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Rice · Remote sensing · AI · Knowledge translation
Tomoaki Yamaguchi
山口 友亮
Assistant Professor, Faculty of Applied Biological Sciences, Gifu University
Translating crop science
through remote sensing and AI,
and delivering it to farmers.
Research findings in crop science too often stay confined to academic papers. Using UAVs, spectral sensors, and machine learning — including explainable AI (XAI) — I develop crop growth monitoring systems and yield prediction models for rice and other crops. Beyond data collection and analysis, my work centers on "translating" complex scientific knowledge into forms that farmers can actually use. Through large language models (LLMs) and free web applications, I keep bridging the gap between research and the field.
Understanding rice yield gaps with crop modeling and machine learning in a long-term continuous cropping experiment
Quantified rice yield gaps and identified their underlying drivers using Long-Term Continuous Cropping Experiment data.
Machine learning reveals drivers of yield sustainability in five decades of continuous rice cropping
Revealed factors governing long-term yield sustainability in five decades of continuous rice cropping using explainable AI.
A study on optimal input images for rice yield prediction models using CNN with UAV imagery
Identified optimal UAV image inputs for CNN-based rice yield prediction and visualized regions of interest using explainable AI.
A novel neural network model to achieve generality for diverse morphologies
Proposed a novel neural network model for growth estimation of rice varieties with diverse morphologies while incorporating crop-science interpretability.
Improving efficiency of ground-truth data collection for UAV-based rice growth estimation
Examined efficient ground-truth data collection methods for UAV-based rice growth estimation models.
Feasibility of Combining Deep Learning and RGB Images for Leaf Area Index Estimation
Demonstrated that combining deep learning with standard RGB imagery enables accurate estimation of leaf area index (LAI).
Improving efficiency of ground-truth data collection for UAV-based rice growth estimation models: investigating the effect of sampling size on model accuracy
The Crop Science Society of JapanPractical Feasibility of 3D Data of Individual Rice Plants Reconstructed from Smartphone-Captured Videos
The Crop Science Society of JapanEffect of Environmental Differences on Empirical Regression Models for Estimating Leaf Area Index Using Vegetation Indices in Rice
Asian Crop Science AssociationTutorials, videos, and tools for crop science data analysis.
Feel free to reach out.