Search Results - "discrimination"

In-Field Forage Biomass and Quality Prediction Using Image and VIS-NIR Proximal Sensing with Machine Learning and Covariance-Based Strategies for Livestock Management in Silvopasto... by Serpa Imbett, Claudia M., Gómez Palencia, Erika L., Medina Herrera, Diego A., Mejía Luquez, Jorge A., Martínez, Remberto R., Burgos Paz, William O., Aguayo Ulloa, Lorena A.

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Decision-making in the purchase of equipment in agricultural research laboratories: a multiple-criteria approach under partial information by Moreno Rodriguez, Jenny Milena, Abreu Kang, Takanni Hannaka, Asfora Frej, Eduarda, Teixeira de Almeida, Adiel

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Efecto de la dinámica de tostado sobre las propiedades del licor de copoazú (Theobroma grandiflorum Willd. ex Spreng. Schum.) by Criollo Nuñez, Jenifer, Sandoval Aldana, Sandoval Aldana, Méndez Arteaga, John Jairo

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Occurrence and characterization of cyst nematode species (Globodera spp.) associated with potato crops in Colombia by Vallejo, Daniela, Rojas, Diego A., Martinez, John A., Marchant, Sergio, Holguin, Claudia M., Pérez, Olga Y.

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Principios de virología molecular de plantas tropicales. by Marín Montoya, Mauricio, Gutiérrez Sánchez, Pablo Andrés

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VIS/NIR hyperspectral imaging and N-way PLS-DA models for detection of decay lesions in citrus fruits by Folch-Fortuny, A., Prats-Montalbán, José M., Cubero, Sergio, Blasco, José, Ferrer, Alberto

“…To build the discriminant model a set of oranges and mandarins was infected by the fungus and another set was infiltrated just with water for control purposes. …”
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Las ómicas en el control biológico by Barrera, Gloria Patricia, Ghiringhelli, Pablo Daniel, Lewis Mosher, Stephen, Caro Quintero, Alejandro, Massart, Sebastien, Belaich, Mariano Nicolás

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Visible and Near-Infrared Diffuse Reflectance Spectroscopy for Fast Qualitative and Quantitative Assessment of Nectarine Quality by Cortés, Victoria, Blasco, José, Aleixos, Nuria, Cubero, Sergio, Talens, Pau

“…Big Top and Magique, respectively). Discriminant models, using linear discriminant and partial least-squares discriminant analyses, were built to classify the nectarines. …”
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GGE biplot analysis of Dioscorea rotundata cultivar tela genotypes in Ghana by Otoo, E., Asiedu, Robert

“…Fumesua environments were most representative and discriminating. Bodwease environments except BOD04 were most discriminating but most non-representative and Wenchi least discriminating and least representative. …”
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A comparison between NIR and ATR-FTIR spectroscopy for varietal differentiation of Spanish intact almonds by Cortés, Victoria, Barat, Jose Manuel, Talens, Pau, Blasco, José, Lerma-García, María J.

“…After spectra measurement, NIR and ATR-FTIR spectral data were pre-treated and employed to construct two classification methods (partial least square-discriminant analysis (PLS-DA) and quadratic discriminant analysis (QDA)) in order to check their ability to classify almonds according to their variety. …”
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Multivariate Analysis of Diversityof Landrace Rice Germplasm by Sanni, K.A., Fawole, I., Ogunbayo, A., Tia, D., Somado, E., Futakuchi, K., Sié, M., Nwilene, F.E., Guei, R.G.

“…The unweighted pair group method of the average linkage (UPGMA) cluster analysis, canonical discriminant analysis, and principal component analysis (PCA) were used to analyze the data obtained. …”
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Identifying target groups for livestock improvement research: the classification of sedentary livestock producers in western Niger by Williams, Timothy O.

“…Four recommendation domains were identified. Discriminant analysis was used to profile the characteristics of the obtained groups. …”
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Evaluating standard procedures for instrumental textural analysis of steamed potato: Relationship with sensory parameters by Nakitto, M., Moyo, M., Mendes, T., Balikoowa, B., Ssali, R.T., Ayetigbo, O., Mestres, C., Dufour, D.

“…Finally, the more discriminant textural parameters for TPA were Hardness (20-52 N), Gumminess (5-19 N) and Chewiness (5-18 N). …”
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Species-informative SNP markers for characterising freshwater prawns of genus Macrobrachium in Cameroon by Makombu, J.G., Cheruiyot, E.K., Stomeo, Francesca, Thuo, D.N., Oben, P.M., Oben, Benedicta O., Zango, P., Mialhe, E., Ngueguim, J.R., Mujibi, F.D.N.

“…However, large sample size is needed to identify more informative SNPs for discriminating genetically closely related species, including M. macrobrachion versus M. vollenhovenii and M. sollaudii versus M. dux. …”
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Development and validation of animal welfare knowledge, attitude, and practice assessment tool among smallholder farmers in Ethiopia by Ayalew, Gezahegn, Berhe, Tsega, Mekonnen, Mesfin, Gelan, Eyob, Wieland, Barbara, Knight-Jones, Theodore J.D., Doyle, Rebecca

“…Graphs depicting the discriminating ability and precision of an item across different levels of the latent trait were plotted. …”
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The Mahibere-Silassie composite: A new cattle breed type in north-western Ethiopia by Wuletaw, Z., Ayalew, W., Sölkner, Johann, Hegde, B.P.

“…Horn length and navel flap were the best discriminating variables from quantitative and qualitative variables, respectively. …”
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Characterization of citrus HSVd isolates by Palacio-Bielsa, Ana, Romero-Durbán, J., Durán-Vila, Núria

“…The electrophoretic profiles of SSCP appeared to be able to discriminate between non-cachexia and cachexia sources of HSVd. …”
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Bioinformatics approach for developing a minimum set of SNP markers for identification of temperate japonica rice varieties cultivated in Spain by Sales, Ester, García-Romeral, Julia, Domingo, Concha

“…In addition, we used this algorithm to discriminate the 43 most cultivated in Spain rice varieties with minimal sets of 8 SNPs. …”
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Evaluation of soil quality identified by local farmers in Mai-Negus catchment, northern Ethiopia by Brhane Tesfahunegn, Gebrayesus, Tamene, Lulseged D., Vlek, Paul L.G.

“…Using variables retained in the four identified component factors, discriminant analysis identified soil porosity to be the most powerful variable that can help discriminate the SQ status. …”
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Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features by Blasco, José, Aleixos, Nuria, Gómez-Sanchís, Juan, Moltó, Enrique

“…In this paper, we present a computer vision system that was developed for the recognition and classification of the most common external defects in citrus. in order to discriminate between 11 types of defects, images of the defects were acquired in five spectral areas, including the study of near infrared reflectance and ultraviolet induced fluorescence. …”
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