Identifying Optimal Wavelengths as Disease Signatures Using Hyperspectral Sensor and Machine Learning

Xing Wei,Hillary L Mehl,Marcela A Johnson,Song Li,David B Langston

doi:10.3390/rs13142833

Abstract

Hyperspectral sensors combined with machine learning are increasingly utilized in agricultural crop systems for diverse applications, including plant disease detection. This study was designed to identify the most important wavelengths to discriminate between healthy and diseased peanut (Arachis hypogaea L.) plants infected with Athelia rolfsii, the causal agent of peanut stem rot, using in-situ spectroscopy and machine learning. In greenhouse experiments, daily measurements were conducted to inspect disease symptoms visually and to collect spectral reflectance of peanut leaves on lateral stems of plants mock-inoculated and inoculated with A. rolfsii. Spectrum files were categorized into five classes based on foliar wilting symptoms. Five feature selection methods were compared to select the top 10 ranked wavelengths with and without a custom minimum distance of 20 nm. Recursive feature elimination methods outperformed the chi-square and SelectFromModel methods. Adding the minimum distance of 20 nm into the top selected wavelengths improved classification performance. Wavelengths of 501–505, 690–694, 763 and 884 nm were repeatedly selected by two or more feature selection methods. These selected wavelengths can be applied in designing optical sensors for automated stem rot detection in peanut fields. The machine-learning-based methodology can be adapted to identify spectral signatures of disease in other plant-pathogen systems.

Highlights

Peanut (Arachis hypogaea L.) is an important oilseed crop cultivated in tropical and subtropical regions throughout the world, mainly for its seeds, which contain high-quality protein and oil contents [1,2]
The peanut plant is unusual because even though it flowers above ground, the development of the pods that contain the edible seed occurs below ground [3], which makes this crop prone to soilborne diseases
For three-class classification, K-nearest neighbors (KNN), random forests (RF), support vector machine with the linear kernel (SVML), partial least square discriminant analysis (PLSDA), gradient boosting (GBoost), and XGBoost had greater accuracy compared with Naïve Bayes (NB), linear discriminant analysis (LDA), and multi-layer perceptron neural network (MLPNN) (p < 0.0001), and the average accuracy for all methods was approximately 80%

Summary

Introduction

Peanut (Arachis hypogaea L.) is an important oilseed crop cultivated in tropical and subtropical regions throughout the world, mainly for its seeds, which contain high-quality protein and oil contents [1,2]. The peanut plant is unusual because even though it flowers above ground, the development of the pods that contain the edible seed occurs below ground [3], which makes this crop prone to soilborne diseases. The infection of A. rolfsii usually occurs first on plant tissues near the soil surface mid-to-late season following canopy closure [4]. The dense plant canopy provides a humid microclimate that is conducive for pathogen infection and disease development when warm temperatures (~30 ◦ C) occur [5,6]. The dense plant canopy prevents foliar-applied fungicides from reaching below the canopy where infection of A. rolfsii initially occurs and blocks visual inspection of signs and symptoms of disease

Methods

Results

Discussion

Conclusion