16 citations · 30 across the 11 of their papers we have counts for
11 papers
Robust Plant Disease Diagnosis with Few Target-Domain Samples
Takafumi Nogami, Satoshi Kagiwada, Hitoshi Iyatomi
Various deep learning-based systems have been proposed for accurate and convenient plant disease diagnosis, achieving impressive performance. However, recent studies show that thes…
DDD: Discriminative Difficulty Distance for plant disease diagnosis
Yuji Arima, Satoshi Kagiwada, Hitoshi Iyatomi
Recent studies on plant disease diagnosis using machine learning (ML) have highlighted concerns about the overestimated diagnostic performance due to inappropriate data partitionin…
Few-shot Metric Domain Adaptation: Practical Learning Strategies for an Automated Plant Disease Diagnosis
Shoma Kudo, Satoshi Kagiwada, Hitoshi Iyatomi
Numerous studies have explored image-based automated systems for plant disease diagnosis, demonstrating impressive diagnostic capabilities. However, recent large-scale analyses hav…
Investigation to answer three key questions concerning plant pest identification and development of a practical identification framework
Ryosuke Wayama, Yuki Sasaki, Satoshi Kagiwada +2
The development of practical and robust automated diagnostic systems for identifying plant pests is crucial for efficient agricultural production. In this paper, we first investiga…
Hierarchical Object Detection and Recognition Framework for Practical Plant Disease Diagnosis
Kohei Iwano, Shogo Shibuya, Satoshi Kagiwada +1
Recently, object detection methods (OD; e.g., YOLO-based models) have been widely utilized in plant disease diagnosis. These methods demonstrate robustness to distance variations a…
Towards Robust Plant Disease Diagnosis with Hard-sample Re-mining Strategy
Quan Huu Cap, Atsushi Fukuda, Satoshi Kagiwada +3
With rich annotation information, object detection-based automated plant disease diagnosis systems (e.g., YOLO-based systems) often provide advantages over classification-based sys…
LASSR: Effective Super-Resolution Method for Plant Disease Diagnosis
Quan Huu Cap, Hiroki Tani, Hiroyuki Uga +2
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.…
LeafGAN: An Effective Data Augmentation Method for Practical Plant Disease Diagnosis
Quan Huu Cap, Hiroyuki Uga, Satoshi Kagiwada +1
Many applications for the automated diagnosis of plant disease have been developed based on the success of deep learning techniques. However, these applications often suffer from o…
Super-Resolution for Practical Automated Plant Disease Diagnosis System
Quan Huu Cap, Hiroki Tani, Hiroyuki Uga +2
Automated plant diagnosis using images taken from a distance is often insufficient in resolution and degrades diagnostic accuracy since the important external characteristics of sy…
AOP: An Anti-overfitting Pretreatment for Practical Image-based Plant Diagnosis
Takumi Saikawa, Quan Huu Cap, Satoshi Kagiwada +2
In image-based plant diagnosis, clues related to diagnosis are often unclear, and the other factors such as image backgrounds often have a significant impact on the final decision.…
A comparable study: Intrinsic difficulties of practical plant diagnosis from wide-angle images
Katsumasa Suwa, Quan Huu Cap, Ryunosuke Kotani +3
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 import…