Zobrazeno 1 - 10
of 143
pro vyhledávání: '"Kagiwada, Satoshi"'
Publikováno v:
Computers and Electronics in Agriculture, Volume 222, July 2024, 109021
The development of practical and robust automated diagnostic systems for identifying plant pests is crucial for efficient agricultural production. In this paper, we first investigate three key research questions (RQs) that have not been addressed thu
Externí odkaz:
http://arxiv.org/abs/2407.18000
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 and excel at detecting small lesions compared to classification methods
Externí odkaz:
http://arxiv.org/abs/2407.17906
Autor:
Cap, Quan Huu, Fukuda, Atsushi, Kagiwada, Satoshi, Uga, Hiroyuki, Iwasaki, Nobusuke, Iyatomi, Hitoshi
With rich annotation information, object detection-based automated plant disease diagnosis systems (e.g., YOLO-based systems) often provide advantages over classification-based systems (e.g., EfficientNet-based), such as the ability to detect disease
Externí odkaz:
http://arxiv.org/abs/2309.01903
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. However, they are very difficult to obtain and are not always availabl
Externí odkaz:
http://arxiv.org/abs/2010.06499
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 overfitting, and the diagnostic performance is drastically decreased wh
Externí odkaz:
http://arxiv.org/abs/2002.10100
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. In this paper, we first propose an effective pre-proc
Externí odkaz:
http://arxiv.org/abs/1911.11341
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. As a result, overfitting due to latent similarities in the dataset of
Externí odkaz:
http://arxiv.org/abs/1911.10727
Autor:
Suwa, Katsumasa, Cap, Quan Huu, Kotani, Ryunosuke, Uga, Hiroyuki, Kagiwada, Satoshi, Iyatomi, Hitoshi
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 t
Externí odkaz:
http://arxiv.org/abs/1910.11506
Publikováno v:
In Computers and Electronics in Agriculture August 2021 187
Publikováno v:
In Biochemical and Biophysical Research Communications 26 November 2024 735