Application of Random Forest Classification to Detect the Pine Wilt Disease from High Resolution Spectral Images

Abstract

Pine Wilt Disease is one of the forest pests with high destructive potential, due to its random spreading and the fast evolution of the symptoms. The correct identification of infected trees is critical for the containment of the pest in affected areas. This paper exploits the capabilities of Random Forest classification algorithms designed to spot the infected trees based on remote sensing images. We use as input both multi-and hyperspectral imagery with high spatial resolution, acquired via remotely piloted airborne systems in infected Portuguese forests. For both imagery types, the classification schemes achieve accuracies higher than 0.91. We conclude that Random Forest classification is a feasible method to detect the Pine Wilt Disease in spectral images acquired over wild forests, even at early stages of the infestation.

Detection of Pine-Wilt-Disease-Affected Trees Based on Improved YOLO v7

“…Ultimately, YOLO v7-SE exhibited the best performance, achieving an F1 score of 0.9117, a recall rate of 0.8958, and detection accuracy of 0.9281. These results exceed the accuracy achieved using the RF (Random Forest) method (0.91) [4] and the average accuracy achieved using the SVM (Support Vector Machine) method (0.9036) [6]. Moreover, the F1 score (0.79) obtained from detection outcomes using the SCANet recognition network introduced by Qin et al [13] underscores the superior effectiveness of our research results.…”

“…Initial stages of remote sensing image recognition have relied on traditional machine learning algorithms to extract artificial design features from images and training models. The Random Forest classifier has been used to detect and classify symptomatic trees, achieving an accuracy higher than 0.91 on both high spatial resolution multispectral and hyperspectral images [4]. Integrating a multiscale segmentation algorithm with an object-oriented approach has optimized the feature space of segmentation results, enabling accurate and rapid identification and classification of symptomatic trees [5].…”

UAV-Based Forest Health Monitoring: A Systematic Review

“…Moreover, needle or leaf samples were taken to identify the presence of nematodes [106,142,146], to quantify pigments under laboratory conditions [85,105], and to assess their water content and spectral properties [123,136]. Soil sampling was conducted to determine possible origins of nutritional deficiencies [109] and to measure the field capacity [147].…”

“…Repetitive UAV surveys of the same area were designed to collect time series as a database for change analysis. Hence, the scientists analyzed spectral and structural changes over time to assess mechanical crown damage [97,98], fire damage based on pre-and postfire data [99][100][101], phenological differences [89], and different stages of stress-induced symptoms evident in the tree canopy [92,94,[102][103][104][105][106][107][108][109][110][111][112]. The primary period of FHM typically lay within the growing season.…”

“…Others used the opportunity to get a clear view of the ground, enabling the creation of an improved photogrammetric digital terrain model (DTM) [114]. Only in six papers was the collection of UAV data over a period of more than a year described [97,98,105,106,108,115]. The longest and most comprehensive time series covered five years of monthly drone flights to study the influence of heavy rain events on tree fall and branch fall rates [97].…”