Automated detection of phenological transitions for yellow flowering plants such as Brassica oilseeds

Sulik, John; Long, Dan S.

doi:10.1002/agg2.20125

Cited by 9 publications

(3 citation statements)

References 27 publications

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“…Due to a number of additional factors (e.g., in-field variability of growing conditions, presence of parasites/disease agents, spectral signal of canopies rather than single plants, and shadows from overlapping plants), field phenotyping requires a slightly different approach to automated tracking of flowering in rapeseed. However, similar to our findings, the blue and green spectral bands of RGB images (e.g., used to calculate the normalized difference yellowness index, NDYI) have proven to be the most useful for distinguishing between flower and overall canola canopy signals in field settings (Sulik and Long, 2020).…”

Section: Flower Phenotyping/growth Stagessupporting

confidence: 85%

Exploiting High-Throughput Indoor Phenotyping to Characterize the Founders of a Structured B. napus Breeding Population

et al. 2022

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Phenotyping is considered a significant bottleneck impeding fast and efficient crop improvement. Similar to many crops, Brassica napus, an internationally important oilseed crop, suffers from low genetic diversity, and will require exploitation of diverse genetic resources to develop locally adapted, high yielding and stress resistant cultivars. A pilot study was completed to assess the feasibility of using indoor high-throughput phenotyping (HTP), semi-automated image processing, and machine learning to capture the phenotypic diversity of agronomically important traits in a diverse B. napus breeding population, SKBnNAM, introduced here for the first time. The experiment comprised 50 spring-type B. napus lines, grown and phenotyped in six replicates under two treatment conditions (control and drought) over 38 days in a LemnaTec Scanalyzer 3D facility. Growth traits including plant height, width, projected leaf area, and estimated biovolume were extracted and derived through processing of RGB and NIR images. Anthesis was automatically and accurately scored (97% accuracy) and the number of flowers per plant and day was approximated alongside relevant canopy traits (width, angle). Further, supervised machine learning was used to predict the total number of raceme branches from flower attributes with 91% accuracy (linear regression and Huber regression algorithms) and to identify mild drought stress, a complex trait which typically has to be empirically scored (0.85 area under the receiver operating characteristic curve, random forest classifier algorithm). The study demonstrates the potential of HTP, image processing and computer vision for effective characterization of agronomic trait diversity in B. napus, although limitations of the platform did create significant variation that limited the utility of the data. However, the results underscore the value of machine learning for phenotyping studies, particularly for complex traits such as drought stress resistance.

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Section: Flower Phenotyping/growth Stagessupporting

confidence: 85%

Exploiting High-Throughput Indoor Phenotyping to Characterize the Founders of a Structured B. napus Breeding Population

et al. 2022

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“…However, a few studies have applied comparable approaches to detect and map the spatial distribution of other plant species such as A. saligna, canola, Eucalyptus spp., etc. [11,13,24,58]. The method presented here shows the utility of high spatial resolution drone images acquired during the flowering events of H. subaxillaris over selected areas for the training and validation of landscape-scale models.…”

Section: Discussionmentioning

confidence: 99%

Unveiling the Invasion: Advancing Ecological Mapping of Heterotheca Subaxillaris Through Integrated Remote Sensing Techniques With Drones and Satellites

Gupta,

Ben-dor,

Sternberg

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Invasive plant species (IPS) pose a significant threat to natural ecosystems, causing substantial global biodiversity loss. Despite the fact that only a few species invade Israel's coastal dunes, their impact on landscape and ecosystems is notable. The invasive plant Heterotheca subaxillaris, introduced a few decades ago, has aggressively spread across these dunes, becoming a major invasive species along the Israeli coast. To provide essential spatial information for ecologists and land managers, this study developed a methodology to identify and map H. subaxillaris at various scales by integrating unmanned aerial vehicle (UAV) and satellite imagery.During the flowering period, drone images were employed to create multi-resolution ground references using the random forest classification method, achieving 97.4% accuracy for H. subaxillaris identification. These UAV-based references validated mapping results from Vision-1 and PlanetScope satellite images, with accuracies of 96.9% and 83.3%, respectively. Vision-1 image outcomes were used as a reference for extensive probabilistic mapping using Sentinel-2 images.Given the species' small size and dispersed nature, no previous study has utilized the synergy of UAV and satellite images for multi-scale mapping of H. subaxillaris. This research significantly advances detection across varying spatial resolutions, serving as a foundation for future monitoring systems with regularly acquired data. Additionally, the study offers an annual phenology-based approach for identifying flowering dates through spectral indices, contributing to phenological change investigations. The present method also helps remote sensing track IPS expansion in similar species with sparse canopy cover, providing vital geographic data for conservation and management to prevent future growth.

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“…Based on this, early-stage winter canola was mapped based on SAR images at the early-bolting stage, with F-score of 0.76 and OA of 79.23% in 2017(Figure 8). Some studies have found that red, green, blue and NIR bands can be effectively used for winter canola mapping during the flowering stage [13,40,41]. Previous studies have mainly explored the features of winter canola at the flowering stage, but rarely explored the early-stage features of winter canola.…”

Section: Important Features For Winter Canola Mappingmentioning

confidence: 99%

Early-Stage Mapping of Winter Canola by Combining Sentinel-1 and Sentinel-2 Data in Jianghan Plain China

Liu,

Li,

Zhao

et al. 2024

Preprint

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Early and accurate mapping of winter canola planting areas provides critical support for sustainable cropland management. Although some methods have been proposed to map the winter canola at the flowering or later stage, mapping winter canola planting areas at the early stage is still challenging, due to the insufficient understanding of the multi-source remote sensing features sensitive for winter canola mapping. This study proposed an early-stage winter canola area mapping method by using the combination of optical and synthetic aperture radar (SAR) data. We analyzed the spectral features, backscatter coefficients, and textural features of winter canola based on Sentinel-2 and Sentinel-1 images. Random forest (RF) and Support Vector Machine (SVM) classification models were built to map winter canola based on early-stage images and field samples in 2017 and then apply the best model to corresponding satellite data in 2018-2022. Results showed that: (1) The red edge and near-infrared-related spectral features were most important for the mapping of early-stage winter canola, followed by VV, DVI and GOSAVI, which also showed greater differences than the other features; (2) Based on Sentinel-1 and Sentinel-2 data, winter canola could be earliest mapped around 130 days prior to ripening (i.e., early overwinter stage) with the F-score over 0.85 and the OA over 81%; (3) Adding Sentinel-1 could improve the OA by about 2%-4% and the F-score by about 1% -2% in winter canola mapping. (4) The F-score of winter canola mapping based on the classifier transfer approach in 2018-2022 varied between 0.75 and 0.97, and the OA ranged from 79% to 86%. This study demonstrates the potential of early-stage winter canola mapping using the combination of Sentinel-2 and Sentinel-1 images, which could provide valuable and timely information for stakeholders and decision makers.

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Automated detection of phenological transitions for yellow flowering plants such as Brassica oilseeds

Cited by 9 publications

References 27 publications

Exploiting High-Throughput Indoor Phenotyping to Characterize the Founders of a Structured B. napus Breeding Population

Exploiting High-Throughput Indoor Phenotyping to Characterize the Founders of a Structured B. napus Breeding Population

Unveiling the Invasion: Advancing Ecological Mapping of Heterotheca Subaxillaris Through Integrated Remote Sensing Techniques With Drones and Satellites

Early-Stage Mapping of Winter Canola by Combining Sentinel-1 and Sentinel-2 Data in Jianghan Plain China

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