Abstract
Phytosanitary conditions can hamper the normal development of trees and significantly impact their yield. The phytosanitary condition of chestnut stands is usually evaluated by sampling trees followed by a statistical extrapolation process, making it a challenging task, as it is labor-intensive and requires skill. In this study, a novel methodology that enables multi-temporal analysis of chestnut stands using multispectral imagery acquired from unmanned aerial vehicles is presented. Data were collected in different flight campaigns along with field surveys to identify the phytosanitary issues affecting each tree. A random forest classifier was trained with sections of each tree crown using vegetation indices and spectral bands. These were first categorized into two classes: (i) absence or (ii) presence of phytosanitary issues. Subsequently, the class with phytosanitary issues was used to identify and classify either biotic or abiotic factors. The comparison between the classification results, obtained by the presented methodology, with ground-truth data, allowed us to conclude that phytosanitary problems were detected with an accuracy rate between 86% and 91%. As for determining the specific phytosanitary issue, rates between 80% and 85% were achieved. Higher accuracy rates were attained in the last flight campaigns, the stage when symptoms are more prevalent. The proposed methodology proved to be effective in automatically detecting and classifying phytosanitary issues in chestnut trees throughout the growing season. Moreover, it is also able to identify decline or expansion situations. It may be of help as part of decision support systems that further improve on the efficient and sustainable management practices of chestnut stands.
Highlights
Chestnut trees (Castanea sativa Mill.) are one of the most important species in Portugal for both forestry and agricultural purposes
As for studies related to chestnut trees, orthophoto mosaics obtained through photogrammetric processing of high-resolution imagery acquired from unmanned aerial vehicles (UAVs) were used by Martins et al [15] to monitor 231 ha of chestnut trees
C(co,dn).sidering trees with no visible symptoms (22 trees, ~48% of the total number of trees) and trees otherwise affected by phytosanitary issues (24 trees affected by ink disease or/and nutritional deficiencies), the former represents between 63% to 67% of the crown area along the flight campaigns
Summary
Chestnut trees (Castanea sativa Mill.) are one of the most important species in Portugal for both forestry and agricultural purposes. As for studies related to chestnut trees, orthophoto mosaics obtained through photogrammetric processing of high-resolution imagery acquired from UAVs were used by Martins et al [15] to monitor 231 ha of chestnut trees By comparing these data with aerial imagery acquired almost ten years earlier, it was possible to measure areas of new plantations and to assess the decline of chestnut trees. A novel method based on image processing was proposed in Marques et al [17], enabling the automatic monitoring of chestnut trees through estimation of some of the main parameters, such as tree height and crown diameter and area By applying this methodology, multi-temporal analysis was possible both at the tree and plantation level. Despite the numerous advances in monitoring chestnut trees provided by the use of UAV-based high-resolution aerial imagery, little progress has been made in both automatic detection and classification of the biotic or abiotic factors that can affect them. The proposed methodology is able to distinguish healthy chestnut trees, and it can identify which is the specific limiting factor affecting the development of each tree
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