Search Results - "algorithm"

Energieffektivisering av dricksvattendistribution : dataanalys och simulering av pumpdrift vid Görvälns vattenverk by Thorstensson, Anton

“…The project also investigates the possibilities of create consumption forecasts with machine learning algorithms and using these to improve the distribution system. …”

Développement des prévisions hydrologiques saisonnières de Nouvelle Génération pour l’Afrique de l’Ouest et le Sahel by Ali, Abdou, Kiema, Arsene, Houngnibo, Mandela C M, Mohamed, Hamatan, Nimon, Pouwereou, Segnon, Alcade Christel, Zougmore, Robert Bellarmin

“…La nouvelle Génération des prévisions saisonnières combine les méthodes statistiques avancées, la modélisation hydrologique et l’utilisation d’algorithmes d’intelligence artificielle. Les nouveaux produits de prévision sont plus détaillés, plus précis que l’approche consensuelle. …”
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Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning by Hengl, T., Leenaars, Johan G.B., Shepherd, Keith D., Walsh, Markus G., Heuvelink, Gerard B.M., Mamo, T., Tilahun, H., Berkhout, Ezra D., Cooper, M., Fegraus, E., Wheeler, I., Kwabena, N. A.

“…An ensemble model was then created for each nutrient from two machine learning algorithms.…”
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Machine learning and big data techniques for satellite-based rice phenology by Aguilar-Ariza, Andrés

“…Analytics from validation showed that the algorithms were able to estimate rice phases with performances above 0.94 in f-1 score. …”
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Impact of Ghana's agricultural mechanization services center program by Benin, Samuel

“…A two‐stage propensity score matching and difference‐in‐difference estimation procedure is used to estimate the impacts of the program, employing different definitions of treatment, model specifications, and matching algorithms to assess sensitivity of the estimator to different assumptions. …”
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Class prediction of closely related plant varieties using gene expression profiling by Ancillo, Gema, Gadea, Jose, Forment, Javier, Guerri, José, Navarro, Luis

“…Gene expression profiles have been used to predict mandarin clementine varieties (Citrus clementina, Hort. ex Tan.) by means of two independent supervised learning algorithms: Support Vector Machines and Prediction Analysis of Microarrays. …”
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A Bayesian optimization R package for multitrait parental selection by Villar-Hernandez, Bartolo de J., Dreisigacker, Susanne, Crespo-Herrera, Leonardo A., Perez-Rodriguez, Paulino, Perez-Elizalde, Sergio, Toledo, Fernando H., Crossa, José

“…The package employs Bayesian optimization algorithms and three loss functions (Kullback–Leibler, Energy Score, and Multivariate Asymmetric Loss) to identify parental candidates with desirable traits. …”
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Globe - LFMC a global plant water status database for vegetation ecophysiology and wildfire applications by Yebra, Marta, Scortechini, Gianluca, Badi, Abdulbaset, Beget, Maria Eugenia, Boer, Mathias, Bradstock, Ross, Chuvieco, Emilio, Mark Danson, F., Dennison, Philip, Resco de Dios, Víctor, Di Bella, Carlos Marcelo, Forsyth, Greg, Frost, Philip, García, Mariano, Hamdi, Abdelaziz, He, Bimbin, Jolly, Matt, Kraaij, Tineke, Martin, M. Pilar, Mouillot, Florent, Newnham, Glenn, Nolan, Rachae, Pellizzaro, Grazia, Qi, Yi, Quan, Xingwen, Riaño, David, Roberts, Dar, Sow, Momadou, Ustin, Susan

“…The database contains 161,717 individual records based on in situ destructive samples used to measure LFMC, representing the amount of water in plant leaves per unit of dry matter. the primary goal of the database is to calibrate and validate remote sensing algorithms used to predict LFMC. However, this database is also relevant for the calibration and validation of dynamic global vegetation models, eco-physiological models of plant water stress as well as understanding the physiological drivers of spatiotemporal variation in LFMC at local, regional and global scales. …”
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Non-Destructive Spectral Systems (NDSS) for modern inspection systems in real-time: challenges and industrial perspectives by Blasco, Jose, Gorla, Giulia, Munera, Sandra, Vitale, Raffaele, Amigo, Jose Manuel

“…Recent technological innovations, data acquisition, computational algorithms, and their applications in the agri-food, pharmaceutical, and environmental monitoring sectors are explored. …”
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Modelo de simulación de yuca (Manihot esculenta Crantz) en el trópico by Moreno Cadena, Leidy Patricia

“…CurveExpert was used to define the algorithms, the software Simile evaluated the performance of the simulation of individual equations and the program FITEVAL assess the goodness of fit of the model. …”
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Estimation of height and aerial biomass in Eucalyptus globulus plantations using UAV-LiDAR by Enriquez Pinedo, Lucía, Ortega Quispe, Kevin, Ccopi Trucios, Dennis, Urquizo Barrera, Julio, Rios Chavarría, Claudia, Pizarro Carcausto, Samuel, Matos Calderon, Diana, Patricio Rosales, Solanch, Rodríguez Cerrón, Mauro, Ore Aquino, Zoila, Paz Monge, Michel, Castañeda Tinco, Italo

“…Various LiDAR metrics were employed alongside field measurements to calibrate predictive models using multiple regression and machine learning algorithms. The results at the individual tree level show that RF is the most accurate model, with a coefficient of determination (R²) of 0.76 in the training set and 0.66 in the test set, outperforming Elastic Net (R² of 0.58 and 0.57, respectively). …”
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Robust Path Matching and Anomalous Route Detection Using PosteriorWeighted Graphs by Doocy, Lauren, Prager, Steven D., Kider, Joseph T., Wiegand, R. Paul

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Using phenotypic distribution models to predict livestock performance by Lozano Jaramillo, Maria, Worku, Setegn, Dessie, Tadelle, Komen, Hans, Bastiaansen, John W.M.

“…Phenotype distribution models were built using machine learning algorithms to make predictions of weight in the local environmental conditions based on the productivity for the breed. …”
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Machine learning approach for high-throughput phenolic antioxidant screening in black Rice germplasm collection based on surface FTIR by Herath, Achini, Tiozon, Rhowell Jr., Kretzschmar, Tobias, Sreenivasulu, Nese, Mahon, Peter, Butardo, Vito

“…Here we utilised rapid surface Fourier transform infrared (FTIR) spectroscopy and machine learning algorithms (ML) to predict and classify polyphenolic antioxidants. …”
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From Pixels to Planting Dates: Using the AgWise Remote-sensing Framework to Automate Maize Planting-date Detection by Dastidar, Payel Ghosh, Srivastava, Amit, Leroux, Louise

“…This study uses multitemporal satellite data, vegetation index information, and smoothing algorithms to extract planting dates across Kenya and Rwanda utilising the automated remote-sensing workflow of the AgWise platform. …”
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Application of a GIS-AF/RF model to assess the risk of herbicide leaching in a citrus-growing area of the Valencia Community, Spain by De-Paz, José M., Rubio, José L.

“…The soil and climate data required by the model were stored in an Arc/Info GIS in which the model algorithms were integrated using the AML programming language. …”
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Modelling agricultural drought: a review of latest advances in big data technologies by Houmma, I.H., El Mansouri, L., Gadal, S., Garba, M., Hadria, R.

“…The analysis focused on the different methods used, the choice of algorithms and the most relevant variables depending on whether they are descriptive or predictive models. …”
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Mathematical modeling to predict rice's phenolic and mineral content through multispectral imaging by Buenafe, Reuben James, Tiozon, Rhowell N., Boyd, Lesley A., ‪Sartagoda, Kristel June, Sreenivasulu, Nese

“…Random forest and artificial neural network models depicted the multi-nutritional features of black rice with 85.35 and 99.9% accuracy, respectively. These prediction algorithms would help rice breeders strategically breed nutritionally valuable genotypes based on simple, high-through-put videometerLAB readings and a small number of nutritional assays.…”
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Development of AI methods for seed yield prediction using RGB images: Progress Report by Arrechea, Darwin A., Cardoso Arango, Juan Andrés

“…The focal point of our exploration is the automated counting of seeds in forages using DL algorithms. The current landscape of agricultural technology reveals a growing need for more accurate and efficient methods of predicting seed yields. …”
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Livestock detection in African rangelands: Potential of high-resolution remote sensing data by Ocholla, I.A., Pellikka, P., Karanja, F.N., Vuorinne, I., Odipo, Victor, Heiskanen, J.

“…In this paper, we present a review of current technological advancements in remote sensing and detection algorithms in livestock censuses, identifying weaknesses in sensors and detection methods, and highlighting issues that currently limit adoption of these technologies in African countries. …”
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