement vectors with the edge, also from the KEM step. Experiments were conducted initially in corn plantations with different growth stages and patterns with aerial RGB imagery to present the advantages of adopting each module. We assessed the generalization capability in the other two cultures (orange and eucalyptus) datasets. The proposed method was compared against state-of-the-art deep learning methods and achieved superior performance with a significant margin considering all three datasets. This approach is useful in extracting lines with spaced plantation patterns and could be implemented in scenarios where plantation gaps occur, generating lines with few-to-no interruptions