Search Results for author: Satoshi Kagiwada

Found 6 papers, 1 papers with code

Towards Robust Plant Disease Diagnosis with Hard-sample Re-mining Strategy

no code implementations5 Sep 2023 Quan Huu Cap, Atsushi Fukuda, Satoshi Kagiwada, Hiroyuki Uga, Nobusuke Iwasaki, Hitoshi Iyatomi

Hard-sample mining (HSM) is a common technique for re-training a model by using the mis-detected boxes as new training samples.

object-detection Object Detection

LASSR: Effective Super-Resolution Method for Plant Disease Diagnosis

no code implementations12 Oct 2020 Quan Huu Cap, Hiroki Tani, Hiroyuki Uga, Satoshi Kagiwada, Hitoshi Iyatomi

The collection of high-resolution training data is crucial in building robust plant disease diagnosis systems, since such data have a significant impact on diagnostic performance.

Super-Resolution

LeafGAN: An Effective Data Augmentation Method for Practical Plant Disease Diagnosis

1 code implementation24 Feb 2020 Quan Huu Cap, Hiroyuki Uga, Satoshi Kagiwada, Hitoshi Iyatomi

LeafGAN generates a wide variety of diseased images via transformation from healthy images, as a data augmentation tool for improving the performance of plant disease diagnosis.

Data Augmentation Image-to-Image Translation +1

Super-Resolution for Practical Automated Plant Disease Diagnosis System

no code implementations26 Nov 2019 Quan Huu Cap, Hiroki Tani, Hiroyuki Uga, Satoshi Kagiwada, Hitoshi Iyatomi

Automated plant diagnosis using images taken from a distance is often insufficient in resolution and degrades diagnostic accuracy since the important external characteristics of symptoms are lost.

Super-Resolution

AOP: An Anti-overfitting Pretreatment for Practical Image-based Plant Diagnosis

no code implementations25 Nov 2019 Takumi Saikawa, Quan Huu Cap, Satoshi Kagiwada, Hiroyuki Uga, Hitoshi Iyatomi

As a result, overfitting due to latent similarities in the dataset often occurs, and the diagnostic performance on real unseen data (e, g.

A comparable study: Intrinsic difficulties of practical plant diagnosis from wide-angle images

no code implementations25 Oct 2019 Katsumasa Suwa, Quan Huu Cap, Ryunosuke Kotani, Hiroyuki Uga, Satoshi Kagiwada, Hitoshi Iyatomi

Practical automated detection and diagnosis of plant disease from wide-angle images (i. e. in-field images containing multiple leaves using a fixed-position camera) is a very important application for large-scale farm management, in view of the need to ensure global food security.

Management Object Recognition

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