Estimating early season growth and biomass of field pea for selection of divergent ideotypes using proximal sensing

Tefera, Abeya Temesgen; Banerjee, Bikram Pratap; Pandey, Babu Ram; James, Laura; Puri, Ramesh Raj; Cooray, Onella O.; Marsh, Jasmine; Richards, Mark F.; Kant, Surya; Fitzgerald, Glenn J.; Rosewarne, Garry M.

doi:10.1016/j.fcr.2021.108407

Cited by 11 publications

(6 citation statements)

References 40 publications

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“…In 2021, plant height was assessed as CHM, which is an estimation of crop height using UAV imagery (Banerjee et al, 2020). CHM correlated positively with YOR and yield mostly at flowering, suggesting that this trait can be assessed using field-based phenotyping remote sensing as has been also done recently in field peas (Tefera et al, 2022). Lentil plant height is even more important under stressful conditions, as it tends to be reduced in such environments (Lake and Sadras, 2021) and assessing plant height with remote sensing can be an efficient alternative for a breeding program.…”

Section: Crop Architecturementioning

confidence: 93%

Breeding has selected for architectural and photosynthetic traits in lentils

et al. 2022

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Genetic progress in seed yield in lentils (Lens culinaris Medik) has increased by 1.1% per year in Australia over the past 27 years. Knowing which plant traits have changed through breeding during this time can give important insights as to how lentil yield has increased. This study aims to identify morphological and physiological traits that were directly or indirectly selected between 1993 and 2020 in the Australian lentil breeding program using 2 years of experimental data. Major changes occurred in plant architecture during this period. Divergent selection has seen the release of varieties that have sprawling to very upright types of canopies. Despite this genetic diversity in recently released varieties, there is an overall tendency of recently released varieties having increased plant height and leaf size with reduced number of branches. Increased light interception was positively correlated with year of release (YOR) and yield, and likely results from indirect selection of yield and taller plant types. There is an indication that recently released varieties have lower CO2 assimilation rate, stomatal conductance and canopy temperature depression (CTD) at high ambient temperatures (~30°C). Understanding lentil physiology will assist in identifying traits to increase yield in a changing climate with extreme weather events.

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Section: Crop Architecturementioning

confidence: 93%

Breeding has selected for architectural and photosynthetic traits in lentils

et al. 2022

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“…By digitizing these structures, manual measurements can be avoided and performed through efficient algorithms, thus reducing the response time of in-field monitoring. Some of the most relevant studies regarding vegetation properties address the estimation of biomass (Walter et al, 2019;Wang et al, 2021), phenotyping (Tefera et al, 2022) as well as coarse-grained and leaf-related parameters (Rosell et al, 2009). Furthermore, the reconstruction of trees represents a baseline for predicting and modeling the state of an environment, thus providing a background for precision agriculture, soil, forestry (Nită, 2021) and urban management (Gobeawan et al, 2021).…”

Section: Related Workmentioning

confidence: 99%

Reconstruction of tree branching structures from UAV-LiDAR data

Cárdenas

López²,

Ogáyar³

et al. 2022

Front. Environ. Sci.

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The reconstruction of tree branching structures is a longstanding problem in Computer Graphics which has been studied over several data sources, from photogrammetry point clouds to Terrestrial and Aerial Laser Imaging Detection and Ranging technology. However, most data sources present acquisition errors that make the reconstruction more challenging. Among them, the main challenge is the partial or complete occlusion of branch segments, thus leading to disconnected components whether the reconstruction is resolved using graph-based approaches. In this work, we propose a hybrid method based on radius-based search and Minimum Spanning Tree for the tree branching reconstruction by handling occlusion and disconnected branches. Furthermore, we simplify previous work evaluating the similarity between ground-truth and reconstructed skeletons. Using this approach, our method is proved to be more effective than the baseline methods, regarding reconstruction results and response time. Our method yields better results on the complete explored radii interval, though the improvement is especially significant on the Ground Sampling Distance In terms of latency, an outstanding performance is achieved in comparison with the baseline method.

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“…These applications rely on retrieving image-based features that can be indirectly associated with yield. A large body of literature reports the accuracy of vegetation indices (VIs) in predicting yield and other important agronomic and stress-tolerant traits at breeding plot level for multiple crops such as wheat (Kyratzis et al, 2017;Hassan et al, 2018;Hassan et al, 2019;Li et al, 2019;Fu et al, 2020;Shafiee et al, 2021;Zeng et al, 2021), soybean (Zhang et al, 2019;Maimaitijiang et al, 2020a;Maimaitijiang et al, 2020b;Roth et al, 2022;Santana et al, 2022), maize (Buchaillot et al, 2019;Adak et al, 2021;Sankaran et al, 2021), and pulse crops (Sankaran et al, 2018;Marzougui et al, 2019;Vargas et al, 2019;Valencia-Ortiz et al, 2021;Zhang et al, 2021;Tefera et al, 2022). While yield prediction using UAS-based sensing approaches has shown promising results, scaling up the application to cover large areas and/or multienvironment trials is still a major challenge (Zhang et al, 2020;Jin et al, 2021;Smith et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Remote sensing for field pea yield estimation: A study of multi-scale data fusion approaches in phenomics

et al. 2023

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IntroductionRemote sensing using unmanned aerial systems (UAS) are prevalent for phenomics and precision agricultural applications. The high-resolution data for these applications can provide useful spectral characteristics of crops associated with performance traits such as seed yield. With the recent availability of high-resolution satellite imagery, there has been growing interest in using this technology for plot-scale remote sensing applications, particularly those related to breeding programs. This study compared the features extracted from high-resolution satellite and UAS multispectral imagery (visible and near-infrared) to predict the seed yield from two diverse plot-scale field pea yield trials (advanced breeding and variety testing) using the random forest model.MethodsThe multi-modal (spectral and textural features) and multi-scale (satellite and UAS) data fusion approaches were evaluated to improve seed yield prediction accuracy across trials and time points. These approaches included both image fusion, such as pan-sharpening of satellite imagery with UAS imagery using intensity-hue-saturation transformation and additive wavelet luminance proportional approaches, and feature fusion, which involved integrating extracted spectral features. In addition, we also compared the image fusion approach to high-definition satellite data with a resolution of 0.15 m/pixel. The effectiveness of each approach was evaluated with data at both individual and combined time points.Results and discussionThe major findings can be summarized as follows: (1) the inclusion of the texture features did not improve the model performance, (2) the performance of the model using spectral features from satellite imagery at its original resolution can provide similar results as UAS imagery, with variation depending on the field pea yield trial under study and the growth stage, (3) the model performance improved after applying multi-scale, multiple time point feature fusion, (4) the features extracted from the pan-sharpened satellite imagery using intensity-hue-saturation transformation (image fusion) showed better model performance than those with original satellite imagery or high definition imagery, and (5) the green normalized difference vegetation index and transformed triangular vegetation index were identified as key features contributing to high model performance across trials and time points. These findings demonstrate the potential of high-resolution satellite imagery and data fusion approaches for plot-scale phenomics applications.

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Estimating early season growth and biomass of field pea for selection of divergent ideotypes using proximal sensing

Cited by 11 publications

References 40 publications

Breeding has selected for architectural and photosynthetic traits in lentils

Breeding has selected for architectural and photosynthetic traits in lentils

Reconstruction of tree branching structures from UAV-LiDAR data

Remote sensing for field pea yield estimation: A study of multi-scale data fusion approaches in phenomics

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