Anjin Chang

Temporal Estimates of Crop Growth in Sorghum and Maize Breeding Enabled by Unmanned Aerial Systems

We comprehensively validated the use of UAS in sorghum and maize breeding programs. Temporal estimates of plant growth will allow researchers to elucidate new phenotypes. The stage of the breeding pipeline dictates the applicability of UAS platforms. The implementation of UAS is demonstrated in different crop species. Monetary and time costs should be considered before implementation of UAS.

A ground based platform for high throughput phenotyping

Abstract The objective of this effort was to evaluate current commercially-available sensor technology (three sonic ranging and two NDVI sensors) for use in a ground-based platform for plant phenotyping and crop management decisions. The Global Positioning System (GPS) receiver from Trimble provided a high level of accuracy during our tests. Normalized Difference Vegetation Index (NDVI) data collected using the GreenSeeker sensors were more consistent and presented less variability when compared to the Decagon SRS sensor. The consistency could be due to the GreenSeeker system averaging readings of more rows. The tests also indicated that although sonic ranging sensor technology may be employed to obtain average plant height estimates, the technology is still a limiting factor for high-accuracy measurements at the plant level.

Assessing land leveling needs and performance with unmanned aerial system

Abstract. Land leveling is the initial step for increasing irrigation efficiencies in surface irrigation systems. The objective of this paper was to evaluate potential utilization of an unmanned aerial system (UAS) equipped with a digital camera to map ground elevations of a grower’s field and compare them with field measurements. A secondary objective was to use UAS data to obtain a digital terrain model before and after land leveling. UAS data were used to generate orthomosaic images and three-dimensional (3-D) point cloud data by applying the structure for motion algorithm to the images. Ground control points (GCPs) were established around the study area, and they were surveyed using a survey grade dual-frequency GPS unit for accurate georeferencing of the geospatial data products. A digital surface model (DSM) was then generated from the 3-D point cloud data before and after laser leveling to determine the topography before and after the leveling. The UAS-derived DSM was compared with terrain elevation measurements acquired from land surveying equipment for validation. Although 0.3% error or root mean square error of 0.11 m was observed between UAS derived and ground measured ground elevation data, the results indicated that UAS could be an efficient method for determining terrain elevation with an acceptable accuracy when there are no plants on the ground, and it can be used to assess the performance of a land leveling project.

Unmanned aerial system assisted framework for the selection of high yielding cotton genotypes

Abstract Recent advances in molecular breeding and bioinformatics have greatly accelerated the screening of large sets of genotypes. Development of field-level phenotyping, however, still lags behind and is considered by many as the main bottleneck to improved efficiency in breeding programs. Unmanned Aerial System (UAS) and sensor technology available today enables collection of data at high spatial and temporal scales, previously unobtainable using traditional airborne remote sensing technologies. Here, we propose an UAS-assisted high throughput phenotyping framework for cotton ( Gossypium hirsutum L.) genotype selection. UAS data collected on July 24, 2015 were used to calculate canopy cover, and UAS data collected on August 5, 2015 were used to extract open boll related phenotypic features including number of open bolls, average area of open bolls, average diameter of open bolls, perimeter of open bolls, perimeter to area ratio. Using the extracted features, a sequential selection procedure was performed on a population of 144 entries. Entries selected from the proposed framework were compared to the highest yielding entries determined by mechanical harvest results. Experimental results indicated that the selection process increased minimum and average lint yield of the remaining population by 7.4 and 10%, respectively, and UAS-selected entries and genotypes matched 80 and 73%, respectively, the same lists ranked by actual field harvest measurements.

Phenotyping of sorghum panicles using unmanned aerial system (UAS) data

Unmanned Aerial System (UAS) is getting to be the most important technique in recent days for precision agriculture and High Throughput Phenotyping (HTP). Attributes of sorghum panicle, especially, are critical information to assess overall crop condition, irrigation, and yield estimation. In this study, it is proposed a method to extract phenotypes of sorghum panicles using UAS data. UAS data were acquired with 85% overlap at an altitude of 10m above ground to generate super high resolution data. Orthomosaic, Digital Surface Model (DSM), and 3D point cloud were generated by applying the Structure from Motion (SfM) algorithm to the imagery from UAS. Sorghum panicles were identified from orthomosaic and DSM by using color ratio and circle fitting. The cylinder fitting method and disk tacking method were proposed to estimate panicle volume. Yield prediction models were generated between field-measured yield data and UAS-measured attributes of sorghum panicles.

Calibrated plant height estimates with structure from motion from fixed-wing UAV images

Field-based high-throughput phenotyping is a bottleneck to future breeding advances. The use of remote sensing with unmanned aerial vehicles (UAVs) can change the way agricultural research operates by increasing the spatiotemporal resolution of data collection to monitor status of plant growth. A fixed-wing UAV (Tuffwing) was operated to collect images of a sorghum breeding research field with 70% overlap at an altitude of 120 m. The study site was located at Texas A and M AgriLife Research’s Brazos Bottom research farm near College Station, Texas, USA. Relatively high-resolution (>2.7cm/pixel) images were collected from May to July 2017 over 880 sorghum plots (including six treatments with four replications). The collected images were mosaicked and structure from motion (SfM) calculated, which involves construction of a digital surface model (DSM) by interpolation of 3D point clouds. Maximum plant height for each genotype (plot) was estimated from the DSM and height calibration implemented with aerial measured values of groundcontrol points with known height. Correlations and RMSE values between actual height and estimated height were observed over sorghum across all genotypes and flight dates. Results indicate that the proposed height calibration method has a potential for future application to improve accuracy in plant height estimations from UAVs.

Cotton growth modeling using unmanned aerial vehicle vegetation indices

Unmanned aerial vehicle (UAV) images have great potential for agricultural researches because of their high spatial and temporal resolutions. However, most UAV researches in the agriculture field have adopted vegetation indices without second derivative parameters related with a growth model. In addition, visible band vegetation indices in UAV researches have not been explored in detail despite of their importance in UAV application. In this study, three RGB vegetation indices that showed good performance in previous studies are adopted and growth modeling using time series vegetation indices is proposed. In addition, growth model-based second derivatives are extracted for crop growth analysis. R squares of the proposed method from three RGB vegetation indices were 0.8–0.9 and excessive green vegetation index (ExG) showed the best accuracy.

Sorghum panicle extraction from unmanned aerial system data

Attributes of sorghum panicle is a very important to assess overall crop condition, irrigation, and estimation of terminal yield. In this study, a novel method to extract sorghum panicle and estimate panicle volume of grain sorghum using an Unmanned Aerial System (UAS) is proposed. UAS data were acquired with 85% overlap at an altitude of 10m above ground. Ortho-mosaic image, Digital Surface Model (DSM), and 3D point cloud were generated by applying the Structure from Motion (SfM) algorithm to the images. Ground Control Points (GCP) were used for accurate geo-referencing. Sorghum panicles were determined from RGB image and DSM by using color ratio and circle fitting. Panicle volume was estimated by the cylinder fitting method and the disk stacking method. The results of this study showed that UAS data can provide non-destructive, more efficient, and may be considered to replace the field work.