5 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…