e-space
Manchester Metropolitan University's Research Repository

Novel CropdocNet Model for Automated Potato Late Blight Disease Detection from Unmanned Aerial Vehicle-Based Hyperspectral Imagery

Shi, Y ORCID logoORCID: https://orcid.org/0000-0001-8424-6996, Han, L ORCID logoORCID: https://orcid.org/0000-0003-2491-7473, Kleerekoper, A ORCID logoORCID: https://orcid.org/0000-0002-3621-8568, Chang, S and Hu, T (2022) Novel CropdocNet Model for Automated Potato Late Blight Disease Detection from Unmanned Aerial Vehicle-Based Hyperspectral Imagery. Remote Sensing, 14 (2). p. 396. ISSN 2072-4292

[img]
Preview
Published Version
Available under License Creative Commons Attribution.

Download (8MB) | Preview

Abstract

The accurate and automated diagnosis of potato late blight disease, one of the most destructive potato diseases, is critical for precision agricultural control and management. Recent advances in remote sensing and deep learning offer the opportunity to address this challenge. This study proposes a novel end-to-end deep learning model (CropdocNet) for accurate and automated late blight disease diagnosis from UAV-based hyperspectral imagery. The proposed method considers the potential disease-specific reflectance radiation variance caused by the canopy’s structural diversity and introduces multiple capsule layers to model the part-to-whole relationship between spectral– spatial features and the target classes to represent the rotation invariance of the target classes in the feature space. We evaluate the proposed method with real UAV-based HSI data under controlled and natural field conditions. The effectiveness of the hierarchical features is quantitatively assessed and compared with the existing representative machine learning/deep learning methods on both testing and independent datasets. The experimental results show that the proposed model significantly improves accuracy when considering the hierarchical structure of spectral–spatial features, with average accuracies of 98.09% for the testing dataset and 95.75% for the independent dataset, respectively.

Impact and Reach

Statistics

Activity Overview
6 month trend
36Downloads
6 month trend
56Hits

Additional statistics for this dataset are available via IRStats2.

Altmetric

Actions (login required)

View Item View Item