Early Detection of Dendroctonus valens Infestation with Machine Learning Algorithms Based on Hyperspectral Reflectance

Abstract

The red turpentine beetle (Dendroctonus valens LeConte) has caused severe ecological and economic losses since its invasion into China. It gradually spreads northeast, resulting in many Chinese pine (Pinus tabuliformis Carr.) deaths. Early detection of D. valens infestation (i.e., at the green attack stage) is the basis of control measures to prevent its outbreak and spread. This study examined the changes in spectral reflectance after initial attacking of D. valens. We also explored the possibility of detecting early D. valens infestation based on spectral vegetation indices and machine learning algorithms. The spectral reflectance of infested trees was significantly different from healthy trees (p < 0.05), and there was an obvious decrease in the near-infrared region (760–1386 nm; p < 0.01). Spectral vegetation indices were input into three machine learning classifiers; the classification accuracy was 72.5–80%, while the sensitivity was 65–85%. Several spectral vegetation indices (DID, CUR, TBSI, DDn2, D735, SR1, NSMI, RNIR•CRI550 and RVSI) were sensitive indicators for the early detection of D. valens damage. Our results demonstrate that remote sensing technology could be successfully applied to early detect D. valens infestation and clarify the sensitive spectral regions and vegetation indices, which has important implications for early detection based on unmanned airborne vehicle and satellite data.

Assessment of Injury by Four Major Pests in Soybean Plants Using Hyperspectral Proximal Imaging

“…Other recent studies used machine or deep learning methods to detect pests based on plant responses, achieving high classification accuracy [17,70,[72][73][74]. However, these studies could not classify the level of infestation, which is the great challenge.…”

“…The MLP-ANN model had the best performance for discriminating C. includensinfested plants (overall accuracy of 75%). Although the overall accuracy for discriminating C. includens-infested plants had been highest, the recall (sensitivity) also deserves attention for soybean managers because it represents the missed detection rate of infested plants ("negative false") [72]. In these cases, the average recall was 72%.…”