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

Yue Shi,Liangxiu Han,Anthony Kleerekoper,Tongle Hu,Sheng Chang

doi:10.3390/rs14020396

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.

Highlights

IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
We quantitatively investigated the performance of the proposed model considering the hierarchical structure of the spectral–spatial information and the representative machine/deep learning approaches without considering it (i.e., Support vector machine (SVM) with the spectral features only, random forest (RF) with the spatial features only and 3D convolutional neural network (3D-convolutional neural network (CNN)) with the joint spectral–spatial features only) for potato late blight disease detection with different feature extraction strategies
Unlike the traditional scalar features used in the existing machine learning/deep learning approaches, our proposed method introduces the capsule layers to learn the hierarchical structure of the late blight disease-associated spectral–spatial characteristics, which allows the capture of the rotation invariance of the late blight disease under complicated field conditions, leading to improvements in terms of the model’s accuracy, robustness and generalizability

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Potato late blight disease, caused by Phytophthora infestans (Mont.) de Bary, is one of the most destructive potato diseases, resulting in significant potato yield loss across the major potato growing areas worldwide [1,2]. The yield loss due to the infestation of late blight disease is around 30% to 100% [3,4]. The current control measure mainly relies on the application of fungicides [5], which is expensive and has negative impacts on the environment and human health due to excessive use of pesticides. The early, accurate detection of potato late blight disease is vital for effective disease control and management with minimal application of fungicides.

Methods

Results

Discussion

Conclusion