Resultados de búsqueda - "algorithm"

Spotting East African mammals in open savannah from Space por Zheng Yang, Tiejun Wang, Skidmore, Andrew K., Leeuw, Jan de, Said, Mohammed Yahya, Freer, J.

“…However, little research has been conducted in the area of satellite-aided wildlife census, although computer processing speeds and image analysis algorithms have vastly improved. This paper explores the possibility of detecting large animals in the open savannah of Maasai Mara National Reserve, Kenya from very high-resolution GeoEye-1 satellite images. …”
Enlace del recurso

Integrating Global Satellite-Derived Data Products as a Pre-Analysis for Hydrological Modelling Studies: A Case Study for the Red River Basin por Simons, Gijs, Bastiaanssen, Wim G.M.

“…The assortment of satellite-derived open-access information sources on rainfall (P) and land use/land cover (LULC) is currently being expanded with the application of actual evapotranspiration (ETact) algorithms on the global scale. We demonstrate how global remotely sensed P and ETact datasets can be merged to examine hydrological processes such as storage changes and streamflow prior to applying a numerical simulation model. …”
Enlace del recurso

Complete sequence of three different biotypes of tomato spotted wilt virus (wild type, tomato Sw-5 resistance-breaking and pepper Tsw resistance-breaking) from Spain por Debreczeni, Diana E., Lopez, Carmelo, Aramburu, Jose, Daros, José A., Soler, Salvador, Galipienso, Luis, Falk, Bryce W., Rubio, Luis

“…Phylogenetic analysis of the five TSWV open reading frames showed evidence of reassortment between genomic segments of LL-N.05 and Pujol1TL3, which was supported by analysis with different recombination-detecting algorithms.…”
Enlace del recurso

Computer vision for automatic quality inspection of dried figs (Ficus carica L.) in real-time por Benalia, Souraya, Cubero, Sergio, Prats-Montalbán, José M., Bernardi, Bruno, Zimbalatti, Giuseppe, Blasco, José

“…The second research issue had the purpose of developing image processing algorithms to achieve real-time sorting of figs using an experimental prototype based on machine vision, simulating an industrial application. …”
Enlace del recurso
Enlace del recurso

Assessing the accuracy and robustness of a process-based model for coffee agroforestry systems in Central America por Ovalle-Rivera, Oriana, Van Oijen, Marcel, Läderach, Peter, Roupsard, Olivier, Virginio Filho, Elias de Melo, Barrios, Mirna, Rapidel, Bruno

“…Bayesian statistics using Markov chain Monte Carlo algorithms is increasingly used for calibration of parameter-rich models. …”
Enlace del recurso
Enlace del recurso

Factors influencing genomic prediction accuracies of tropical maize resistance to fall armyworm and weevils por Badji, Arfang, Machida, Lewis, Kwemoi, Daniel Bomet, Kumi, Frank, Okii, Dennis, Mwila, Natasha, Agbahoungba, Symphorien, Ibanda, Angele, Bararyenya, Astere, Nghituwamhata, Selma Ndapewa, Odong, Thomas L., Wasswa, Peter, Otim, Michael, Ochwo-Ssemakula, Mildred, Talwana, Herbert, Asea, Godfrey, Kyamanywa, Samuel, Rubaihayo, Patrick

“…Genomic selection (GS) can accelerate variety improvement when training set (TS) size and its relationship with the breeding set (BS) are optimized for prediction accuracies (PAs) of genomic prediction (GP) models. Sixteen GP algorithms were run on phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) of resistance to both fall armyworm (FAW) and maize weevil (MW) in a tropical maize panel. …”
Enlace del recurso

Factors influencing genomic prediction accuracies of tropical maize resistance to fall armyworm and weevils por Badji, Arfang, Machida, Lewis, Kwemoi, Daniel Bomet, Kumi, Frank, Okii, Dennis, Mwila, Natasha, Agbahoungba, Symphorien, Ibanda, Angele, Bararyenya, Astere, Nghituwamhata, Selma Ndapewa, Odong, Thomas L., Wasswa, Peter, Otim, Michael, Ochwo-Ssemakula, Mildred, Talwana, Herbert, Asea, Godfrey, Kyamanywa, Samuel, Rubaihayo, Patrick

“…Genomic selection (GS) can accelerate variety improvement when training set (TS) size and its relationship with the breeding set (BS) are optimized for prediction accuracies (PAs) of genomic prediction (GP) models. Sixteen GP algorithms were run on phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) of resistance to both fall armyworm (FAW) and maize weevil (MW) in a tropical maize panel. …”
Enlace del recurso

Breedbase: a digital ecosystem for modern plant breeding por Morales, Nicolas, Ogbonna, Alex C., Ellerbrock, Bryan J., Bauchet, Guillaume J., Tantikanjana, Titima, Tecle, Isaak Y., Powell, Adrian F., Lyon, David, Menda, Naama, Simoes, Christiano C., Saha, Surya, Hosmani, Prashant, Flores, Mirella, Panitz, Naftali, Preble, Ryan S., Agbona, Afolabi, Rabbi, Ismail, Kulakow, Peter A., Peteti, Prasad, Kawuki, Robert, Esuma, Williams, Kanaabi, Micheal, Chelangat, Doreen M., Uba, Ezenwanyi, Olojede, Adeyemi, Onyeka, Joseph, Shah, Trushar, Karanja, Margaret, Egesi, Chiedozie N., Tufan, Hale Ann, Paterne, Agre, Asfaw, Asrat, Jannink, Jean-Luc, Wolfe, Marnin, Birkett, Clay L., Waring, David J., Hershberger, Jenna M., Gore, Michael A., Robbins, Kelly R., Rife, Trevor, Courtney, Chaney, Poland, Jesse A., Arnaud, Elizabeth, Laporte, Marie-Angélique, Kulembeka, Heneriko, Salum, Kasele, Mrema, Emmanuel, Brown, Allan, Bayo, Stanley, Uwimana, Brigitte, Akech, Violet, Yencho, Craig, Boeck, Bert de, Campos, Hugo, Swennen, Rony L., Edwards, Jeremy D., Mueller, Lukas A.

“…Of crucial importance is a central database able to 1) track breeding materials, 2) store experimental evaluations, 3) record phenotypic measurements using consistent ontologies, 4) store genotypic information, and 5) implement algorithms for analysis, prediction and selection decisions. …”
Enlace del recurso

Combining object-based image analysis with topographic data for landform mapping: a case study in the semi-arid Chaco ecosystem, Argentina por Castillejo González, Isabel Luisa, Angueira, Maria Cristina, García Ferrer, Alfonso, Sánchez de la Orden, Manuel

“…The first stage focused on basic-spectral landform classifications where both pixel- and object-based image analyses were tested with five classification algorithms: Mahalanobis Distance (MD), Spectral Angle Mapper (SAM), Maximum Likelihood (ML), Support Vector Machine (SVM) and Decision Tree (DT). …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform por Cubero, Sergio, Aleixos, Nuria, Albert, Francisco, Torregrosa, Antonio, Ortiz, Coral, García-Navarrete, Óscar L., Blasco, José

“…The equipment is capable of analysing fruit colour and size at a speed of eight fruits per second. The algorithms developed achieved prediction accuracy with an R-2 coefficient of 0.993 for size estimation and an R-2 coefficient of 0.918 for the colour index.…”
Enlace del recurso

Predicting Risk of Ammonia Exposure in Broiler Housing: Correlation with Incidence of Health Issues por Barbosa, Leonardo V. S., da-Silva-Lima, Nilsa D., Granja-Barrios, Juliana-de-Souza, de-Moura, Daniella J., Estellés, Fernando, Ramón-Moragues, Adrián, Calvet-Sanz, Salvador, Villagrá, Arantxa

“…A total of 1250 birds were used, and classification algorithms (decision tree, SMO, Naive Bayes, and Multilayer Perceptron) were applied to predict ammonia risk levels. …”
Enlace del recurso
Enlace del recurso

Metagenomic analyses and genetic diversity of Tomato leaf curl Arusha virus affecting tomato plants in Kenya por Avedi, E.K., Adediji, A.O., Kilalo, D.C., Olubayo, F.M., Macharia, I., Ateka, E.M., Machuka, Eunice M., Mutuku, Josiah M.

“…Analysis of amino acid sequences showed the highest identities in the regions coding for the coat protein gene (98.5–100%) within the isolates, and 97.1–100% identity with the C4 gene of ToLCArV. Phylogenetic algorithms clustered all Kenyan isolates in the same clades with ToLCArV, thus confirming the isolates to be a variant of the virus. …”
Enlace del recurso

Monitoring the elimination of gambiense human African trypanosomiasis in the historical focus of Batié, South-West Burkina Faso por Compaoré, C., Kaboré, J., Ilboudo, H., Thomas, Lian F., Falzon, Laura C., Bamba, M., Sakande, H., Koné, M., Kaba, D., Bougouma, C., Adama, I., Amathe, O., Belem, A., Fèvre, Eric M., Büscher, Philippe, Lejon, V., Jamonneau, V.

“…In this context, the performance of diagnostic tests and testing algorithms for detection of the re-emergence of Trypanosoma brucei gambiense HAT remains to be assessed. …”
Enlace del recurso

Does climate-smart agriculture improve household income and food security? Evidence from Southern Ethiopia por Belay, Abrham, Mirzabaev, Alisher, Recha, John W.M., Oludhe, Christopher, Osano, Philip M., Berhane, Zerihun, Olaka, Lydia A., Tegegne, Yitagesu T, Demissie, Teferi Dejene, Mutsami, Chrispinus, Solomon, Dawit

“…Primary and secondary data were used, and propensity score matching with different types of matching algorithms, such as nearest neighbor, kernel, and radius matching, was employed to quantify the conditional impacts of CSA intervention on farm income and food security. …”
Enlace del recurso

High-throughput characterization and phenotyping of resistance and tolerance to virus infection in sweetpotato por Kreuze, Jan F., Ramírez, D., Fuentes, S., Loayza, H., Ninanya, J., Rinza, J., David, M., Gamboa, S., Boeck, B. de, Díaz, F., Pérez, A., Silva, L., Campos, Hugo

“…Using 14 remote sensing predictors, machine learning algorithms were trained to classify all plots under the said categories. …”
Enlace del recurso

Host tree-based scenario modelling for predicting a key edible insect, mopane worm Gonimbrasia belina (Westwood, 1894) distribution in Southern Africa por Meltus, Q., Mudereri, B.T., Mutamiswa, R., Abdel-Rahman, E.M., Matunhu, J., Musundire, R., Niassy, S., Tonnang, H.

“…Using the species distribution modelling (SDM) package in R, an ensemble of random forest (RF), support vector machine (SVM), and boosted regression tree (BRT) algorithms were used to assess the spatial extent of mopane worm distribution in Southern Africa. …”
Enlace del recurso

A crop-specific and time-variant spatial framework to characterize production environments: a case study for rainfed wheat in Ethiopia por Gelagay, H.S., Leroux, L., Tamene, Lulseged D., Chernet, M., Blasch, G., Tibebe, D., Abera, W., Sida, T., Tesfaye, K., Corbeels, M., Silva, J.V.

“…METHODSAn ensemble machine learning approach built upon time-series satellite images and environmental data was used for crop type mapping while pixel- and object-based clustering algorithms were used to delineate dynamic ASUs from two temporal perspectives: annual ASUs for the 2021 and 2022 growing seasons to assess short-term dynamism, and ASUs from aggregated data (2016 – 2022) to capture long-term variations in the production environment.4. …”
Enlace del recurso

Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning por Momen, Mehdi, Bhatta, Madhav, Hussain, Waseem, Yu, Haipeng, Morota, Gota

“…Three directed paths were consistently identified by two Bayesian network algorithms. Flag leaf‐related traits influenced leaf rust, and yellow rust and stem rust influenced grain yield. …”
Enlace del recurso

Assessing land suitability for leguminous crops in the okavango river basin: A multicriteria and machine learning approach por Negussie, Kaleb Gizaw, Gebrekidan, Bisrat Haile, Wyss, Daniel, Kappas, M.

“…On the other hand, the objective component used a data-driven multivariate approach supported by tree-based learning algorithms. Twenty-two variables were considered, encompassing climatic conditions, hydro-geomorphologic features, soil characteristics, vegetation patterns, and socio-economic factors. …”
Enlace del recurso

Pathogen and pest communities in agroecosystems across climate gradients: anticipating future challenges in the highland tropics por Mouafo-Tchinda, R.A., Sula, A.I.P., Etherton, B.A., Okonya, J.S., Nakato, G.V., Xing, Y., Robledo, J., Adhikari, A., Blomme, G., Kantungeko, D., Nduwayezu, A., Kreuze. J.F., Kroschel, J., Legg, J.P., Garrett, K.A.

“…RESULTS AND CONCLUSIONS Among ten algorithms evaluated, random forests and support vector machines generally performed best for predicting severity or infestation. …”
Enlace del recurso