Takashi Tanaka

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Associate Professor

Gifu University

Japan

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Takashi Tanaka is an Associate Professor at Gifu University in Japan. His research focuses on the application of machine learning and remote sensing technologies for agricultural yield predictions and soil property estimations. Recent studies include the use of UAV imagery and Bayesian techniques to analyze factors affecting soybean and rice yields. His work aims to enhance the understanding of environmental impacts on crop performance and improve precision agriculture practices.

Recent Grants

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Quantitative Evaluation of Impact of the Vegetation Management on Nutrient Dynamics in a Constructed Wetland of the Inland of Western China

Open Date: 2015-01-01

Close Date: 2015-12-01

Grant: Close

中国西部内陸部における人工湿地の植生管理がその水質浄化能に及ぼす影響の定量的評価

Open Date: 2014-04-01

Close Date:

Takashi Tanaka

Research Interests

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About

Recent Grants

Articles (20)

Predictive performance of mid-infrared FTIR spectroscopy and machine learning models for soil property estimation in Japan

Assessing uncertainty of soybean yield response to seeding rates in on-farm experiments using Bayesian posterior passing technique

Assessing available silicon impact on rice yield and quality through on‐farm experimentations

A study on optimal input images for rice yield prediction models using CNN with UAV imagery and its reasoning using explainable AI

Prediction of winter wheat nitrogen status using UAV imagery, weather data, and machine learning

A hierarchical Bayesian approach to assess the impact of environmental factors on soybean yield and yield components

Accuracy and robustness of a plant-level cabbage yield prediction system generated by assimilating UAV-based remote sensing data into a crop simulation model

An instance segmentation dataset of cabbages over the whole growing season for UAV imagery

Can machine learning models provide accurate fertilizer recommendations?

A Bayesian approach to assessing uncertainty in the effect of fertilization strategies on paddy rice yield via multiple on-farm experiments

Toward an effective approach for on-farm experimentation: lessons learned from a case study of fertilizer application optimization in Japan

Multimodal Deep Learning for Rice Yield Prediction Using UAV-Based Multispectral Imagery and Weather Data

Detection of on‐tree chestnut fruits using deep learning and RGB unmanned aerial vehicle imagery for estimation of yield and fruit load

Winter wheat yield prediction using convolutional neural networks and UAV-based multispectral imagery

Critical reevaluation of an efficient sampling design for assessing soil properties using bootstrap sampling and geostatistical analysis in Japanese large-scale paddy fields

Causal forest approach for site-specific input management via on-farm precision experimentation

Using linear mixed-effects modeling to evaluate the impact of edaphic factors on spatial variation in winter wheat grain yield in Japanese consolidated paddy fields

Assessment of design and analysis frameworks for on-farm experimentation through a simulation study of wheat yield in Japan

Machine Learning Techniques to Predict Soybean Plant Density Using UAV and Satellite-Based Remote Sensing

Harvester Evaluation Using Real-Time Kinematic GNSS and Hiring Service Model

Collaborators (3)

Masato Yayota

Koki Homma

Shinji kamio

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