copyright 2020, mark d. mayo

Estimating Cotton Yield in Breeder Plots Using Unmanned Aerial Vehicle (UAV)

Imagery

by

Mark Douglas Mayo, B.S.

A Thesis

In

Plant and Soil Science

Submitted to the Graduate Faculty

of Texas Tech University in

Partial Fulfillment of

the Requirements for

the Degree of

MASTER OF SCIENCE

Approved

Dr. Glen Ritchie

Chair of Committee

Dr. Carlos Portillo-Quintero

Dr. Wenxuan Guo

Mark Sheridan

Dean of the Graduate School

December, 2020

Texas Tech University, Mark D. Mayo, December 2020

ii

ACKNOWLEDGEMENTS

I would like to thank Dr. Glen Ritchie, as a non-traditional student, graduate

school has been quite challenging. Dr. Ritchie has guided me through this process and

made it as straightforward as it can be. I would also like to thank the other members of

my committee, Dr. Carlos Portillo-Quintero, and Dr. Wenxuan Guo.

I would like to acknowledge Jay Pendergrass, Corey Thompson, Will Dodge,

Ryan Gregory, Tanner Drake, Jared White, and Kendall Boyd, for their assistance. I

would also like to thank my parents Darrell and Nancy Mayo, my mother-in-law Sue

Ward, for their support and encouragement along this path. Also, my daughter Dr.

Lindsay Mayo-Fincher without her inspiration, I might not have committed myself to

this task. I would like to say a special “Thank You” to my wife Rhonda for her endless

love and support.

Lastly, I would like to thank BASF for the financial support and allowing me

to continue my education.


iii

TABLE OF CONTENTS

ACKNOWLEDGEMENTS .............................................................................. ii

ABSTRACT ....................................................................................................... iv

LIST OF TABLES ............................................................................................. v

LIST OF FIGURES .......................................................................................... vi

CHAPTER I ....................................................................................................... 1

LITERATURE REVIEW ................................................................................. 1

CHAPTER II ...................................................................................................... 7

ESTIMATING COTTON YIELD USING UAV IMAGERY ....................... 7

2.1 Introduction .......................................................................................................... 7

2.2 Materials and Methods ......................................................................................... 9

2.2.1 Field Data Locations ..................................................................................... 9

2.2.2 Hardware ..................................................................................................... 16

2.2.3 Image Analysis ............................................................................................ 16

2.2.4 Evaluation of Nursery Material ................................................................... 19

2.3 Results and Discussion ....................................................................................... 19

2.4 Conclusions ........................................................................................................ 26

REFERENCES ................................................................................................. 41


iv

ABSTRACT

Traditionally, cotton breeding programs have depended on mechanical harvest

of small breeding plots to evaluate cultivars for lint yield. The primary limiting factor

in testing of breeding lines is the time and equipment costs associated with harvest;

large increases in a testing programs plot-load are not possible without additional

investment. UAVs may provide a method to evaluate early-generation progeny rows

and increase the overall volume of material evaluated without additional investment in

harvest equipment. Unmanned Aerial Vehicles (UAVs) are commonly used for high-

resolution imagery and have become popular for cotton (Gossypium hirsutum L.)

phenotyping. Advancements in UAV imagery and image analysis may enable cotton

researchers to advance cultivars based on UAV imagery yield estimates. This study

was designed to evaluate methods of cotton lint yield estimation using UAV imagery

collected before mechanical harvest. This study was conducted using data from three

cotton breeding regions, Coastal Bend, High Plains, and Rolling Plains. Images were

classified for pixel counts of lint, and boll counts were acquired by counting each

contiguous group of lint pixels. Boll counts were found to be more closely correlated

with harvested yield and visual ratings, suggesting that UAV boll counting methods

may be appropriate for large-scale field breeding trials. UAV yield estimates were

further enhanced when data was limited to analysis within pedigrees, suggesting that

aerial imagery can be useful for advancement of early-generation cultivars as an

alternative to traditional mechanical plot harvest.


v

LIST OF TABLES

Table 1. Summary of 2019 UAV and Breeder Selections........................................... 29

Table 2. Centroid-based boll estimates had improved R-Squared values when

compared to seedcotton pixel counts and lint yield correlation. ................... 30


vi

LIST OF FIGURES

Figure 1. Stages of Image Classification...................................................................... 31

Figure 2. Distribution of progeny row selections by method and filtered by

pedigree (germplasm_2) and (germplasm_3). .............................................. 32

Figure 3. Variable Yield and Similar Pixel counts: A (1477 kg ha-1, 23536

pixels), B (1974 kg ha-1, 23645 pixels). ........................................................ 33

Figure 4. Variable Pixel Counts and Similar Yield: A (1491 kg ha-1, 24123-

pixels), B (1452 kg ha-1, 45645-pixels). ........................................................ 34

Figure 5. High Plains_1 experiment, MAD design nine hundred and fifteen

entries, (6) spatially placed repeated checks for eight-five plots, 1000

total entries .................................................................................................... 35

Figure 6. High Plains_2 experiment, MAD design eight hundred and twenty

entries, (6) spatially placed repeated checks for eighty plots, 900 total

entries. ........................................................................................................... 36

Figure 7. Rolling Plains_1 experiment, MAD design three hundred thirty-six

entries, (6) spatially placed repeated checks for thirty-four plots, 370

total entries .................................................................................................... 37

Figure 8. Coastal Bend_1 experiment, RCBD design, three replications, forty-

two entries, 126 total plots. ........................................................................... 38

Figure 9. Coastal Bend_2 experiment, RCBD design, three replications, forty-

two entries, 126 total plots. ........................................................................... 39

Figure 10. Correlation with UAV centroid counts and yield were improved

when data was limited to within (pedigree_1) and (pedigree_4). ................. 40


1

CHAPTER I

LITERATURE REVIEW

Yield has been the primary selection factor for row crops, including upland

cotton (Gossypium hirsutum L.). However, cotton breeding programs were relatively

small until the 1990s, because producers retained and processed cottonseed from the

previous harvest, a practice that provided “farmer-caught” or “brown-bag” seed. This

process was protected under the Plant Variety Protection Act (Act 1970). Commercial

delinting companies provided bulk fuzzy seed transportation, acid delinting, seed

cleaning, seed treating, and finished bag storage. The seed production system kept

producer seed costs low, but it also discouraged investment in seed technology from

larger corporate partners (Alston and Venner 2002).

The seed companies with breeding programs in the Texas High Plains were

restricted in scope due to the limitations of the Texas High Plains seed sales market,

and cotton breeding programs included nursery plots and yield trials with limited

numbers of testing lines. The primary method of cultivar selection for advancement

was visual observation. Genetic lines were subjected to multi-location yield trials later

in the advancement process to test yield stability over a variety of environments, but

the volume of yield plots was limited by several factors, including yield evaluation

cost per-plot, available personnel, and harvest equipment. Because the Texas High

Plains was the center of cotton production in the United States, minimal resources


2

were focused on other cotton-growing regions, including the Rolling Plains,

Blacklands, and Coastal Bend regions of Texas.

The introduction of transgenic cotton in the Texas High Plains was popular

among producers, allowing the use of new herbicides and decreasing insect pressure.

In 1998, cotton growers on the Texas High Plains planted approximately 1.4 million

acres of Roundup Ready® cotton (White, Jones et al. 2000). The success of the

original transgenic technologies in the High Plains market increased corporate

investment in cotton research. Cotton breeding programs were expanded as well as

cotton yield testing programs (Rochester and Constable 2015). However, a new

limitation emerged with larger testing programs: the time-consuming process of plot

harvests in cotton limited how many plots could be grown and harvested.

Because cotton cultivars, or genotypes interact with diverse growing

environments in various ways, a reliable testing program is required in order to

evaluate the G x E interaction (Bednarz, Nichols et al. 2006). The testing program

would evaluate the available genetic material across locations, within the regions of

interest, and over multiple years. The stability of testing locations, year to year, would

deliver data reliability, which would provide breeders with the best information

available to make germplasm advancement decisions.

Modern cotton breeding programs consist of companion counter season

nursery increase programs that create tens of thousands of cultivars for each regional

breeding program each year. This germplasm must be evaluated each year, and

advancement decisions must be made. Approximately eighty percent of this material


3

will be discarded. Currently, field observations by plant breeders are used to evaluate

early generation nursery material, which can be a subjective approach to germplasm

selection. Advancement decisions should be made with quantitative, not qualitative

data.

The advanced germplasm will move into the testing program to be evaluated

for yield using mechanical harvest during the next stage of yield testing. The testing

program’s capacity for mechanical yield evaluation is limited by the amount of

investment in harvest equipment. Commercial harvest equipment is modified to

temporarily interrupt the flow of seedcotton and obtain a plot weight (Maja, Campbell

et al. 2016). This process is repeated thousands of times each season. Support

equipment such as cotton module builders and boll buggies are required to maintain

harvest efficiency. The seasonal output of an efficient harvest team should be

approximately fifteen-thousand plots per-cotton-harvester. Large increases in the

amount of germplasm to be evaluated will not be possible without the additional

acquisition of harvest equipment and personnel to operate. Each harvest crew has daily

limitations on production based on harvest capacity and potential problems: weather,

mechanical breakdowns, delays due to transportation of equipment.

To eliminate the restrictions of mechanical yield evaluation, the possibilities

for yield estimation with remote sensing should be evaluated. Remote sensing in

cotton has been conducted for many years. Advancements in platforms and instrument

technology have had direct effects on the usage and accuracy of remote sensing. Early

studies focused on solar reflectance measurements using stationary modular systems


4

built to support multimodular radiometer (MMR) used to separate light into its

component wavelengths (Huete and Jackson 1988). Satellite and airplanes have been

utilized as platforms to estimate ground cover fraction (GCF) (Maas and Rajan 2008,

Rajan and Maas 2009). A tethered blimp was utilized to collect aerial imagery to

estimate GCF in cotton using Normalized Difference Vegetative Index (NDVI)

(Ritchie, Sullivan et al. 2010). Previous studies were performed to evaluate off-the-

shelf cameras for determining visible and near-infrared (NIR) radiation (Ritchie,

Sullivan et al. 2008) also using a tethered blimp for a platform. Stationary towers have

also been used for remote sensing in crops, Smartfield™ FIT System allows for

precise data capture throughout the season. Canopy temperature can be obtained on

hundreds of individual plots. Mobile phenotyping platforms have been designed to

facilitate the simultaneous use of multiple sensors to collect phenotyping data such as

plant height, canopy temperature, and NDVI measurements, using the Global

Positioning System (GPS) for plot location identification. The Lee Agra 3218-GM

open rider sprayer (Lee Spider, Lubbock, TX, US) has been a popular mobile platform

(Enciso, Maeda et al. 2017, Thompson, Guo et al. 2019). Aerial-based platforms for

remote sensing are fixed-wing and rotary-wing. Fixed-wing UAVs are not commonly

used for small plot data but have greater application for commercial farm-to-crop

analysis. Rotary wing UAVs have become the most used platform for small plot data.

Rotary wing UAVs can self-land, and flight plans can be created with downloadable

applications that can control height, speed, and overlap (Pabuayon, Sun et al. 2019).


5

They have limited payload capacity and flight times up to 30 minutes per battery

charge, flight times can be extended with the use of multiple batteries.

Vegetation indices (VI) are derived from reflectance data and can be utilized to

estimate the growth stage of the cotton crop such as leaf area index or ground cover

fraction. VI measurements taken early in the crop growth cycle can provide

quantitative data for key traits of interest like seedling emergence, and seedling vigor.

Cultivars with improved emergence and seedling vigor have increased marketability in

a wider range of growing regions (Pendergrass 2019). VI measurements can be used to

estimate yield, increased plant height has been shown to correlate with increased lint

yield (Bechere, Zeng et al. 2014) in limited irrigation environments. When VI ratings

begin to decline these measurements may be used to estimate maturity or

susceptibility to disease.

High Throughput Phenotyping (HTP) has also been the focus of UAV imagery

in cotton. Frequent flights during peak bloom can provide estimates of physiological

maturity between cultivars, based on peak bloom counts. Thermal imagery can be

used to estimate canopy temperature, and this information might be a predictor of

drought response (Drake 2019).

The next logical step for UAV imagery should include mapping end-of-season

phenotypic traits and estimating yield (Ball and Konzak 1993). The low cost of UAV

imagery when compared to mechanical harvest methods creates an advantage for

UAV technology (Schirrmann, Giebel et al. 2016). Technological improvements are

increasing rapidly for UAVs and UAV sensor capability (Pabuayon, Sun et al. 2019).


6

UAV imagery is non-destructive, UAV yield estimates can be verified by utilizing

mechanical harvest data from breeder plots. Cotton breeding programs should not

limit germplasm evaluation based on the current restrictions of mechanical plot

harvest limitations.


7

CHAPTER II

ESTIMATING COTTON YIELD USING UAV IMAGERY

2.1 Introduction

Yield is the primary selection factor for cultivar advancement in upland cotton

(Gossypium hirsutum L.). The Texas High Plains can be a difficult environment for

cotton breeding due to the limited amount of harvest days each season. The expansion

of cotton breeding programs has steadily increased the volume of germplasm to be

mechanically evaluated for lint yield. An annual total of fifteen thousand plots-per-

harvester should be considered a reasonable output in a testing program. Mechanical

difficulties and weather conditions can create challenges in daily productivity. Cost-

per-plot is another factor to consider while planning trial locations, although cost

analysis is dependent upon many factors and can be difficult to measure. Long-term

investment in equipment and personnel is critical for stability in a testing program.

Volumetric increases of germplasm to be mechanically evaluated will require

additional harvest equipment and personnel.

Remote sensing has been used in cotton for many years and measurements

based on vegetation indices have been a staple of biomass estimation. Advancements

in platforms and instrument technology have had direct affects on the increased usage

and accuracy of remote sensing. The next logical step for UAV imagery should

include mapping end-of-season phenotypic traits and estimating yield. Two methods

of yield estimation were used in this study; pixel counts of lint, and centroid-based


8

boll estimates derived from contiguous groups of lint pixels, both methods had

positive linear relationships with lint yield.

The low cost of data acquisition and analysis is an advantage for UAV

technology when compared to the mechanical harvest of breeder plots. UAV imagery

is non-destructive, and verification of data can be proven prior to large-scale use.

Cotton breeding programs should not limit the volume of germplasm due to

mechanical plot harvest limitations. Breeding programs develop thousands of unique

lines, or cultivars for evaluation each breeding cycle. These cultivars must be

evaluated and either advanced to the next level of testing or discarded to make room

for the next group of germplasm. Early generation yield trials are focused on the

identification and elimination of poor performing cultivars, not necessarily

establishing a linear performance ranking of the germplasm.

UAV yield estimates can be used to advance cultivars to the next stage of

evaluation. Visual observation has long been the accepted method of germplasm

advancement in breeding nurseries. UAV yield estimates can be used to identify

superior cultivars with the same or similar pedigree lineage. Remote sensing yield

estimates can provide alternative solutions to the limitations of mechanical plot

harvest and minimize errors in germplasm advancement using visual advancement.

This study had the following objectives:

1. Compare lint pixel counts with centroid-based boll identification methods

to determine the more robust method of estimating yield in multiple cotton

breeding pedigrees.


9

2. Determine the effect of pedigree on the boll estimate-yield relationship.

3. Test the relationship among locations to determine what adjustments might

be necessary based on the growth habits of the crop.

2.2 Materials and Methods

2.2.1 Field Data Locations

To evaluate the repeatability and precision across variable environments, UAV

imagery was collected before the mechanical harvest of small breeding plots, in three

cotton breeding regions, Coastal Bend, High Plains, and Rolling Plains. Each region

utilizes agronomic practices that affect the growth pattern of the crop. Irrigation

accessibility and rainfall will encourage high yielding locations, or in some cases, lack

of access to irrigation or rainfall will result in low-to-moderate yielding locations. This

variability should be identified to determine if UAV yield estimates can be used across

cotton-growing regions.

The Coastal Bend testing sites were in Nueces County, one near Corpus Christi

(27.675° Latitude, -97.454° Longitude, Elevation 7 m asl), with the other location

being near the community of Chapman Ranch (27.622° Latitude, -97.552° Longitude,

Elevation 12 m). The Coastal Bend locations were rainfed and were within 20 miles of

each other. The soil type at Corpus and Chapman was a Southern Blackland Victoria

Clay with 0 to1 percent slopes (Fine, smectitic, hyperthermic Sodic Haplusterts)

(USDA 2008). Plot dimensions at both locations were 2.0 m wide by 9.1 m in length,

with 1.0 m row spacing. The alley width between plots was 1.5 m. The Corpus site


10

was planted on March 22, 2019, and the Chapman site was planted on March 26,

2019. Both locations had a seeding rate of 105,000 seed ha-1, and adequate soil

moisture that produced uniform stands across all experiments. Conventional tillage

and herbicides were utilized for weed control and as-needed hand weeding was

performed to maintain weed-free plots. The Coastal Bend locations received greater

than average soil moisture during early-season planting and stand establishment

period. Plant growth regulators (PGR) were utilized to control vegetative growth, two

applications of mepiquat chloride (Pix by Arysta LifeScience) were made at a rate of

0.58 L ha−1, the first at pin-head square, and the second at first bloom. However, dry

conditions during the boll-filling period limited the crop to average yields across the

area. PGR applications along with limited soil moisture, reduced the vegetative growth

of the crop, creating excellent conditions for UAV yield estimates. Corpus and

Chapman locations received harvest aid treatments. Complete defoliation required two

spray applications, the first treatment was on August 3, 2019, a tank mix application of

ethephon and cyclanilide (Finish 6 Pro by Bayer CropScience) at a rate of 1.75 L ha−1

and thidiazuron and diuron (Ginstar by Bayer CropScience) at a rate of 0.58 L ha−1,

and a final burndown application of paraquat (Gramoxone 3SL by Syngenta) at a rate

of 1.75 L ha−1 was made on August 10, 2019. The UAV data collection was conducted

nine days after the initial defoliation application at both testing locations. Mechanical

plot harvest was completed at the Corpus and Chapman locations on August 14, 2019,

and August 18, 2019, respectively.


11

UAV imagery was collected on a component line trial, this experiment was

comprised of twenty-seven sister-lines derived-from a common pedigree and fifteen

commercial lines as checks. The experiment design was Randomized Complete Block

(RCB) it contained forty-two entries and was replicated three times at each location.

The Corpus site was named Coastal Bend_1, and the Chapman site was named Coastal

Bend_2. The mean yield of Coastal Bend_1 was 1266 kg ha-1 and the mean yield of

Coastal Bend_2 was 1341 kg ha-1. The similarities among the Coastal Bend locations

such as, agronomic practices, soil type, crop height and stature, crop harvest

conditioning and defoliation method, proximity to each other, and solar radiation at

the time of UAV flight, resulted in UAV imagery data analysis that was very

repeatable across both locations and experiments.

The cotton-growing region across the High Plains in the 2019 crop year

experienced a multitude of environmental conditions. Cool soils with ample moisture

created difficulties obtaining uniform stand establishment across field experiments.

Severe weather conditions in the form of high winds and damaging hailstorms

decimated thousands of acres of cotton across the High Plains growing region. And

lastly, the early-spring severe weather pattern was followed by drought conditions

which lasted for more than ninety days without measurable precipitation.

The Floydada testing site was located in Floyd County (33.920° Latitude, -

101.361° Longitude, Elevation 965 m). The soil type was Olton Clay Loam with 0 to 1

percent slopes, (Fine, mixed, superactive, thermic Aridic Paleustolls) (USDA 2016).

Plot dimensions were 2.0 m wide by 7.6 m in length, with 1.0 m row spacing. The


12

alley width between plots was 1.5 m. Supplemental Irrigation was applied using sub-

surface drip tape, the drip tape was installed on 102-centimeter centers. The initial

planting date for the experiment was May 14, 2019 however, severe weather

comprised of hail and high winds destroyed the location on May 21, 2019. The trial

was re-planted on May 30, 2019, with a seeding rate of 109,000 seed ha-1. Adequate

soil moisture and warm soils produced uniform stands across all experiments.

Conventional tillage and herbicides were utilized for weed control and as-needed hand

weeding was performed to maintain weed-free plots. The irrigation and soil fertility

were managed for moderate yield. PGRs were utilized to control vegetative growth,

application of cyclanilide and mepiquat chloride (Stance by Bayer CropScience) was

made at a rate of 109 mL ha−1 at pin-head square. Crop maturity reached (4) nodes

above cracked boll (NACB) and harvest aid applications were made on October 10,

2019. A tank mix application of ethephon and cyclanilide (Finish 6 Pro by Bayer

CropScience) at a rate of 1.75 L ha−1 and thidiazuron and diuron (Ginstar by Bayer

CropScience) at a rate of 0.58 L ha−1. The location received a light frost on October

16, 2019, at Floydada. The UAV imagery was collected sixteen days after the initial

defoliation on October 26, 2019. The mechanical plot harvest was completed at the

Floydada location on November 1, 2019.

UAV imagery was collected on (2) preliminary level yield trials. Both trials

used an experimental design called Modified Augmented Design (MAD). This

experimental design consists of entries that are randomized and non-replicated, along

with replicated control cultivars (Schaalje, Lynch et al. 1987). The experiment named


13

High Plains_1 consisted of 915 experimental cultivars and six spatially placed

repeating checks for a total of 1,000 entries. The experiment named High Plains_2

consisted of 820 experimental cultivars and six spatially placed repeating checks for a

total of 900 entries. The mean lint yield for High Plains_1 was 1368 kg ha-1 and 1394

kg ha-1 for High Plains_2. The UAV imagery was collected before any weather-related

event. The genetic material within these yield experiments contained variability for all

qualitative and quantitative traits e.g., boll type and lint yield.

An additional testing site was placed at Southland, Texas, located in Garza

County (33.362° Latitude, -101.538° Longitude, Elevation 921 m). The soil type at

this location was Acuff Loam with 0 to 1 percent slopes (Fine-loamy, mixed,

superactive, thermic Aridic Paleustolls) (USDA 2016). Plot dimensions were 1.02 m

wide by 3.05 m in length, with 1.02 m row spacing. The alley width between plots was

1.5 m. Supplemental irrigation was applied using sub-surface drip tape. The drip tape

was installed on 204-centimeter centers. This location was critical to the breeding

program’s success due to the sensitive nature of breeding nurseries and limited seed

quantities of cultivars, there was only enough seed on hand for (1) planting, no re-

plant seed was available. The planting date for this location was extended to the last

week of May to avoid severe weather, this is a common practice for this location. The

planting date for the breeding nursery was May 28, 2019. The seeding rate was 76,000

seed ha-1, adequate soil moisture and warm soils produced uniform stands across the

breeding nursery. On June 18, 2019, severe weather, comprised of hail and high

winds, damaged the breeding nursery, some plants were eliminated, and others were


14

delayed in maturity. Conventional tillage and herbicides were utilized for weed control

and as-needed hand weeding was performed to maintain weed-free plots. The

irrigation and soil fertility were managed for moderate yield. PGRs were not utilized

due to the damaged condition of the breeding plots. Crop maturity reached (4) nodes

above cracked boll (NACB) and harvest aid applications were made on October 8,

2019. A tank mix application of ethephon and cyclanilide (Finish 6 Pro by Bayer

CropScience) at a rate of 1.75 L ha−1 and thidiazuron and diuron (Ginstar by Bayer

CropScience) at a rate of 0.58 L ha−1. The location received a light frost to further

condition the crop for harvest on October 13, 2019. The UAV imagery was collected

sixteen days after the initial defoliation on October 24, 2019. The Southland nursery

plots were commercially harvested mid-November after all visual phenotypic notes

were taken and advancement decisions were completed.

UAV imagery was collected on the breeding nursery. The nursery was planted

in a serpentine pattern, organized by pedigree with repeating checks for visual

comparisons of phenotypic traits. It consisted of six-thousand and ninety-two unique

cultivars and (5) spatially placed repeating checks for a total of six-thousand and

ninety-five entries. The UAV imagery was collected before any weather-related event.

The genetic material within the breeding nursery contained variability for all

qualitative and quantitative traits e.g., boll type and lint yield.

The Rolling Plains testing site was placed near Wall, Texas, located in Tom

Green County (31.382° Latitude, -100.266° Longitude, Elevation 566 m). The soil

type at this location was Angelo clay Loam, 0 to 1 percent slopes (Fine-silty, mixed,


15

superactive, thermic Aridic Calciustolls) (USDA 2013). Plot dimensions were 1.98 m

wide by 7.62 m in length, with 0.99 m row spacing. The alley width between plots was

1.5 m. Supplemental Irrigation was applied using sub-surface drip tape, the drip tape

was installed on 0.99-centimeter centers. The location received pre-plant irrigation and

had adequate soil moisture for stand establishment. The location was planted on May

31, 2019. The location survived a severe weather event on June 1, 2019. This storm

contained winds over 112 km/h and the location received 16.5 cm of rain. The location

produced uniform stands across all experiments. The irrigation and soil fertility were

managed for moderate yield. PGRs were utilized to control vegetative growth, the

application of cyclanilide and mepiquat chloride (Stance by Bayer CropScience) was

made at a rate of 109 mL ha−1 at pin-head square, and again at peak bloom. The

location grew off well and developed uniform test plots. Conventional tillage and

herbicides were utilized for weed control and as-needed hand weeding was performed

to maintain weed-free plots. Crop maturity reached (4) nodes above cracked boll

(NACB) and harvest aid applications were made on October 18, 2019. A tank mix

application of ethephon and cyclanilide (Finish 6 Pro by Bayer CropScience) at a rate

of 1.75 L ha−1 and thidiazuron and diuron (Ginstar by Bayer CropScience) at a rate of

0.58 L ha−1. A final burndown application of paraquat (Gramoxone 3SL by Syngenta)

at a rate of 1.75 L ha−1 was made on October 25, 2019.

UAV imagery was taken ten days after the initial defoliation application on

October 28, 2019. UAV imagery was collected on (1) preliminary level yield trial. The

Rolling Plains_1 experiment design was MAD design, randomized, and non-


16

replicated, consisting of 336 experimental cultivars and six spatially placed repeating

checks for a total of 370 entries. The Rolling Plains_1 experiment was comprised of

F2 generation cultivars, it contained one entry per pedigree, the purpose of this trial is

to assist breeders when evaluating the F4 derived F5 progeny rows. The mean lint

yield for Rolling Plains_1 was 1257 kg ha-1. The UAV imagery was collected before

any weather-related event. The genetic material within the breeding nursery contained

variability for all qualitative and quantitative traits e.g., boll type and lint yield.

2.2.2 Hardware

The UAV flight plans were implemented using DroneDeploy version 4.10

(DroneDeploy, Inc., San Francisco, CA). The UAV consisted of a DJI Inspire II fitted

with a Zenmuse X5S camera. Image resolution was defined using the ground sample

distance (GSD) of 0.64cm2/pixel. Flight altitude was 36.5m, and flight speed was

20.9km/h. Forward and side overlap was set to seventy-five percent. The UAV took

between five hundred to seven hundred-fifty images per location, these images were

stitched together to create an Orthomosaic map (Cardoso Arango and Louhaichi

2019). Agisoft Metashape was used to stitch images from the UAV (Agisoft LLC, St

Petersburg, Russia).

2.2.3 Image Analysis

GIS applications and image analysis were conducted using QGIS version 3.8

Zanzibar (QGIS 2018). Images were mapped to the WGS84 coordinate system, and

boundaries were constructed in QGIS around the rectangular plots using the vector


17

create grid feature. After spatial and image analysis, datasets were exported to

Microsoft Excel.

Image analysis on harvest-ready cotton is unique, due to the contrast between

desired and undesired pixel color, cotton lint is white and can be easily identified. The

normal color image, as shown in Figure 1, consisting of red, green, and blue (RGB)

pixels is simply converted to a grayscale image. Next, the desired range of white

pixels is identified also in Figure 1. Then the values are set to a binary scale, undesired

pixels such as soil, shadows, or crop residue will be defined with a value = 0, desired

pixels such as cotton lint will be defined with a value = 1. Binary thresholding as a

system to classify UAV images is commonly used to differentiate between desired and

undesired pixels (Dodge 2019, Young, Mahan et al. 2020) The user must provide a

thresholding value that acts to determine whether a pixel will either be thresholded to

0 or 255 depending on whether the pixel intensity is either above or below the

threshold.

Thresholding identifies pixels based on brightness values, ranging from 0 to

255 for 8-bit images. The threshold brightness values were compared to determine a

threshold level that corresponded with a minimum of false positives (other material

classified as lint pixels) and false negatives (lint pixels that were not identified within

the threshold). Threshold values varied by location but were usually centered around a

brightness value of 195 in band 3 (blue). Visual verification was completed by using

the thresholded image as a mask to test for the over-or-under estimation of lint pixels.

The desired range of white pixels varies at each field data location. Environmental


18

conditions at each location will influence the amount of solar reflectance that is

present at the time of data collection. The maturity level of the cotton crop will also

determine the level of brightness for lint classification. This process of image analysis

could be defined as “image classification using a determined binary threshold range”.

Lint pixel counts (value = 1) can now be extracted from the classified image.

Boll estimates are created by converting the classified grayscale raster (image)

to a vector (shape) file. Data filtering was performed before shapefile creation to

remove small clusters of lint pixels. These small groups of pixels may only represent a

single locule of seedcotton however, if they are not removed, they will be counted as a

complete boll. This modification was applied uniformly across the raster layer.

Next, the classified grayscale image was transformed, raster-to-vector. Small polygons

were created from each cluster of lint pixels, and larger polygons were created from

clusters of lint pixels that represented areas of overlapping bolls. To reduce errors in

the data, these large polygons were broken into smaller segments. Polygon

segmentation was applied uniformly across the vector layer, see segmentation in

Figure 1.

Polygon centroids were created to identify the center of each polygon, or

shape. A point or centroid was placed in each polygon. The centroid can be visually

identified and counted. Each centroid represents a complete cotton boll. To obtain a

boll estimate for each plot, centroid counts were extracted from the vector layer.

Visual verification of centroid-to-boll is shown in Figure 1. The centroid layer is

overlaid on the original RGB image for visual comparison.


19

2.2.4 Evaluation of Nursery Material

The final stage of the breeding nursery contains F4 derived F5 progeny rows,

which provide the largest group of cultivars within each pedigree. This large group of

material will include over ten thousand cultivars and will be eliminated by up to eighty

percent. UAV imagery and analysis using centroid-based boll estimates were

compared with breeder advancements, based on visual observation, in the 2019

breeding nursery. The UAV progeny row selections were made independently of

breeder selections. In this study, the UAV advancement standards required cultivars to

contain a boll estimate of (+1 standard deviation) or greater than the population mean

(Figure 2) this threshold could be adjusted to obtain the desired level of advanced

cultivars. UAV imagery and Breeder selections concurred on 79.4% of cultivars. UAV

imagery advanced only 14.8% of cultivars while breeders advanced 18.9% of

cultivars. Professional judgment by breeders allowed cultivars to be advanced based

on qualitative factors and perceived performance. The breeder’s selections required

two people and several weeks to complete, whereas the UAV imagery selections

required one person less than one day to complete. UAV imagery selections were

simply based on quantitative values. UAV imagery was more efficient than Breeder

selections and advanced cultivars without bias (Table 1).

2.3 Results and Discussion

Aerial imagery is susceptible to errors associated with both sensing and

identifying plant structures. These differences have been attributed to within-sensor


20

noise, spatial sampling techniques, and the observation footprints of the sensors

(Weiss, Jacob et al. 2020), but also due to the ability of remote sensors to detect and

identify structures that are obscured within a scene. Cultivars with compact fruiting

patterns and stormproof bolls will contain cotton that is obscured by branches and

other plant structures, while cultivars with more open bolls or bolls on exterior fruiting

positions will be more visible to the sensors, resulting in relatively higher yield

estimation.

The examples are shown in Figure 3 and Figure 4 demonstrate the errors that

can occur with measurements of lint pixel counts to estimate cotton yield. An example

of this divergence between pixel counts and yield is shown from images acquired from

the combined High Plains_1 and High Plains_2 experiments. By combining the data

from both experiments the variability was increased for qualitative traits that affect

sensor visibility, like plant structure and boll type. Additionally, the variability was

increased for quantitative traits that affect lint yield such as, boll weight, seed weight,

and lint turnout.

The plots in Figure 3 had similar scaled white pixel counts, ranging from

23536 to 23645 pixels per plot, but Figure 3a had a yield of 1477 kg ha-1, and Figure

3b had a yield of 1974 kg ha-1. The cultivar represented in 3a appears to have open

type canopy, as well as an open boll type. The plot has a gap approximately three feet

in length and some variability for plant density. The cultivar represented in 3b has a

compact plant canopy, with a stormproof boll type. Also, plant uniformity is good

throughout the plot, this is a very productive plot.


21

The plots in Figure 4 had similar scaled values for lint yield, ranging from

1452 to 1491 kg ha-1 with highly variable pixel counts. If we use the example shown

in 4a as a baseline at 24123 pixels-per-plot, 4b had almost twice the number of pixels

with 45645 pixels-per-plot. yet similar yields were recorded. The cultivar in 4a had an

open canopy, along with an open boll type. Some alley effect is apparent (Holman and

Bednarz 2001), as well as a missing portion of one row approximately three feet in

length. This cultivar had a value of 0.43 percent lint. The cultivar represented in figure

4b is an open plant structure, with an open boll type. Lint string-out is visibly inflating

the amount of pixels-per-boll. The fruiting pattern for this cultivar seems to be

accentuated to the exterior of the canopy. Plant density per-plot is marginal, with some

small gaps. This cultivar had a value of 0.41 percent lint, this lower value would

reduce lint yield. Percent lint is an important contributor to lint yield. (Meredith Jr and

Bridge 1973).

This type of visualization error also occurs when cotton breeders attempt to

select cultivars based solely on visual appearance (Bowman, Bourland et al. 2004).

The correlation of Centroid-based boll counts to lint yield had improved r2 values

when compared to lint pixel counts to lint yield correlation. This was consistent across

all experiments and locations (Table 2). Previous research on the yield components of

cotton has established that the volume of bolls per square meter to be a consistent

indicator of lint yield (Worley, Culp et al. 1974, Worley, Ramey et al. 1976, Jenkins,

McCarty et al. 1990, Bednarz, Bridges et al. 2000, Feng, Bufon et al. 2010, Jones, Joy

et al. 2014, Sharma, Mills et al. 2015).


22

Findings from the High Plains_1 experiment are shown in Figure 5. The

scatterplot on the left shows the relationship between centroid-based boll estimates

and lint yield, and the scatterplot on the right shows the correlation of lint pixel counts

and lint yield. Both methods of UAV yield estimates are linear and are positively

correlated. However, centroid-based boll estimates have a stronger association with

lint yield than lint pixel counts. Boll estimates resulted in regression line with a greater

r2 value and fewer outliers when compared to pixel counts. The High Plains_1

experiment had an r2 value of 0.59 when boll estimates were compared to lint yield

and an r2 value of 0.39 when lint pixel counts were correlated to lint yield. Similar

results were found when analyzing the High Plains_2 experiment. Boll estimates and

lint yield were compared resulting in an r2 value of 0.60 and lint pixel counts were

compared with lint yield resulting in an r2 value of 0.46. The cultivars contained in the

experiments, High Plains_1 and High Plains_2 were advanced to preliminary stage

testing from the progeny row nursery in 2018, using visual selection, or professional

judgment. The genetic material within these yield experiments contained variability

for qualitative traits that will affect sensor visibility and increase within-sensor noise.

Also, these cultivars contained variability for quantitative traits that are correlated with

lint yield but cannot be identified with UAV imagery e.g., boll weight, lint percent

(Meredith Jr and Bridge 1973). Lint yield was highly variable for both experiments,

High Plains_1 ranged from (387 to 2144 kg ha-1) with a mean yield of 1367 kg ha-1,

and High Plains_2 ranged from (233 to 2011 kg ha-1) with a mean yield of 1394 kg ha-

1.


23

Using the same configuration of scatterplots as in previous examples, the

Rolling Plains_1 experiment is shown in Figure 7. The scatterplot on the left

visualizes the relationship between centroid-based boll estimates and lint yield, and

the graph on the right shows the correlation of lint pixel counts to lint yield. Centroid-

based boll estimates and lint pixel counts correlated well with lint yield. Both methods

of UAV yield estimates were linear, with positively correlated associations and have

strong relationships with lint yield. Both methods of UAV yield estimates produced

acceptable results, boll estimates correlated with lint yield resulted in r2 value of 0.58,

lint pixels were compared to lint yield resulting in an r2 value of 0.54. Lint yield was

highly variable, Rolling Plains_1 ranged from (260 to 1937 kg ha-1) with a mean yield

of 1257 kg ha-1. This experiment was comprised of F2 generation cultivars and

contained one entry per pedigree. The purpose of this trial was to provide breeders

with quantitative yield data by specific pedigree for use when visually assessing these

same pedigrees as F4 derived F5 progeny rows (Tang, Jenkins et al. 1993).

Data from the Coastal Bend provided the strongest relationships for UAV

imagery. Centroid-based boll estimates, and lint pixel counts resulted in strong,

positive linear relationships when compared to mechanically harvested lint yield.

These results remained consistent for both Coastal Bend locations. The Coastal

Bend_1 experiment is shown in Figure 8. Both methods of UAV yield estimates

produced strong results, boll estimates correlated with lint yield resulted in r2 value of

0.88, and lint pixels were compared to lint yield resulting in an r2 value of 0.85. Lint


24

yield was highly variable, Coastal Bend_1 ranged from (375 to 1849 kg ha-1) with a

mean yield of 1266 kg ha-1.

The Coastal Bend_2 experiment is shown in Figure 9. This location provided

similar observations between Coastal Bend_1 and Coastal Bend_2. The scatterplot on

the right visualizes the correlation of lint pixel counts to lint yield, resulting in an r2

value of 0.75. The scatterplot on the left represents the relationship between centroid-

based boll estimates and lint yield. This comparison resulted in an r2 value of 0.80.

Both methods of lint yield estimation provided strong, positive linear relationships

with mechanically harvested lint yield. Lint yield was highly variable, Coastal Bend_2

ranged from (400 to 2175 kg ha-1) with a mean yield of 1341 kg ha-1.

The consistent results obtained from the Coastal Bend locations could be

explained by the germplasm that made-up the experiments. The experiment was

comprised of twenty-seven sister-lines derived-from a common pedigree and fifteen

commercial lines as checks. The experiment design was Randomized Complete Block

(RCB) it contained forty-two entries and was replicated three times at each location.

UAV yield estimates had improved correlation with lint yield when the data was

limited to cultivars within the same or similar genetic backgrounds, or pedigrees.

Phenotypic traits that have a direct correlation with lint yields such as boll weight,

seed weight, and lint turnout are similar within these small genetic groups. Errors in

Aerial imagery due to within-sensor noise and identification of plant structures are

minimized when the data is limited to similar cultivars originating from a common

pedigree.


25

The High Plains_1 experiment, was comprised of numerous cultivars from the

same or similar pedigrees. The number of cultivars derived-from a unique pedigree

within the experiment can range from as few as one to thirty or more lines. The

number of cultivars from each pedigree was not constant due to advancement

decisions based on qualitative traits and quantitative traits. To minimize field error, the

entries were randomized within the experiment. The correlation of boll estimates and

lint yield had an r2 value of 0.59 as shown in Figure 5. The limitations of aerial

imagery and UAV yield estimates can be minimized by limiting data analysis to

cultivars by germplasm type. The examples shown in (Figure 10) demonstrate

improved r2 values by germplasm, 0.75 for pedigree_1, and 0.74 for pedigree_4. The

scatterplots represent strong, positive linear relationships for boll estimates and lint

yield. The data points for Pedigree_1 were arranged in a linear pattern with some

points grouped ranging from 480 to 600 bolls, and lint yield of 1025 to 1575 kg ha-1.

Lint yield was highly variable for Pedigree_1, lint yields ranged from (829 to 1852 kg

ha-1) with a mean yield of 1331 kg ha-1. The data points for Pedigree_4 were arranged

in a linear pattern along the length of the regression line, a few outliers are apparent,

lint yield ranged from (1050 to 1740 kg ha-1) with a mean yield of 1355 kg ha-1. To

utilize the strengths of UAV imagery, the limitations of UAV imagery must be

defined. Data analysis, within germplasm categories, increases the accuracy of UAV

yield estimates.


26

2.4 Conclusions

The first objective of this study was to determine if UAV imagery could be

used to estimate yield in small breeder plots, and determine which method was more

robust, boll estimates, or pixel counts. Centroid-based boll counts had improved r2

values when compared to lint pixel counts and lint yield correlation. This was

consistent across all regions and experiments.

The second objective was to determine the effect of pedigree on the boll

estimate-yield relationship. Pedigree has a significant effect on the correlation of

centroid and pixel counts with lint yield. Both centroid-based boll estimates, and lint

pixel counts had improved correlation with mechanically harvested yield, when the

data was limited to cultivars within the same or similar pedigrees. Phenotypic traits

that have a direct correlation with lint yields such as boll weight, seed weight, and lint

turnout are similar within these related genetic groups. Errors in aerial imagery due to

within-sensor noise and identification of plant structures are minimized when the data

is limited to similar cultivars originating from a common pedigree.

The final objective of this study was to test the relationship among locations to

determine what adjustments might be necessary based on the growth habits of the

crop. The environmental conditions throughout the growing season influence the

precision of UAV imagery. Field selection and agronomic practices should be in place

to encourage phenotypic uniformity across the experiment and the location. Plot

replication and blocking should be used to reduce field error across elevation changes,

soil types, or irrigation differences. The use of plant growth regulators should be


27

utilized to encourage a compact fruiting pattern along with Integrated Pest

Management (IPM) program to minimize fruiting damage from insects. Also, an

Irrigation scheduling program to maintain plant health and boll set, without causing

excessive vegetative growth is suggested.

For optimum data accuracy, the UAV flight should be completed as soon as

possible after complete defoliation. Remaining plant tissue such as leaves, or small

branches will inhibit the visibility of the UAV imagery. Environmental factors such as

excessive wind, or rain can cause fragile lint to be shaken and pulled from the bolls

(Quisenberry, Dilbeck et al. 1980). UAV yield estimates are sensitive to these factors,

whereas mechanical harvesters, within limits, are not. Boll estimates were obtained

from shapefiles created from areas of lint pixels, open boll cultivars have larger areas

of lint and the image analysis will increase the number of perceived bolls per plot,

increasing the error in UAV yield estimates.

UAV yield estimates should be used as a complementary tool that is available

to researchers. Yield estimates may be useful for analysis of location statistics before

harvest. This may allow the prioritization of mechanical harvest by location, bringing

in the best data first, or elimination of a location, due to poor heritability across

experiments.

Mechanical plot harvest is reliant on the weather conditions of the day. Cotton

lint must be dry to enable the material to flow through the harvest equipment. A heavy

dew during the early morning hours can delay harvest up to two hours or more. Windy

conditions, which are common on the High Plains, will affect harvest weight accuracy


28

and slow down weight stabilization and acquisition per plot. As the sun sets, relative

humidity increases, excessive humidity will cause inaccurate plot weights and

blockages of seedcotton. UAV imagery is also susceptible to the environment, but it

only requires a brief flight time per location for success.

UAV imagery should be used as a tool to assist breeders when advancing

progeny rows to preliminary yield trials. Timeliness of progeny row selection with

UAV analysis may allow more breeding plots to be planted. Advancement decisions

of progeny rows are based on quantitative values, increasing the mean yield of

cultivars in preliminary experiments, which may ultimately improve the breeding

programs' genetic gain (Schwartz and Smith 2008).

Remote sensing yield estimates could reduce the harvest area in an offsite

location to a calibration area. This data could be used to calibrate UAV imagery and

verify the data accuracy. Resulting in an overall reduction in the volume of material to

be mechanically harvested and increasing the overall volume of genetic material to be

evaluated by the breeding program. The improvement in germplasm selection and

increase in germplasm volume should increase the breeding programs’ genetic gain.


29

Table 1. Summary of 2019 UAV and Breeder Selections

2019 Nursery row selections

Breeder & UAV Discard 4581

Breeder Advance 779

UAV Advance 517

Breeder & UAV Advance 415

sum of plots 6292

Breeder & UAV agree 79.4%

UAV Advance 14.8%

Breeder Advance 19.0%


30

Table 2. Centroid-based boll estimates had improved R-Squared values when

compared to seedcotton pixel counts and lint yield correlation.

Region and Experiment Lint Yield / Centroid

Counts r2

Lint Yield / Pixel

Counts r2

High Plains_1

High Plains_2

Rolling Plains_1

Coastal Bend_1

Coastal Bend_2

0.59

0.60

0.58

0.88

0.80

0.39

0.46

0.54

0.85

0.75


31

Figure 1. Stages of Image Classification


32

Figure 2. Distribution of progeny row selections by method and filtered by pedigree (germplasm_2) and (germplasm_3).


33

Figure 3. Variable Yield and Similar Pixel counts: A (1477 kg ha-1, 23536 pixels), B

(1974 kg ha-1, 23645 pixels).


34

Figure 4. Variable Pixel Counts and Similar Yield: A (1491 kg ha-1, 24123-pixels), B

(1452 kg ha-1, 45645-pixels).


35

Figure 5. High Plains_1 experiment, MAD design nine hundred and fifteen entries, (6) spatially placed repeated checks for

eight-five plots, 1000 total entries


36

Figure 6. High Plains_2 experiment, MAD design eight hundred and twenty entries, (6) spatially placed repeated checks for

eighty plots, 900 total entries.


37

Figure 7. Rolling Plains_1 experiment, MAD design three hundred thirty-six entries, (6) spatially placed repeated checks for

thirty-four plots, 370 total entries


38

Figure 8. Coastal Bend_1 experiment, RCBD design, three replications, forty-two entries, 126 total plots.


39

Figure 9. Coastal Bend_2 experiment, RCBD design, three replications, forty-two entries, 126 total plots.


40

Figure 10. Correlation with UAV centroid counts and yield were improved when data was limited to within (pedigree_1) and

(pedigree_4).


41

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