A Methodology for the Automated Delineation of Crop Tree Crowns from UAV-Based Aerial Imagery by Means of Morphological Image Analysis

Abstract

The popularisation of aerial remote sensing using unmanned aerial vehicles (UAV), has boosted the capacities of agronomists and researchers to offer farmers valuable data regarding the status of their crops. This paper describes a methodology for the automated detection and individual delineation of tree crowns in aerial representations of crop fields by means of image processing and analysis techniques, providing accurate information about plant population and canopy coverage in intensive-farming orchards with a row-based plant arrangement. To that end, after pre-processing initial aerial captures by means of photogrammetry and morphological image analysis, a resulting binary representation of the land plot surveyed is treated at connected component-level in order to separate overlapping tree crown projections. Then, those components are morphologically transformed into a set of seeds with which tree crowns are finally delineated, establishing the boundaries between them when they appear overlapped. This solution was tested on images from three different orchards, achieving semantic segmentations in which more than 94% of tree canopy-belonging pixels were correctly classified, and more than 98% of trees were successfully detected when assessing the methodology capacities for estimating the overall plant population. According to these results, the methodology represents a promising tool for automating the inventorying of plants and estimating individual tree-canopy coverage in intensive tree-based orchards.

Remote Sensing Extraction of Crown Planar Area and Plant Number of Papayas Using UAV Images with Very High Spatial Resolution

“…By combining multispectral and thermal infrared images, more comprehensive growth parameters can be obtained for evaluating the quality traits of vegetation [48]. To correct the false extraction and missed extraction results in drone images, morphological methods [49] should be further considered to accurately extract on the basis of the shape of papaya crowns. For weeds and targets with similar spectral characteristics that are easily falsely extracted, the morphology-spectrum joint feature should be used to eliminate interference from similar spectra [50].…”

Detection of Cherry Tree Crown Based on Improved LA-dpv3+ Algorithm

“…Accurate tree identification is a prerequisite for canopy extraction studies. Featurebased machine-learning methods are widely used for tree identification owing to their simplicity and universality in terms of canopy textures [13][14][15], grayscales [16][17][18], and spectra [19,20]. Such image features typically do not appear individually, but are combined together in statistical analyses to achieve various types of advanced tasks [21][22][23].…”

Automatic instance segmentation of orchard canopy in unmanned aerial vehicle imagery using deep learning

“…Using a local-maxima-based technique on UAV-derived Canopy Height Models (CHMs), Mohan et al (2017) assessed the applicability of low-altitude visible light image and structurefrom-motion (SFM) algorithm). To distinguish between overlapping tree crown projections, Ponce et al (2021) developed a novel method for crop tree identification using image analysis techniques, doing away with the usage of vegetation indices and machine learning-based approaches. The aforementioned methods, however, are likely to have a low fidelity for interlaced orchards.…”