Tomoaki Yamaguchi — Assistant Professor, Gifu University
作物学を翻訳する
Translating crop science
through remote sensing and AI,
and delivering it to farmers.
Research findings in crop science often remain within academic papers. Working with 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, I am committed to "translating" complex scientific knowledge into forms that farmers can actually use. Through the use of large language models (LLMs) and freely available web applications, I continue to bridge the gap between research and the field.
Understanding rice yield gaps with crop modeling and machine learning in a long-term continuous cropping experiment
Machine learning reveals drivers of yield sustainability in five decades of continuous rice cropping
A study on optimal input images for rice yield prediction models using CNN with UAV imagery
A novel neural network model to achieve generality for diverse morphologies
Improving efficiency of ground-truth data collection for UAV-based rice growth estimation
Feasibility of Combining Deep Learning and RGB Images for Leaf Area Index Estimation
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 Japan