Quantification of Pinus pinea L. Pinecone Productivity Using Machine Learning of UAV and Field Images - Shawn C. Kefauver; Ma. Luisa Buchaillot; Joel Segarra; Jose Armando Fernandez Gallego; Jose Luis Araus; Xavier Llosa; Mario Beltrán; Míriam Piqué (Environmental Sciences Proceedings, 2022)
Currently, for Pinus pinea L., a valuable Mediterranean forest species in Catalonia, Spain, pinecone production is quantified visually before harvest with a manual count of the number of pinecones of the third year in a selection of trees and then extrapolated to estimate forest productivity. To increase the efficiency and objectivity of this process, we propose the use of remote sensing to estimate the pinecone productivity for every tree in a whole forest (complete coverage vs. subsampling). The use of unmanned aerial vehicle (UAV) flights with high-spatial-resolution imaging sensors is hypothesized to offer the most suitable platform with the most relevant image data collection from a mobile and aerial perspective. UAV flights and supplemental field data collections were carried out in several locations across Catalonia using sensors with different coverages of the visible (RGB) and near-infrared (NIR) spectrum. Spectral analyses of pinecones, needles, and woody branches using a field spectrometer indicated better spectral separation when using near-infrared sensors. The aerial perspective of the UAV was anticipated to reduce the percentage of hidden pinecones from a one-sided lateral perspective when conducting manual pinecone counts in the field. The fastRandomForest WEKA segmentation plugin in FIJI (Fiji is just ImageJ) was used to segment and quantify pinecones from the NIR UAV flights. The regression of manual image-based pinecone counts to field counts was R2 = 0.24; however, the comparison of manual image-based counts to automatic image-based counts reached R2 = 0.73. This research suggests pinecone counts were mostly limited by the perspective of the UAV, while the automatic image-based counting algorithm performed relatively well. In further field tests with RGB color images from the ground level, the WEKA fastRandomForest demonstrated an out-of-bag error of just 0.415%, further supporting the automatic counting machine learning algorithm capacities.