Examinando por Materia "Algorithms"

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A Comparison of Classification Algorithms for Predicting Distinctive Characteristics in Fine Aroma Cocoa Flowers Using WEKA Modeler
(2024-09-24) Tineo Flores, Daniel; Murillo, Yuriko S.; Marin, Mercedes; Gomez Fernandez, Darwin; Taboada, Víctor H.; Goñas Goñas, Malluri; Quiñonez Huatangari, Lenin
The expression of crop functional traits is influenced by environmental and management conditions, which in turn is reflected in genetic diversity. This study employed a data mining approach to determine the functional traits of flowers that influence cocoa diversity. A total of 1,140 flowers from 228 trees were utilized in this study, with 177 representing fine aroma cocoa trees and 51 trees belonging to other commercial cultivars. Three attribute evaluators (InfoGainAttributeEval, CorrelationAttributeEval and GainRatioAttributeEval), and six algorithms (Naive Bayes, Multinomial Logistic Regression, J48, Random Forest, LTM and Simple Logistic) were employed in this study. The findings indicated that the GainRatioAttributeEval attribute generator was the most efficacious in discerning the functional trait in cocoa diversity flowers. The algorithms Simple Logistic and LMT were the most accurate and specific, while Naive Bayes was the most efficient in terms of computational complexity for model building. This research provides a comprehensive overview of the use of machine learning to analyze functional traits of flowers that most influence cocoa genetic diversity. It also highlights the need to further improve these models by integrating additional techniques to increase their efficiency and extend the data mining approach to other agricultural sectors.
Digital soil mapping of metals and metalloids in croplands using multiple geospatial data and machine learning, implemented in GEE, for the Peruvian Mantaro Valley
(Elsevier, 2024-03-29) Pizarro Carcausto, Samuel; Vera Vilchez, Jesús Emilio; Huamani, Joseph; Cruz, Juancarlos; Lastra, Sphyros; Solórzano Acosta, Richard; Verástegui Martínez, Patricia
Quality and safety of the soil are essential to ensure social and economic development and provides the supply of contaminant free food. With agriculture intensification, expansion of urban zones, construction of roads, and mining, some agricultural soils sites become polluted increasing environmental risks to ecosystems functions and human health. Hence the need know the spatial distribution of elements in soils, we mapped 25 elements, namely Ca, Mg, Sr, Ba, Be, K, Na, As, Sb, Se, Tl, Cd, Zn, Al, Pb, Hg, Cr, Ni, Cu, Mo, Ag, Fe, Co, Mn and V, using various geospatial datasets, such as remote sensing, climate, topography, soil data, and distance, to establish the spatial estimation models of spatial distribution trained trough machine learning model with a supervised dataset of 109 topsoil samples, into Google earth engine platform. Using R2, RMSE and MAE to assess the prediction accuracy. First Random Forest gave satisfactory results in predicting the distribution of analyzed elements in soil, being improved for some elements when adds more trees. Additionally, each element analyzed has a different combination of environmental covariates as predictor, mainly soil, climate, topographic and distance variables especially croplands close to rivers, with less importance for spectral variables. Our results suggest that is possible to identify polluted soils and improved regulations to minimize harm to environmental health and human health, for short-to-medium-term environmental risk control.