Classification of soybean frogeye leaf spot disease using leaf hyperspectral reflectance.

In this study, the feasibility of classifying soybean frogeye leaf spot (FLS) is investigated. Leaf images and hyperspectral reflectance data of healthy and FLS diseased soybean leaves were acquired. First, image processing was used to classify FLS to create a reference for subsequent analysis of hyperspectral data. Then, dimensionality reduction methods of hyperspectral data were used to obtain the relevant information pertaining to FLS. Three single methods, namely spectral index (SI), principal component analysis (PCA), and competitive adaptive reweighted sampling (CARS), along with a PCA and SI combined method, were included. PCA was used to select the effective principal components (PCs), and evaluate SIs. Characteristic wavelengths (CWs) were selected using CARS. Finally, the full wavelengths, CWs, effective PCs, SIs, and significant SIs were divided into 14 datasets (DS1-DS14) and used as inputs to build the classification models. Models' performances were evaluated based on the classification accuracy for both the overall and individual classes. Our results suggest that the FLS comprised of five classes based on the proportion of total leaf surface covered with FLS. In the PCA and SI combination model, 5 PCs and 20 SIs with higher weight coefficient of each PC were extracted. For hyperspectral data, 20 CWs and 26 effective PCs were also selected. Out of the 14 datasets, the model input variables provided by five datasets (DS2, DS3, DS4, DS10, and DS11) were more superior than those of full wavelengths (DS1) both in support vector machine (SVM) and least squares support vector machine (LS-SVM) classifiers. The models developed using these five datasets achieved overall accuracies ranging from 91.8% to 94.5% in SVM, and 94.5% to 97.3% in LS-SVM. In addition, they improved the classification accuracies by 0.9% to 3.6% (SVM) and 0.9% to 3.7% (LS-SVM).

Interaction between two adjacent grounded sources in frequency domain semi-airborne electromagnetic survey.

Multi-source and multi-frequency emission method can make full use of the valuable and short flight time in frequency domain semi-airborne electromagnetic (FSAEM) exploration, which has potential to investigate the deep earth structure in complex terrain region. Because several sources are adjacent in multi-source emission method, the interaction of different sources should be considered carefully. An equivalent circuit model of dual-source is established in this paper to assess the interaction between two individual sources, where the parameters are given with the typical values based on the practical instrument system and its application. By simulating the output current of two sources in different cases, the influence from the adjacent source is observed clearly. The current waveforms show that the mutual resistance causes the fluctuation and drift in another source and that the mutual inductance causes transient peaks. A field test with dual-source was conducted to certify the existence of interaction between adjacent sources. The simulation of output current also shows that current errors at low frequency are mainly caused by the mutual resistance while those at high frequency are mainly due to the mutual inductance. Increasing the distance between neighboring sources is a proposed measure to reduce the emission signal errors with designed ones. The feasible distance is discussed in the end. This study gives a useful guidance to lay multi sources to meet the requirement of measurement accuracy in FSAEM survey.