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| Registros recuperados: 34 | |
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| Cremon, Cassiano; UNEMAT; Rosa Júnior, Edgard Jardim; UFMS; Serafim, Milson Evaldo; UFMS; Ono, Fábio Benedito; UFLA. |
| O objetivo deste trabalho é caracterizar micromorfometricamente os agregados de um Latossolo submetido a diferentes formas de manejo do solo. O trabalho desenvolveu-se em um LATOSSOLO VERMELHO Distroférrico, de Dourados, Mato Grosso do Sul. O delineamento experimental utilizado foi o de blocos casualizados com quatro repetições e os tratamentos foram arranjados em parcelas sub-subdivididas. Os tratamentos consistiram de dois sistemas de manejo (convencional e plantio direto), duas doses de gesso (zero e 2 t ha-1) e a utilização de duas culturas de verão (soja e milho). Os agregados foram coletados, nas profundidades de 0 a 10 e 10 a 20 cm, utilizando-se, nos procedimentos experimentais, os retidos no intervalo de diâmetro de 9,52 e 4,76 mm, após... |
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Palavras-chave: 5.02.01.07-7 Solos Florestais plantio direto; Imagens digitais; Agregados. no-tillage; Digital images; Aggregates. |
| Ano: 2009 |
URL: http://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/6640 |
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| BARBEDO, J. G. A.. |
| Abstract. This paper presents a survey on methods that use digital image processing techniques to detect, quantify and classify plant diseases from digital images in the visible spectrum. Although disease symptoms can manifest in any part of the plant, only methods that explore visible symptoms in leaves and stems were considered. This was done for two main reasons: to limit the length of the paper and because methods dealing with roots, seeds and fruits have some peculiarities that would warrant a specific survey. The selected proposals are divided into three classes according to their objective: detection, severity quantification, and classification. Each of those classes, in turn, are subdivided according to the main technical solution used in the... |
| Tipo: Artigo em periódico indexado (ALICE) |
Palavras-chave: Processamento de imagens; Imagem digital; Image processing; Digital images. |
| Ano: 2013 |
URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/976311 |
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| Ganganagowder,Narendra Veernagouda; Kamath,Priya. |
| Abstract The aim of this research is to build a supervised intelligent classification model of food products such as Biscuits, Cereals, Vegetables, Edible nuts and etc., using digital images. The Correlation-based Feature Selection (CFS) algorithm and 2nd derivative pre-treatments of the Morphological, Colour and Texture features are used to train the models for classification and detection. The best prediction accuracy is obtained for the Multilayer Perceptron (MLP), Support Vector Machines (SVM), Random Forest (RF), Simple Logistic (SLOG) and Sequential Minimal Optimization (SMO) classifiers (more than 80% of the success rate for the training/test set and 80% for the validation set). The percentage of correctly classified instances is very high in these... |
| Tipo: Journal article |
Palavras-chave: Algorithm; Digital images; Food classifiers; Prediction accuracy; Training/test. |
| Ano: 2017 |
URL: http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-28122017000400486 |
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| Registros recuperados: 34 | |
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