of the manuscript. Competing interests: The authors declare that they have no competing interests. Data Availability Developed software and analysis are available in the GitHub repositories at https://github.com/msudrybeanbreeding and datasets can be download at Zenodo deposit page ( https://zenodo.org/ ) using the links to RM: https://doi.org/10.5281/zenodo.7922565; SC: https://doi.org/10.5281/zenodo.7922584; and PH: https://doi.org/10.5281/zenodo.7922589 . Supplementary Materials Supplementary 1 Figs. S1 to S14 Tables S1 and S2 Data files S1 to 21 References 1. Uebersax MA , Cichy KA , Gomez FE , Porch TG , Heitholt J , Osorno JM , Kamfwa K , Snapp SS , Bales S . Dry beans ( Phaseolus vu
Open resource ↗zenodo · 10.5281/zenodo.7922565 · lines:677-730Unverified paper record
Drone-Based Digital Phenotyping to Evaluating Relative Maturity, Stand Count, and Plant Height in Dry Beans (Phaseolus vulgaris L.)
Plant Phenomics · 28 Nov 2024 · 10.34133/plantphenomics.0278
Abstract
Substantial effort has been made in manually tracking plant maturity and to measure early-stage plant density and crop height in experimental fields. In this study, RGB drone imagery and deep learning (DL) approaches are explored to measure relative maturity (RM), stand count (SC), and plant height (PH), potentially offering higher throughput, accuracy, and cost-effectiveness than traditional methods. A time series of drone images was utilized to estimate dry bean RM employing a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) model. For early-stage SC assessment, Faster RCNN object detection algorithm was evaluated. Flight frequencies, image resolution, and data augmentation techniques were investigated to enhance DL model performance. PH was obtained using a quantile method from digital surface model (DSM) and point cloud (PC) data sources. The CNN-LSTM model showed high accuracy in RM prediction across various conditions, outperforming traditional image preprocessing approaches. The inclusion of growing degree days (GDD) data improved the model's performance under specific environmental stresses. The Faster R-CNN model effectively identified early-stage bean plants, demonstrating superior accuracy over traditional methods and consistency across different flight altitudes. For PH estimation, moderate correlations with ground-truth data were observed across both datasets analyzed. The choice between PC and DSM source data may depend on specific environmental and flight conditions. Overall, the CNN-LSTM and Faster R-CNN models proved more effective than conventional techniques in quantifying RM and SC. The subtraction method proposed for estimating PH without accurate ground elevation data yielded results comparable to the difference-based method. Additionally, the pipeline and open-source software developed hold potential to significantly benefit the phenotyping community.
Plant phenotyping relevance
ドローン画像と深層学習を用いて成熟度、株数、草丈を推定する手法を開発・評価し、パイプラインとオープンソースソフトウェアも提示しており、植物表現型取得が研究の中心である。
abstractIn this study, RGB drone imagery and deep learning (DL) approaches are explored to measure relative maturity (RM), stand count (SC), and plant height (PH)
abstractAdditionally, the pipeline and open-source software developed hold potential to significantly benefit the phenotyping community.
Code and data availability
The paper explicitly states that all R/Python analysis code, apps, and the complete datasets (orthomosaics, shapefiles, ground notes, clipped plots) are publicly available via the authors' GitHub organization and three Zenodo deposits for RM, SC, and PH.
authors declare that they have no competing interests. Data Availability Developed software and analysis are available in the GitHub repositories at https://github.com/msudrybeanbreeding and datasets can be download at Zenodo deposit page ( https://zenodo.org/ ) using the links to RM: https://doi.org/10.5281/zenodo.7922565; SC: https://doi.org/10.5281/zenodo.7922584; and PH: https://doi.org/10.5281/zenodo.7922589 . Supplementary Materials Supplementary 1 Figs. S1 to S14 Tables S1 and S2 Data files S1 to 21 References 1. Uebersax MA , Cichy KA , Gomez FE , Porch TG , Heitholt J , Osorno JM , Kamfwa K , Snapp SS , Bales S . Dry beans ( Phaseolus vulgaris L.) as a vital component of sustainab
Open resource ↗zenodo · 10.5281/zenodo.7922584 · lines:677-730rests. Data Availability Developed software and analysis are available in the GitHub repositories at https://github.com/msudrybeanbreeding and datasets can be download at Zenodo deposit page ( https://zenodo.org/ ) using the links to RM: https://doi.org/10.5281/zenodo.7922565; SC: https://doi.org/10.5281/zenodo.7922584; and PH: https://doi.org/10.5281/zenodo.7922589 . Supplementary Materials Supplementary 1 Figs. S1 to S14 Tables S1 and S2 Data files S1 to 21 References 1. Uebersax MA , Cichy KA , Gomez FE , Porch TG , Heitholt J , Osorno JM , Kamfwa K , Snapp SS , Bales S . Dry beans ( Phaseolus vulgaris L.) as a vital component of sustainable agriculture and food security—A review . Legu
Open resource ↗zenodo · 10.5281/zenodo.7922589 · lines:677-730s from each individual breeding plot were extracted from the time series of images (6 and 9 flights date), and the RM was estimated using an optimized threshold value of 0.06. To perform the VI extractions from each breeding plot in the field, an open-source Streamlit app in Python was implemented and can be accessed online at: https://msudrybeanbreeding-vegetation-index--vi-extractions-v0-3-9knpzt.streamlit.app/ . Additionally, to accommodate user preferences, an R script is available to perform VI extractions analysis (Data S7 ). SC DL model The SC pipeline deployed in this study comprised 6 distinct steps, starting from the raw images and annotations, and ending with the final SC predicti
Open resource ↗lines:139-147This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.