- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks
摘要： Remote sensing is important to precision agriculture and the spatial resolution provided by Unmanned Aerial Vehicles (UAVs) is revolutionizing precision agriculture workflows for measurement crop condition and yields over the growing season, for identifying and monitoring weeds and other applications. Monitoring of individual trees for growth, fruit production and pest and disease occurrence remains a high research priority and the delineation of each tree using automated means as an alternative to manual delineation would be useful for long-term farm management. In this paper, we detected citrus and other crop trees from UAV images using a simple convolutional neural network (CNN) algorithm, followed by a classification refinement using superpixels derived from a Simple Linear Iterative Clustering (SLIC) algorithm. The workflow performed well in a relatively complex agricultural environment (multiple targets, multiple size trees and ages, etc.) achieving high accuracy (overall accuracy = 96.24%, Precision (positive predictive value) = 94.59%, Recall (sensitivity) = 97.94%). To our knowledge, this is the first time a CNN has been used with UAV multi-spectral imagery to focus on citrus trees. More of these individual cases are needed to develop standard automated workflows to help agricultural managers better incorporate large volumes of high resolution UAV imagery into agricultural management operations.
关键词： UAS,tree identification,citrus,precision agriculture,CNN,feature extraction,deep learning,superpixels
更新于2025-09-23 15:21:01
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Identification of trees and their trunks from mobile laser scanning data of roadway scenes
摘要： Trees along the roads are important assets, which need continuous assessment and maintenance. The mobile laser scanning (MLS) has been adopted as mainstream mapping technique for three-dimensional data acquisition along the roads. In this study, an automated method was developed to identify trees and their trunks from MLS data. A bottom-up search in two stages is adopted in the cylinders, which are formed by partitioning of normalized MLS data. Tree trunk is identified first based on linearity and data distribution homogeneity along lower section of object clusters lying near to the respective cylinder’s base centre. Then, crown of tree is retrieved for respective identified trunk using compactness index for circular or near-circular cross section of crown and its axial symmetry about trunk axis. The object cluster composed of trunk and crown both are identified as tree. The proposed method was tested and validated on MLS data of two different roadway test sites that were acquired at different point spacing. The results reveal that the performance of proposed method in these two sites in terms of average completeness, correctness, and F1 measure was 94.4%, 100%, and 97.1%, respectively. The correctness did not change in both sites and it was 100% and stable, which showed that none of the non-tree objects was falsely identified as tree and correctness in trees identification the test site complexity. The proposed method holds great potential for identifying trees from MLS data of various roadway site conditions, where shapes and sizes of trees in their 3D data get distorted due to occlusions, and partial overlap presents among objects. Furthermore, the proposed method was implemented in the graphics processing unit-based parallel computing framework and runtime was dramatically minimized on MLS datasets of two test sites.
关键词： tree identification,trunk detection,crown retrieval,roadway scenes,parallel computing,Mobile laser scanning
更新于2025-09-12 10:27:22