A new BWO-based RGB vegetation index and ensemble learning strategy for the pests and diseases monitoring of CCB trees using unmanned aerial vehicle

Introduction: The Cinnamomum Camphora var. Borneol (CCB) tree is a valuable timber species with significant medicinal importance, widely cultivated in mountainous areas but susceptible to pests and diseases, making manual surveillance costly.

Methods: This paper proposes a method for detecting CCB pests and diseases using Unmanned aerial vehicle (UAV) as an advanced data collection carrier, capable of gathering large-scale data. To tackle the high cost and challenging data processing issues associated with traditional hyper-spectral/multi-spectral sensors, this method only relies on UAV visible light RGB bands. The process first involves calculating and normalizing 24 visible light vegetation indices from the UAV RGB images of the monitoring area, along with the original RGB bands. To account for the collinearity relationship between indices, the random forest variable importance and correlation coefficient iterative analysis algorithm are employed to select indices, retaining the most important or lowest collinearity multiple vegetation indices. Subsequently, the Beluga Whale Optimization (BWO) algorithm is utilized to generate a new vegetation index, which is then combined with the multi-threshold segmentation method to propose a BWO-weighted ensemble strategy for obtaining the final pests and diseases detection results.

Results and discussion: The experimental results suggest that the new BWO-based vegetation index has a higher feature expression ability than single indices, and the new BWO-based ensemble strategy can yield more accurate detection results. This approach provides an effective means for low-cost pests and diseases detection of CCB trees.