Masahiro Ryo

Leibniz-Centre for Agricultural Landscape Research (ZALF)

Müncheberg

Germany

  • Leibniz-Centre for Agricultural Landscape Research (ZALF)

SCHOLARLY PAPERS

5

DOWNLOADS

482

TOTAL CITATIONS

1

Scholarly Papers (5)

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1.

Explainable Artificial Intelligence and Interpretable Machine Learning for Agricultural Data Analysis

Downloads 323 (203,301)

Explainable Artificial Intelligence and Interpretable Machine Learning for Agricultural Data Analysis

Number of pages: 20 Posted: 27 Sep 2022
Masahiro Ryo
Leibniz-Centre for Agricultural Landscape Research (ZALF)
Downloads 210 (313,098)

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Interpretable machine learning, explainable artificial intelligence, agriculture, crop yield, no-tillage, maize

Explainable Artificial Intelligence and Interpretable Machine Learning for Agricultural Data Analysis

Number of pages: 20 Posted: 19 Oct 2022
Masahiro Ryo
Leibniz-Centre for Agricultural Landscape Research (ZALF)
Downloads 113 (534,547)

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Interpretable machine learning, explainable artificial intelligence, agriculture, crop yield, no-tillage, maize

2.

Co-Developing a Deep Learning-Based Crop Yield Estimation Method in Collaboration with Thousands of Smallholder Coffee Producers

Number of pages: 26 Posted: 23 May 2023
Juan Camilo Rivera Palacio, Christian Bunn, Eric Rahn, Deisy Little-Savage, Paul Schmidt and Masahiro Ryo
affiliation not provided to SSRN, Alliance of Bioversity International and CIAT, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN and Leibniz-Centre for Agricultural Landscape Research (ZALF)
Downloads 99 (584,959)

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Deep learning, crop yield prediction, smallholder, coffee, precision agriculture

3.

Factors Affecting Deep Learning Model Performance in Citizen Science-Based Image Data Collection in Agriculture

Number of pages: 32 Posted: 03 Sep 2024
Juan Rivera-Palacio, Christian Bunn and Masahiro Ryo
affiliation not provided to SSRN, Alliance of Bioversity International and CIAT and Leibniz-Centre for Agricultural Landscape Research (ZALF)
Downloads 23 (1,129,783)
Citation 1

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deep learning, sampling bias, citizen science, mobile pictures, coffee prediction

4.

Predicting Regional-Scale Groundwater Levels at High Spatial Resolution Using Spatial Random Forest Models

Number of pages: 33 Posted: 09 May 2025
Ahsan Raza, Masahiro Ryo, Gohar Ghazaryan, Roland Baatz, Magdalena Main-Knorn, Leonardo Inforsato and Claas Nendel
affiliation not provided to SSRN, Leibniz-Centre for Agricultural Landscape Research (ZALF), affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, University of São Paulo (USP) and affiliation not provided to SSRN
Downloads 20 (1,167,001)

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Machine learning, Spatial interpolation, monthly high-resolution groundwater level mapping, Random Forest for Spatial Interpolation

5.

Using Spatially Explicit Machine Learning to Fine-Scale the Global Gravity-Based Groundwater Product (G3p) for Groundwater Storage Change

Number of pages: 27 Posted: 30 Apr 2025
Ahsan Raza, Masahiro Ryo, Gohar Ghazaryan, Roland Baatz, Leonardo Inforsato and Claas Nendel
affiliation not provided to SSRN, Leibniz-Centre for Agricultural Landscape Research (ZALF), affiliation not provided to SSRN, affiliation not provided to SSRN, University of São Paulo (USP) and affiliation not provided to SSRN
Downloads 17 (1,203,891)

Abstract:

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G3P (Global Gravity-based Groundwater Product)Groundwater Storage Anomalies (GWSA)DownscalingRandom Forest (RF)Multiscale Geographically Weighted Regression (MGWR)GRACE/GRACE-FOHigh-resolution Gridded Estimates