Digital Phenotyping in Plant Breeding: Evaluating Relative Maturity, Stand Count, and Plant Height in Dry Beans via RGB Drone-Based Imagery and Deep Learning Approaches

The datasets comprised were collected from the MSU dry bean breeding program in 2022 planting season at SVREC location. The dataset includes the orthomosaic (.tif) generated using the raw images, raw images (.jpeg) from two flight altitude, plot boundary delimitations (.shp), clipped plots, annotations, Faster R-CNN deep learning model, and the ground-truth (GT) notes to predict stand count (SC).

/a._Orthomosaics: This directory contains 1 file 

                1. Image naming structure: `Date-of-flight< Month-Day-Year>_<sensor>_<format-of-image>/`

Orthomosaic (TIFF image) collected around VC growth stage containing readable EXIF headers with image metadata.

/b._Shapefiles

Plot boundaries files (.shp) and field area from 2022 SVREC location containing plot level information using the breeding program metadata.

/c._ClipPlots

1. Image naming structure: `Experiment-PlotID<Experiment-name>_<Plot-ID>_<flight-altitude>`

PNG images clipped from the orthomosaic of 2022 SVREC location.

/d._Annotations__1

VGG project containing the bean plant annotations via boundary boxes with x, y, height and width coordinates.  

/e._Resize_img_annot__2

VGG project containing the bean plant annotations via boundary boxes with x, y, height and width coordinates.  

/f._HyperTunning__3

Model used to perform the hyperparameter tunning.

/g._PseudoLab__4: Pseudo labeling using repetitions 3 and 4 from the 2022 SVREC location. This folder contains a subfolder:

1. Image naming structure: `Experiment-PlotID<Experiment-name>_<Plot-ID>_<flight-altitude>`

PNG images clipped from the orthomosaic of rep 2 and 4 of the 2022 SVREC location.

/h._TrainModel__5

Model used to perform the training.

/i._TestingModel1:

This folder contains orthomosaic (.tif), shapefile (.shp), clipped plots (.png) and the inference model to early flight date.

/i._TestingModel2

This folder contains orthomosaic (.tif), shapefile (.shp), clipped plots (.png), new set of annotations, and the inference model to lower flight altitude (6 meters).

/i._TestingModel3

This folder contains orthomosaic (.tif), shapefile (.shp), clipped plots (.png) and the inference model to higher flight altitude (10 meters).

/j._Mask_count_Seg-CNN

Traditional methods using mask via R and Python programing to perform stand count (SC). Also, the CNN segmented model is available to classify between soil and vegetation.

/Raw_img3_6_13_22_SVREC_RGB_SC_7m_1: Raw images (.jpeg) collected at 7 meters of flight altitude.

/Raw_img3_6_13_22_SVREC_RGB_SC_7m_2: Cont. Raw images (.jpeg) collected at 7 meters of flight altitude.

/Raw_img4_6_13_22_SVREC_RGB_SC_10m_1: Raw images (.jpeg) collected at 10 meters of flight altitude.

/Raw_img4_6_13_22_SVREC_RGB_SC_10m_2: Cont. Raw images (.jpeg) collected at 10 meters of flight altitude.