|
|
|
| Registros recuperados: 55 | |
|
|
| Kedzierski, Mikaël; Falcou-préfol, Mathilde; Kerros, Marie Emmanuelle; Henry, Maryvonne; Pedrotti, Maria Luiza; Bruzaud, Stéphane. |
| The development of methods to automatically determine the chemical nature of microplastics by FTIR-ATR spectra is an important challenge. A machine learning method, named k-nearest neighbors classification, has been applied on spectra of microplastics collected during Tara Expedition in the Mediterranean Sea (2014). To realize these tests, a learning database composed of 969 microplastic spectra has been created. Results show that the machine learning process is very efficient to identify spectra of classical polymers such as poly(ethylene), but also that the learning database must be enhanced with less common microplastic spectra. Finally, this method has been applied on more than 4000 spectra of unidentified microplastics. The verification protocol... |
| Tipo: Text |
Palavras-chave: Microplastic; Tara mediterranean campaign; FTIR spectra; Machine learning; K-nearest neighbor classification. |
| Ano: 2019 |
URL: https://archimer.ifremer.fr/doc/00501/61247/64825.pdf |
| |
|
|
| OLIVEIRA, D. A. B.; PEREIRA, L. G. R.; BRESOLIN, T.; FERREIRA, R. E. P.; DREA, J. R. R.. |
| In livestock operations, systematically monitoring animal body weight, bio-metric body measurements, animal behavior, feed bunk, and other difficult-to-measure phenotypes is manually unfeasible due to labor, costs, and animal stress. Applications of computer vision are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. However, the development of a computer vision system requires sophisticated statistical and computational approaches for efficient data management and appropriate data mining, as it involves mas-sive datasets. This article aims to provide an overview of how deep learning has been implemented in computer vision systems used in livestock, and how such... |
| Tipo: Artigo de periódico |
Palavras-chave: Inteligência artificial; Machine learning; Gado; Agricultura de Precisão; Suíno; Artificial intelligence. |
| Ano: 2021 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1134741 |
| |
|
|
| Bittig, Henry C.; Steinhoff, Tobias; Claustre, Harve; Fiedler, Bjoern; Williams, Nancy L.; Sauzede, Raphaelle; Koertzinger, Arne; Gattuso, Jean-pierre. |
| This work presents two new methods to estimate oceanic alkalinity (A(T)), dissolved inorganic carbon (C-T), pH, and pCO(2) from temperature, salinity, oxygen, and geolocation data. "CANYON-B" is a Bayesian neural network mapping that accurately reproduces GLODAPv2 bottle data and the biogeochemical relations contained therein. "CONTENT" combines and refines the four carbonate system variables to be consistent with carbonate chemistry. Both methods come with a robust uncertainty estimate that incorporates information from the local conditions. They are validated against independent GO-SHIP bottle and sensor data, and compare favorably to other state-of-the-art mapping methods. As "dynamic climatologies" they show comparable performance to classical... |
| Tipo: Text |
Palavras-chave: Carbon cycle; GLODAP; Marine carbonate system; Surface pCO(2) climatology; Revelle buffer factor increase; Machine learning; Nutrient estimation. |
| Ano: 2018 |
URL: https://archimer.ifremer.fr/doc/00675/78681/80879.pdf |
| |
|
| |
|
| |
|
| |
|
| |
|
| |
|
| |
|
| |
|
|
| Dota,Mara Andrea; Cugnasca,Carlos Eduardo; Barbosa,Domingos Sávio. |
| Agriculture, roads, animal farms and other land uses may modify the water quality from rivers, dams and other surface freshwaters. In the control of the ecological process and for environmental management, it is necessary to quickly and accurately identify surface water contamination (in areas such as rivers and dams) with contaminated runoff waters coming, for example, from cultivation and urban areas. This paper presents a comparative analysis of different classification algorithms applied to the data collected from a sample of soil-contaminated water aiming to identify if the water quality classification proposed in this research agrees with reality. The sample was part of a laboratory experiment, which began with a sample of treated water added with... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Environmentalcontrol; Runoff; Wireless sensor networks; Machine learning; Data mining. |
| Ano: 2015 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782015000200267 |
| |
|
|
| TERNES, S.; MOURA, M. F.; SOUZA, K. X. S. de; VAZ, G. J.; OLIVEIRA, S. R. de M.; HIGA, R. H.; LIMA, H. P. de; TAKEMURA, C. M.; COELHO, E. A.; BARBOSA, F. F. L.; VISOLI, M. C.; MENEZES, G. R. de O.; SILVA, L. O. C. da; SANTOS, S. A.; MASSRUHÁ, S. M. F. S.; ABREU, U. G. P. de; SORIANO, B. M. A.; SALIS, S. M.; OLIVEIRA, M. D. de; TOMAS, W. M.. |
| Introdução. Inteligência artificial. Classificação automática de solos. Sistema especialista baseado no SiBCS. Sistema inteligente de classificação de solos. Mineração de textos em publicações técnico-científicas. Modelagem matemática e estatística. Modelagem da dinâmica de dispersão do "HLB do citros". Avaliação genética de animais. Fazenda Pantaneira Sustentável (FPS). O software FPS. Considerações finais. |
| Tipo: Parte de livro |
Palavras-chave: Agricultura digital; Computação científica; Transformação digital na agricultura; Inteligência Artificial; Aprendizado de máquina; Mineração de textos; Modelagem matemática; Machine learning; Text mining; Digital agriculture; Agricultura; Análise Estatística; Agriculture; Artificial intelligence; Statistical analysis; Mathematical models. |
| Ano: 2020 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1126229 |
| |
|
|
| Renosh, Pannimpullath R.; Jourdin, Frederic; Charantonis, Anastase A.; Yala, Khalil; Rivier, Aurelie; Badran, Fouad; Thiria, Sylvie; Guillou, Nicolas; Leckler, Fabien; Gohin, Francis; Garlan, Thierry. |
| Hydro-sedimentary numerical models have been widely employed to derive suspended particulate matter (SPM) concentrations in coastal and estuarine waters. These hydro-sedimentary models are computationally and technically expensive in nature. Here we have used a computationally less-expensive, well-established methodology of self-organizing maps (SOMs) along with a hidden Markov model (HMM) to derive profiles of suspended particulate inorganic matter (SPIM). The concept of the proposed work is to benefit from all available data sets through the use of fusion methods and machine learning approaches that are able to process a growing amount of available data. This approach is applied to two different data sets entitled “Hidden” and “Observable”. The hidden... |
| Tipo: Text |
Palavras-chave: Suspended particulate inorganic matter; Self-organizing maps; Hidden Markov Model; Machine learning; English Channel; ROMS. |
| Ano: 2017 |
URL: http://archimer.ifremer.fr/doc/00415/52653/53511.pdf |
| |
|
|
| BASTOS, B. P.; PINHEIRO, H. S. K.; FERREIRA, F. J. F.; CARVALHO JUNIOR, W. de; ANJOS, L. H. C. dos. |
| Airborne geophysical data (AGD) have great potential to represent soil-forming factors. Because of that, the objective of this study was to evaluate the importance of AGD in predicting soil attributes such as aluminum saturation (ASat), base saturation (BS), cation exchange capacity (CEC), clay, and organic carbon (OC). The AGD predictor variables include total count (uR/h), K (potassium), eU (uranium equivalent), and eTh (thorium equivalent), ratios between these elements (eTh/K, eU/K, and eU/eTh), factor F or F-parameter, anomalous potassium (Kd), anomalous uranium (Ud), anomalous magnetic field (AMF), vertical derivative (GZ), horizontal derivatives (GX and GY), and mafic index (MI). The approach was based on applying predictive modeling techniques... |
| Tipo: Artigo de periódico |
Palavras-chave: Machine learning; Digital soil mapping; Gamma-ray spectrometry data; Magnetic data; Hillslope areas; Parent material; Mapeamento digital do solo; Sensoriamento Remoto. |
| Ano: 2023 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1155276 |
| |
|
|
| Tang, Weiyi; Cassar, Nicolas. |
| Diazotrophs play a critical role in the biogeochemical cycling of nitrogen, carbon, and other elements in the global ocean. Despite their well‐recognized role, the diversity, abundance, and distribution of diazotrophs in the world's ocean remain poorly characterized largely due to limited observations. Here we update the database of diazotroph nifH gene abundances and assess how environmental factors may regulate diazotrophs at the global scale. Our meta‐analysis more than doubles the number of observations in the previous database. Using linear and nonlinear regressions, we find that the abundances of Trichodesmium, UCYN‐A, UCYN‐B, and Richelia relate differently to temperature, light, and nutrients. We further apply a random forest algorithm to estimate... |
| Tipo: Text |
Palavras-chave: Diazotrophs; Marine nitrogen fixation; Meta-analysis; Machine learning. |
| Ano: 2019 |
URL: https://archimer.ifremer.fr/doc/00591/70322/68359.pdf |
| |
|
|
| GONÇALVES, J. P.; PINTO, F. A. C.; QUEIROZ, D. M.; VILLAR, F. M. M.; BARBEDO, J. G. A.; DEL PONTE, E. M.. |
| Colour-thresholding digital imaging methods are generally accurate for measuring the percentage of foliar area affected by disease or pests (severity), but they perform poorly when scene illumination and background are not uniform. In this study, six convolutional neural network (CNN) architectures were trained for semantic segmentation in images of individual leaves exhibiting necrotic lesions and/or yellowing, caused by the insect pest coffee leaf miner (CLM), and two fungal diseases: soybean rust (SBR) and wheat tan spot (WTS). All images were manually annotated for three classes: leaf background (B), healthy leaf (H) and injured leaf (I). Precision, recall, and Intersection over Union (IoU) metrics in the test image set were the highest for B, followed... |
| Tipo: Artigo de periódico |
Palavras-chave: Aprendizado profundo; Fitopatometria; Inteligência artificial; Aprendizado de máquina; Rede neural convolucional; Segmentação de imagem; Phytopathometry; Machine learning; Convolutional neural network; Image segmentation; Doença de Planta; Artificial intelligence; Plant diseases and disorders; Neural networks. |
| Ano: 2021 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1134326 |
| |
|
|
| SPERANZA, E. A.; NAIME, J. de M.; VAZ, C. M. P.; FRANCHINI, J. C.; INAMASU, R. Y.; LOPES, I. de O. N.; QUEIROS, L. R.; RABELLO, L. M.; JORGE, L. A. de C.; CHAGAS, S. das; SCHELP, M. X.; VECCHI, L.. |
| Abstract: 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... |
| Tipo: Artigo de periódico |
Palavras-chave: Aprendizado de máquina; Variabilidade espacial; Machine learning; Spatial variability; Management Zones; Agricultura de Precisão; Soja; Milho; Algodão; Precision agriculture. |
| Ano: 2023 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1156255 |
| |
|
|
| CHO, D. F.; SCHWAIDA, S. F.; CICERELLI, R. E.; ALMEIDA, T.; RAMOS, A. P. M.; SANO, E. E.. |
| O Cerrado é um ecossistema altamente diversificado e fornece habitat para muitas espécies, porém, vem sofrendo degradação acentuada nas últimas décadas devido à expansão da produção de commodities agrícolas. Esse cenário reforça a necessidade de contínuo monitoramento das mudanças de uso e cobertura do solo, seja com foco na produção agrícola ambientalmente sustentável ou no entendimento do mercado. Recentemente, os algoritmos de aprendizagem de máquina têm-se concretizado como uma abordagem promissora e inovadora para processamento de dados de sensoriamento remoto. Assim, esse trabalho teve por objetivo avaliar o potencial do algoritmo de classificação de imagens Random Forest para o mapeamento e classificação do uso e cobertura do solo no Cerrado... |
| Tipo: Artigo de periódico |
Palavras-chave: Geotecnologia; Processamento em nuvem; Machine learning. |
| Ano: 2021 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1136276 |
| |
|
| |
|
|
| RIBEIRO, A. K. do C.; CAIRO, F. C.; ALBUQUERQUE, B. S. F.; SOARES, G. O.; COMINATO. V.; PEREIRA, B. P.; PAIXÃO, R. Q. da; MACHADO, F. S.; PEREIRA, L. G. R.; TOMICH, T. R.; CAMPOS, M. M.. |
| O aumento da atividade das fêmeas bovinas durante o período de estro é responsável pela diminuição do consumo alimentar desses animais. Cochos e bebedouros eletrônicos são capazes de registrar essa variação, entretanto não geram alertas de estro. O objetivo deste estudo foi determinar a eficiência de detecção e detecção antecipada (6 e 12 h de antecedência) do estro por modelos baseados em regressão logística envolvendo Machine Learning, utilizando dados de comportamento e ingestão alimentar e/ou hídrica, gerados por cochos e bebedouros eletrônicos. Foram utilizados dados de dois ensaios experimentais entre 2015 e 2016 com novilhas Holandês-Gir. Todos os modelos de detecção (0 a -24h e 0 a -174h) analisados com e sem a variável de consumo de alimentos... |
| Tipo: Anais e Proceedings de eventos |
Palavras-chave: Detecção; Machine learning; Randon forest; Regressão logística; Logistic regression; Gado Leiteiro; Novilho Leiteiro; Detection. |
| Ano: 2021 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1134454 |
| |
| Registros recuperados: 55 | |
|
|
|
