A Novel CropdocNet for Automated Potato Late Blight Disease Detection
from the Unmanned Aerial Vehicle-based Hyperspectral Imagery
- URL: http://arxiv.org/abs/2107.13277v1
- Date: Wed, 28 Jul 2021 11:18:48 GMT
- Title: A Novel CropdocNet for Automated Potato Late Blight Disease Detection
from the Unmanned Aerial Vehicle-based Hyperspectral Imagery
- Authors: Yue Shi, Liangxiu Han, Anthony Kleerekoper, Sheng Chang, Tongle Hu
- Abstract summary: Late blight disease is one of the most destructive diseases in potato crop, leading to serious yield losses globally.
Current farm practices in crop disease diagnosis are based on manual visual inspection, which is costly, time consuming, subject to individual bias.
Recent advances in imaging sensors (e.g. RGB, multiple spectral and hyperspectral cameras), remote sensing and machine learning offer the opportunity to address this challenge.
- Score: 3.3283767441645478
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Late blight disease is one of the most destructive diseases in potato crop,
leading to serious yield losses globally. Accurate diagnosis of the disease at
early stage is critical for precision disease control and management. Current
farm practices in crop disease diagnosis are based on manual visual inspection,
which is costly, time consuming, subject to individual bias. Recent advances in
imaging sensors (e.g. RGB, multiple spectral and hyperspectral cameras), remote
sensing and machine learning offer the opportunity to address this challenge.
Particularly, hyperspectral imagery (HSI) combining with machine learning/deep
learning approaches is preferable for accurately identifying specific plant
diseases because the HSI consists of a wide range of high-quality reflectance
information beyond human vision, capable of capturing both spectral-spatial
information. The proposed method considers the potential disease specific
reflectance radiation variance caused by the canopy structural diversity,
introduces the multiple capsule layers to model the hierarchical structure of
the spectral-spatial disease attributes with the encapsulated features to
represent the various classes and the rotation invariance of the disease
attributes in the feature space. We have evaluated the proposed method with the
real UAV-based HSI data under the controlled field conditions. The
effectiveness of the hierarchical features has been quantitatively assessed and
compared with the existing representative machine learning/deep learning
methods. The experiment results show that the proposed model significantly
improves the accuracy performance when considering hierarchical-structure of
spectral-spatial features, comparing to the existing methods only using
spectral, or spatial or spectral-spatial features without consider
hierarchical-structure of spectral-spatial features.
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