Tree's detection & health's assessment from ultra-high resolution UAV imagery and deep learning

Abstract

Detection and classification of crown trees properties and assessment of their health status have raised much interest for the scientists from the forest and environmental sciences due to their essential role in landscape ecology and forestry management. The present study proposes a method based on consumer-based UAVs and deep learning techniques for detecting individual orchard trees and assessing key properties characterising their health status. In the proposed scheme, the Mask R-CNN model is used for detecting and mapping each individual tree morphometrical property such as the height and the crown width. Tree's health assessment is based on the use of vegetation indices such as the Visual Atmospheric Resistance Index (VARI) and Green Leaf Index (GLI), computed from the visible spectrum camera mounted on the UAV platform. The use of the proposed approach is demonstrated at five different orchard tree species, namely plum, apricot, walnut, olive, and almond, located in Romania and Greece, computing a series of statistical metrics. Results returned outstanding ability of the algorithm's performance to map the individual trees and assess their health for four out of the five tree species (plum, walnut, apricot, almond) and satisfactory results for the fifth (olive trees). Overall, the study findings highlighted the promising potential of the proposed methodological framework and its scalable potential for wider applicability as a low-cost, effective solution in mapping individual trees properties and health conditions in the field.