Traditional potato growth models evidence certain limitations, such as the cost of obtaining the input data required to run the models, the lack of spatial information in some instances, or the actual quality of input data. In order to address these issues, we develop a model to predict potato yield using satellite remote sensing. In an effort to offer a good predictive model that improves the state of the art on potato precision agriculture, we use images from the twin Sentinel 2 satellites (European Space Agency-Copernicus Programme) over three growing seasons, applying different machine learning models. First, we fitted nine machine learning algorithms with various pre-processing scenarios using variables from July, August and September based on the red, red-edge and infra-red bands of the spectrum. Second, we selected the best performing models and evaluated them against independent test data. Finally, we repeated the previous two steps using only variables corresponding to July and August. Our results showed that the feature selection step proved vital during data pre-processing in order to reduce multicollinearity among predictors. The Regression Quantile Lasso model (11.67% Root Mean Square Error, RMSE; R 2 = 0.88 and 9.18% Mean Absolute Error, MAE) and Leap Backwards model (10.94% RMSE, R 2 = 0.89 and 8.95% MAE) performed better when predictors with a correlation coefficient > 0.5 were removed from the dataset. In contrast, the Support Vector Machine Radial (svmRadial) performed better with no feature selection method (11.7% RMSE, R 2 = 0.93 and 8.64% MAE). In addition, we used a random forest model to predict potato yields in Castilla y León (Spain) 1-2 months prior to harvest, and obtained satisfactory results (11.16% RMSE, R 2 = 0.89 and 8.71% MAE). These results demonstrate the suitability of our models to predict potato yields in the region studied.The use of new technologies, such as satellite data, Geographic Information Systems (GIS) or Global Positioning Systems (GPS), can improve crop yield production and its quality [1], helping to secure food supply for the future as well as reducing the negative impacts resulting from agricultural practices [11]. More specifically, satellite remote sensing data has many applications in agriculture: soil property detection [12], crop type classification [13], crop yield forecast [14], crop health monitoring [15], soil moisture retrieval [16] or weather data assessment [17]. Remote sensing offers vast amounts of information which can be considered big data [18], and can help to improve crop modelling and decision-making. Big data has been described by Wolfert et al. [19] as "massive volumes of data with a wide variety that can be captured, analysed and used for decision-making", with said authors expecting big data to have a major impact on the agricultural sector. In order to improve the use of this data, given its size and variety, machine learning has emerged as an appropriate tool to identify rules and patterns in datasets [20], in addition to autonomously...
Despite the sustained use of forcefield methodologies to study SiO(2) polymorphs few reviews on the subject are available in the literature. The present study is an attempt to help fill this gap, focusing on classical forcefields used to reproduce and predict properties of pure silica zeolites (or zeosils) such as cell parameters, SiO distance and especially pore size. Instead of an exhaustive study we have focused on an application where diffusion of hydrocarbons makes important the use of pure silica zeolites. A particular area of interest is small pore zeosils containing 8-rings as the largest window, which are industrially interesting for their ability to perform kinetic separations of mixtures of C3 hydrocarbon molecules whose dimensions are of similar characteristics. A set of forcefields have been selected from the literature to analyze their accuracy and transferability when predicting structural, mechanical and dynamical properties of small pore pure silica zeolites and their performance at selective diffusion of C3 hydrocarbons.
The confinement effects upon hydrogen adsorption in Cu(II)-paddle wheel containing metal-organic frameworks (MOFs) were evaluated and rationalized in terms of the structural properties (cavity types and pore diameters) of PCN-12, HKUST-1, MOF-505, NOTT-103 and NOTT-112. First-principles calculations were employed to identify the strongest adsorption positions at the paddle wheel inorganic building unit (IBU). The adsorption centres due to confinement were located through analysis of 3D occupancy maps obtained from the hydrogen trajectories computed via molecular dynamics simulations. It was found that the confinement enhances the adsorption on the weakest adsorption centres around the IBU in regions close to the narrowest windows and promotes the formation of new adsorption regions into the small cavities.Our results indicate that at low pressure, the high H 2 uptake in these materials is partly due to the presence of small cavities (5.3-8.5 Å ) or narrow windows where the long-range contribution to the adsorption becomes important. Conversely, confinement effects in cavities with diameters >12 Å were not observed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.