Remote Sensing for Phenotyping and Precision Management Applications

Texas State Support CommitteeDec. 1st, 2016

Michael Starek, (PI) , Juan Landivar,  (CoPI), Chenghai Yang, (CoPI)

Cooperators:Jinha JungMurilo MaedaChu, TianxingAnjin ChangAndrea MaedaJunho Yeom

Platforms and Sensors

Data interpretation, Applications

Data analysis & visualization  

Objectives of AgriLife-TAMU-CC Remote Sensing Technology Program

Sensors and UAV platforms

• DJI Phantom 4– RGB sensor– 12 Mega Pixel

• 3DR X8+– Tetracam ADC Snap– FLIR Vue Pro R

Data Extraction Scheme• Temporal plant growth patterns

– Plant height, cm– Canopy cover, %– Biomass, Kgs / m

• Plant health (temporal)– Reflectance, NDVI– Canopy Temperature, IR

• Maturity and yield parameters– Bloom count, Maturity– Open Boll Count, yield 

• Growth Analysis– Sigmoidal growth curves, growth rates

• Simulation– GOSSYM, a process level, physiological model

Use all of the above to evaluate and rank genotypes based on specified goals (earliness, drought tolerance, diseases, nematodes, yield)

2016 Study

• 30  Cultivars• 2   Water regimes• 4   Replications• 2   Rows per entry• 10   Grids per Row• 4800 Measurements per flight

20 Blocks (1 m2) /Entry (4800 grids)(Plant height, canopy cover, canopy Volume, ExG, NDVI, canopy temperature, bloom, open boll count)

– Mosaic Images– DSM (Digital Surface Model) – DEM (Digital Elevation Model), DSM was generated from UAS data– DEM was generated from ground point (about 150 points) selected manually– CHM – Crop Height Map 

<DSM> <DEM> <CHM>

Plant Growth Analysis – Plant Height

y = 0.9278x + 0.111R² = 0.9078

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60

Plant Height 1 to 1 Linear (Plant Height)

UAS vs Observed Plant Height (m)

Spectral AnalysisCanopy Cover 

• Drawing individual crop grid– Yellow line: center line of each row– Red box: individual crop grid 

• Grid sized: 1×1 m

• Binary canopy classification– 3 parameters

• Red/Green, Blue/Green• 2×Green – Red – Green

• Canopy cover estimation- Area of white in each grid /total area

R2 = 0.437 

 Maturity RankingName/Code Breeder Max. Bloom Maturity Maturity

Cell Index GroupTAM WK‐11 Lo Hague 5.75 0.00 1.00UA 103 Commercial 5.25 1.25 1.005235 Stelly 3.88 4.69 4.00PHY333 Commercial 3.75 5.00 5.00CA 4004 Dever  3.67 5.21 5.00PHY499 Visual Check 3.66 5.23 5.00DPL 1044 Commercial 3.50 5.63 5.005338 Stelly 3.13 6.56 6.00DPL 491 Check 3.00 6.88 6.00PSC 355 Check 3.00 6.88 6.0011 HF4IPSC‐21‐01 Hague 3.00 6.88 6.00STV 6182 Commercial 2.88 7.19 7.0013‐2‐501FQ Dever  2.88 7.19 7.007‐7‐1303CT Dever  2.75 7.50 7.00CA 4003 Dever  2.75 7.50 7.0013Q‐18 Smith 2.75 7.50 7.00TAMCOT 73 Check 2.63 7.81 7.0011‐11‐307BB Dever  2.63 7.81 7.0010 WE‐11 Smith 2.63 7.81 7.0010x‐64 Hague 2.50 8.13 8.00TAM 11K‐13 ELSU Smith 2.50 8.13 8.007‐7‐519CT Dever  2.38 8.44 8.00CA 4002 Dever  2.38 8.44 8.0011‐21‐703S Dever  2.25 8.75 8.0012 BB 2139 Smith 2.13 9.06 9.005237 Stelly 2.13 9.06 9.005542 Stelly 2.13 9.06 9.00CA 4001 Dever  2.00 9.38 9.005241 Stelly 2.00 9.38 9.0013Q‐51 Smith 1.88 9.69 9.005435 Stelly 1.75 10.00 10.00

Plant Height, m

Raw Data Parameter Description

1 Early Growth Duration ‐ Days2 Early Growth ‐ Rate3 Relative Growth Rate ‐ Early4 Mid‐growth ‐ Linear ‐ Day

Plant Height 5 Mid‐growth ‐ Linear ‐ Rate Max6 Relative Growth Rate ‐ Late7 Late Growth Duration ‐ Days8 Late Growth ‐ Rate9 Duration Max. Growth‐ No of Days10 Early Growth Duration ‐ Days11 Early Growth ‐ Rate12 Relative Growth Rate ‐ Early13 Mid‐growth ‐ Linear ‐ Day

Canopy Cover 14 Mid‐growth ‐ Linear ‐ Rate Max15 Relative Growth Rate ‐ Late16 Late Growth Duration ‐ Days17 Late Growth ‐ Rate18 Duration Max. Growth‐ No of Days19 Early Growth Duration ‐ Days20 Early Growth ‐ Rate21 Relative Growth Rate ‐ Early22 Mid‐growth ‐ Linear ‐ Day

Canopy Volume 23 Mid‐growth ‐ Linear ‐ Rate Max24 Relative Growth Rate ‐ Late25 Late Growth Duration ‐ Days26 Late Growth ‐ Rate27 Duration Max. Growth‐ No of Days

28 No. Third week of Bloom29 No. Fourth week of Bloom

Bloom count 30 No. Fifth week of Bloom31 Rate of Bloom 1st to 2nd Week32 Rate of Bloom 2st to 3nd Week33 No. of Bolls34 Average Bolls area ‐ Size

Boll Count 35 Total Boll Area36 Average Boll Volume37 Total boll volume38 Maximum Value39 Time of Max, days40 Early Slope ‐ Square to Bloom

NDVI 41 Duration Max.‐ No of Days  42 Late Slope ‐ Bloom to Cutout  43 Area of Early Season

44 Area of Late Season45 Duration of Early, days46 Duration of Late, days47 Maximum Value48 Time of Max, days49 Early Slope ‐ Square to Bloom50 Duration Max.‐ No of Days

Greeness Index 51 Late Slope ‐ Bloom to Cutout  52 Area of Early Season

53 Area of Late Season54 Duration of Early, days55 Duration of Late, days56 Early ‐ Squaring57 Mid ‐ Bloom

NIR 58 Late ‐ Cutout59 Maximum Value60 Water Stress Index

Low                        HighLint Yield

         Preliminary  EvaluationName/Code Breeder Yield Canopy  Greenness Boll Area Max. Bloom No. Maturity

lbs. SC / Acre Volume, m3 Index per Row per Cell GroupPHY333 Commercial 3039 0.89 0.32 732.7 3.75 5.00DPL 491 Check 2801 0.84 0.31 703.0 3.00 6.00PSC 355 Check 2672 0.85 0.32 712.6 3.00 6.00TAMCOT 73 Check 2581 0.79 0.32 639.6 2.63 7.00DPL 1044 Commercial 2500 0.82 0.32 692.1 3.50 5.00PHY499 Check 2469 0.85 0.31 699.2 3.66 5.00UA 103 Commercial 2399 0.80 0.29 533.6 5.25 1.00STV 6182 Commercial 2058 0.75 0.29 613.5 2.88 7.00

11‐11‐307BB Dever  2644 0.80 0.33 657.4 2.63 7.00CA 4002 Dever  2472 0.85 0.33 581.1 2.38 8.0011‐21‐703S Dever  2397 0.86 0.33 668.3 2.25 8.0013‐2‐501FQ Dever  2362 0.81 0.30 631.9 2.88 7.007‐7‐1303CT Dever  2325 0.80 0.33 541.2 2.75 7.00CA 4001 Dever  2224 0.74 0.29 555.5 2.00 9.00CA 4004 Dever  2188 0.75 0.30 666.5 3.67 5.00CA 4003 Dever  1774 0.65 0.27 479.5 2.75 7.007‐7‐519CT Dever  1732 0.78 0.30 577.2 2.38 8.00Average Dever 2235 0.78 0.31 595.40 2.63 7.33

TAM WK‐11 Lo Hague 2703 0.88 0.33 707.7 5.75 1.0010x‐64 Hague 2630 0.87 0.32 727.2 2.50 8.0011 HF4IPSC‐21‐01 Hague 2500 0.86 0.31 694.6 3.00 6.00Average Hague 2611 0.87 0.32 709.81 3.75 5.00

13Q‐51 Smith 2757 0.82 0.32 713.9 1.88 9.0012 BB 2139 Smith 2698 0.87 0.32 682.9 2.13 9.0013Q‐18 Smith 2594 0.93 0.34 651.5 2.75 7.0010 WE‐11 Smith 2570 0.83 0.31 639.3 2.63 7.00TAM 11K‐13 ELSU Smith 2397 0.81 0.31 524.2 2.50 8.00Average Smith 2603 0.85 0.32 642.38 2.38 8.00

5237 Stelly 1805 0.77 0.28 551.6 2.13 9.005241 Stelly 1793 0.94 0.34 596.8 2.00 9.005235 Stelly 1662 0.78 0.29 573.7 3.88 4.005542 Stelly 1323 0.72 0.29 587.7 2.13 9.005338 Stelly 1105 0.80 0.27 455.7 3.13 6.005435 Stelly 1079 0.77 0.32 412.8 1.75 10.00Average Stelly 1461 0.80 0.30 529.72 2.50 7.83

Beltwide Cotton Research Conference Papers (Jan 4‐6, 2017 Dallas, Texas)

1. Junho Yeom1, Jinha Jung1, Anjin Chang1, Juan Landivar2 and Murilo Maeda. Open Cotton Boll Detection Methodology Using Unmanned Aerial System (UAS).  Cotton Engineering conference 

2. Jinha Jung, Juan Landivar, Anjin Chang, Junho Yeom and Murilo Maeda. Imagery Unmanned Aerial System (UAS)‐Based Asymmetric Cotton Growth Model for High Throughput Phenotyping. Cotton Engineering Conference .

3. Anjin Chang, Jinha Jung, Murilo Maeda, Juan Landivar, Henrique Cavalho and Junho Yeom. Unmanned Aerial System (UAS)‐Based Cotton Canopy Temperature Measurement System . Cotton Engineering Conference.

1. Juan Landivar, Jinha Jung, Andrea Maeda, Long Huynh and Murilo Maeda. Integration of Unmanned Aerial System (UAS) Data and Process Based Simulation Models to Forecast Crop Growth and Yield. Cotton Physiology Conference.

1. Murilo Maeda, Juan Landivar, Jinha Jung, Anjin Chang, Junho Yeom, Andrea Maeda, Josh McGinty, Juan Enciso, C. Wayne Smith, David M. Stelly, Steve Hague and Jane Dever. Unmanned Aerial System (UAS) Platforms for Cotton Breeding: Findings and Challenges.  Cotton Physiology Conference.

Peer Reviewed Publications:1. Tianxing Chu ; Ruizhi Chen ; Juan A. Landivar ; Murilo M. Maeda ; Chenghai Yang, et al. "Cotton growth modeling and assessment using 

unmanned aircraft system visual‐band imagery", J. Appl. Remote Sens. 10(3), 036018 (Aug 23, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.036018.

Submitted Manuscripts:1. Ruizhi Chen, Tianxing Chu, Juan A. Landivar, Chenghai Yang, Murilo M. Maeda (2016). Monitoring cotton (Gossypium hirsutum L.) 

germination using ultrahigh‐resolution UAS images.

2. Anjin Chang, Jinha Jung, Murilo M. Maeda, Juan Landivar (2016). Crop height monitoring with digital imagery from Unmanned Aerial System (UAS).

3. Jinha Jung, Murilo Maeda, Anjin Chang, Juan Landivar, and Joshua McGinty (2016). Unmanned Aerial System Assisted Framework for the Selection of High Yielding Cotton Genotypes.

Intellectual Property or Patent Pending:1. Growth Analysis Model Using Remote Sensing data.  Submitted  Feb. 2016

2. UASHUB for the management of large data Bases, Submitted Oct, 2016

3. Boll and Bloom count procedure for cotton phenotyping and precision management. In Preparation

4. Machine Learning procedure for cotton phenotyping management. In Preparation

Abstract, Proceedings or Presentations at Professional Meetings1. Maeda M., J. Landivar, J. McGinty, J. Jung, R. Chen, A. Chang, J. Enciso, and T. Chu. 2016. Development of a ground‐based platform for plant phenotyping and crop management decisions. In

Proc. Beltwide Cotton Conf., New Orleans, LA. 5‐7 Jan. 2016. Natl. Cotton Council. Am., Memphis, TN.

2. Landivar J., M. Maeda, J. McGinty, J. Jung, R. Chen, A. Chang, T. Chu, J. Enciso, and C. Yang. 2016. Integration of ground‐ and UAS‐platforms for the evaluation of cultivar performance(phenotyping) and experimental treatments. In Proc. Beltwide Cotton Conf., New Orleans, LA. 5‐7 Jan. 2016. Natl. Cotton Council. Am., Memphis, TN.

3. Jung J., A. Chang, J. Landivar, M. Maeda, R. Chen, T. Chu, J. Enciso, and C. Yang. 2016. Unmanned Aerial System (UAS) assisted framework for the selection of high yielding cultivars. In Proc.Beltwide Cotton Conf., New Orleans, LA. 5‐7 Jan. 2016. Natl. Cotton Council. Am., Memphis, TN.

4. Chang A., J. Jung, J. Landivar, M. Maeda, R. Chen, J. Enciso, T. Chu, C. Yang. 2016. Unmanned Aerial System (UAS) based cotton growth monitoring system. In Proc. Beltwide Cotton Conf., NewOrleans, LA. 5‐7 Jan. 2016. Natl. Cotton Council. Am., Memphis, TN.

5. Chen R., T. Chu, J. Landivar, J. Jung, C. Yang, A. Chang, J. Enciso, and M. Maeda. 2016. Unmanned Aerial System (UAS) for precision agriculture: First results from a growing cycle of cotton. InProc. Beltwide Cotton Conf., New Orleans, LA. 5‐7 Jan. 2016. Natl. Cotton Council. Am., Memphis, TN.

6. Landivar J., M. Maeda, J. Jung, A. Chang, J. McGinty, J. Enciso, A. Maeda, “Remote Sensing Technology and Unmanned Aerial Systems (UAS) for the Precision Management of Crops andNatural Resources,” Symposium on Natural Resource Management with Remote Sensor Technology. Society for Range Management National Meeting. Corpus Christi, TX. Feb 3, 2016.

7. Maeda, Murilo, Juan Landivar, Jinha Jung, Anjin Chang, Josh McGinty, Juan Enciso, and Andrea Maeda, “Remote Sensing Systems for Agricultural Research and Precision Management: AnUpdate on Current Efforts,” Natural Resource Management with Remote Sensing, Texas A&M AgriLife Research and Extension Center, Corpus Christi, TX. Feb 4, 2016.

8. Jung, Jinha (presenter), Anjin Chang, Juan Landivar, Murilo Maeda, “UAS based high throughput phenotyping system,” Natural Resource Management with Remote Sensing, Texas A&MAgriLife Research and Extension Center, Corpus Christi, TX. Feb 4, 2016.

9. Juan Landivar (presenter), Murilo Maeda, Joshua McGinty, Jinha Jung, Anjin Chang, Juan Enciso, “Integration of ground and UAS platforms for the evaluation of cultivar performance(phenotyping) and experimental treatments,” World cotton research conference, Brazil, May 2, 2016.

10. Landivar, Juan, Jinha Jung, Murilo Maeda, Anjin Chang, “Remote sensing platform for research, phenotyping, and precision management,” North American Plant Phenotyping NetworkMeeting, West Lafayette, IN., Aug 29, 2016.

11. Jung, Jinha Jung, Anjin Chang, Murilo Maeda, Juan Landivar, “UASHub: Online research collaboration portal for UAS data,” North American Plant Phenotyping Network Meeting, WestLafayette, IN., Aug 29, 2016.

Outreach Presentations and demonstrations

1. Jinha Jung (presenter) ,Murilo. Maeda, Juan. Landivar, Anjin. Chang, “UAS for precision agriculture,” District 10 and 12 County Agent Meeting, Port Mansfield, TX. Sep15, 2015.

2. Jinha Jung (presenter), Anjin Chang, Murilo Maeda, Juan Landivar, “UAS based high throughput phenotyping for agriculture applications,” ESRI FedGIS 2016,Washington DC, Feb 24, 2016.

3. Jinha Jung (presenter), Juan Landivar, Murilo Maeda, Anjin Chang, “High throughput phenotyping using UAS,” Texas UAS Summit, Austin, TX., Mar 31, 2016.4. Murilo Maeda (presenter), Juan Landivar, Jinha Jung, Anjin Chang, Josh McGinty, Juan Enciso, and Andrea Maeda, “Unmanned Aerial Systems (UASs) in Agriculture

Research and Crop Precision Management,” Medina County Roots, Hooves, & Antlers Workshop, D’Hanis, TX. Apr 29, 2016.5. Murilo Maeda (presenter), Juan Landivar, Jinha Jung, Anjin Chang, Josh McGinty, Juan Enciso, and Andrea Maeda, “Unmanned Aerial Systems (UASs) in Agriculture: A

Program Overview,” San Patricio County Crop Tour, Sinton, TX. Jun 1, 2016.6. Jinha Jung, Murilo Maeda, Anjin Chang. Demonstration of UAS‐based remote Sensing Platform. June, 2016, Nueces County Crop Tour. Texas A&M AgriLife Center,

Corpus Christi, Texas7. Landivar, Juan, Jinha Jung, Murilo Maeda. Uses of UAS in Crop and Range Land Management. July, 2016. Bandera County Agriculture Workshop, Bandera, Texas.8. Landivar, Juan, Jinha Jung, Murilo Maeda. UAS‐based platform for Research and crop Management. July, 2016. Wharton County Agriculture Workshop and Crop Tour,

El Campo, Texas.9. Landivar, Juan, Jinha Jung, Murilo Maeda. Precisions Management of crops using UAS‐Based Platforms, August, 2016. Port Lavaca, Crop workshop and tour, Port

Lavaca, Texas10. Landivar, Juan, Murilo Maeda, Jinha Jung. Remote Sensing for the Evaluation of the Performance of Cotton Genotypes. Texas A&M AgriLife Breeders Tour, August,

2016. Weslaco Texas.11. Murilo Maeda (presenter), Juan Landivar, Jinha Jung, Anjin Chang, Josh McGinty, Juan Enciso, and Andrea Maeda, “After the flight: What to do with the data?”

Managing Natural Resources with UAVs: Present & Future Possibilities, Texas A&M Kingsville, UAV Workshop, Kingsville, TX. Sep 16, 2016.12. Murilo Maeda, Juan Landivar, Jinha Jung, Anjin Chang, Josh McGinty, Juan Enciso, and Andrea Maeda, “Unmanned Aerial Systems,” Texas Ag Industries Association

South Texas Regional Meeting, Caesar Kleberg Wildlife Center, Kingsville, TX. Sep 20, 2016.13. Jung, Jinha , "UAS based high throughput phenotyping system," APHIS|ARS|Texas A&M AgriLife workshop, USDA ARS, College Station, TX. September 13, 2016.14. Jung, Jinha, "Beyond flying: Using UAS for precision agriculture," FAA / UAS Test Sites: Technical Interchange Meeting, Texas A&M University ‐ Corpus Christi, Corpus

Christi, TX. September 28, 2016.15. Landivar, Juan, Jinha Jung, "High throughput phenotyping using UAS," UAS Research Center Workshop, Oak Ridge National Laboratory, Knoxville, TN. (November 1,

2016).

Thank you

Our Thanks to 

TSSC‐Cotton Incorporated, 

Shell Oil CompanyTexas A&M AgriLife Bioenergy Funds

Texas A&M AgriLife Insect‐Vector Funds Office of the Director (AgriLife‐Research)