Search Results - "Neural Networks"

Comparative analysis of modified partial least squares regression and hybrid deep learning models for predicting protein content in Perilla (Perilla frutescens L.) seed meal using... by Kaur, Simardeep, Singh, Naseeb, Dagar, Preety, Kumar, Amit, Jaiswal, Sandeep, Singh, Binay K., Bhardwaj, Rakesh, Chand Rana, Jai, Riar, Amritbir

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Editorial: Deep learning approaches applied to spectral images for plant phenotyping by Polder, Gerrit, Blasco, José, Cen, Haiyan

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From Rangelands to Cropland, Land-Use Change and its impact on soil organic carbon variables in a Peruvian Andean Highlands: A Machine Learning Modeling approach by Carbajal, M., Ramírez, D., Turin, C., Schaeffer, S.M., Konkel, J., Ninanya, J., Rinza, J., De Mendiburu, F., Zorogastua, P., Villaorduña, L., Quiroz, R.

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Data-driven hydrological discharge simulation in the Limpopo River Basin for transboundary water management by Tlhomole, James, Garcia Andarcia, Mariangel, Matheswaran, Karthikeyan, Borgomeo, E.

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Artificial neural network model for forecasting future ainfall scenario of Jharkhand state of India by Verma, H.C., Mishra, Atmaram

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Drought forecasting using artificial neural networks and time series of drought indices by Morid, S., Smakhtin, Vladimir U., Bagherzadeh, K.

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Forecasting commodity prices using long-short-term memory neural networks by Ly, Racine, Traoré, Fousseini, Dia, Khadim

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Artificial Neural Networks in Agriculture, the core of artificial intelligence: What, When, and Why by Castillo-Gironés, Salvador, Munera, Sandra, Martinez-Sober, Marcelino, Blasco, José, Cubero, Sergio, Gómez-Sanchis, Juan

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An artificial neural network model for simulating streamflow using remote sensing data by Gamage, M.S.D.Nilantha, Agrawal, R., Smakhtin, Vladimir U., Perera, B. J. C.

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Function approximation and documentation of sampling data using artificial neural networks by Zhang, Wenjun, Barrion, Albert

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Deep learning for image-based cassava disease detection by Ramcharan, A., Baranowski, K., McCloskey, P., Ahmed, B., Legg, James P., Hughes, D.P.

Subjects: “…convolutional neural networks…”
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Investigating the use of high resolution multi-spectral satellite imagery for crop mapping in Nigeria crop and landuse classification using WorldView-3 high resolution multispectra... by Alabi, Tunrayo, Haertel, M., Chiejile, S.

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Machine learning based groundwater prediction in a data-scarce basin of Ghana by Siabi, E. K., Dile, Y. T., Kabo-Bah, A. T., Amo-Boateng, M., Anornu, G. K., Akpoti, Komlavi, Vuu, C., Donkor, P., Mensah, S. K., Incoom, A. B. M., Opoku, E. K., Atta-Darkwa, T.

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Advancing non-optical water quality monitoring in Lake Tana, Ethiopia: insights from machine learning and remote sensing techniques by Leggesse, E. S., Zimale, F. A., Sultan, D., Enku, T., Tilahun, Seifu A.

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Automatic recognition of quarantine citrus diseases by Stegmayer, Georgina, Milone, Diego Humberto, Garran, Sergio Mario, Burdyn, Lourdes

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An unmanned aerial vehicle (UAV) technology for estimating leaf N content in rice crops, from multispectral imagery by CGIAR Research Program on Rice

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Neuronal network analyses reveal novel associations between volatile organic compounds and sensory properties of tomato fruits by Cortina, Pablo Ramiro, Santiago, Ana N., Sance, María M., Peralta, Iris Edith, Carrari, Fernando, Asis, Ramón

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Predicting optical water quality indicators from remote sensing using machine learning algorithms in tropical highlands of Ethiopia by Leggesse, E.S., Zimale, Fasikaw A., Sultan, D., Enku, T., Srinivasan, R., Tilahun, Seifu A.

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Daily river discharge estimates by merging satellite optical sensors and radar altimetry through artificial neural network by Tarpanelli, A., Santi, E., Tourian, M.J., Filippucci, P., Amarnath, Giriraj, Brocca, L.

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Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability by El Bilali, A., Brouziyne, Youssef, Attar, O., Lamane, H., Hadri, A., Taleb, A.

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