This research investigates the capability of field-based spectroscopy (350–2500 nm) for discriminating banana plants (Cavendish subgroup Williams) infested with spider mites from those unaffected. Spider mites are considered a major threat to agricultural production, as they occur on over 1000 plant species, including banana plant varieties. Plants were grown under a controlled glasshouse environment to remove any influence other than the imposed treatment (presence or absence of spider mites). The spectroradiometer measurements were undertaken with a leaf clip over three infestation events. From the resultant spectral data, various classification models were evaluated including partial least squares discriminant analysis (PLSDA), K-nearest neighbour, support vector machines and back propagation neural network. Wavelengths found to have a significant response to the presence of spider mites were extracted using competitive adaptive reweighted sampling (CARS), sub-window permutation analysis (SPA) and random frog (RF) and benchmarked using the classification models. CARS and SPA provided high detection success (86% prediction accuracy), with the wavelengths found to be significant corresponding with the red edge and near-infrared portions of the spectrum. As there is limited access to operational commercial hyperspectral imaging and additional complexity, a multispectral camera (Sequoia) was assessed for detecting spider mite impacts on banana plants. Simulated multispectral bands were able to provide a high level of detection accuracy (prediction accuracy of 82%) based on a PLSDA model, with the near-infrared band being most important, followed by the red edge, green and red bands. Multispectral vegetation indices were trialled using a simple threshold-based classification method using the green normalised difference vegetation index (GNDVI), which achieved 82% accuracy. This investigation determined that remote sensing approaches can provide an accurate method of detecting mite infestations, with multispectral sensors having the potential to provide a more commercially accessible means of detecting outbreaks.
|Original language||English (US)|
|State||Published - Oct 30 2022|
Bibliographical noteKAUST Repository Item: Exported on 2022-11-28
Acknowledgements: This research was funded by Horticulture Innovation and the Department of Agriculture and Water Resources, Australian Government as part of its Rural R&D for Profit Program’s subproject “Multi-Scale Monitoring Tools for Managing Australia Tree Crops—Industry Meets Innovation” (grant RnD4Profit-14-01-008). The authors would like to acknowledge the support from University of Queensland Glasshouse staff for project assistance. D.W.L. acknowledges the support of Food Agility CRC Ltd., funded under the Commonwealth Government CRC Program. The CRC Program supports industry-led collaborations between industry, researchers, and the community.
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)