Growth Analysis of Wheat Using Machine Vision: Opportunities and Challenges
Abstract
:1. Introduction
2. Materials and Methods
2.1. Genotypes and Growing Condition
2.2. Experimental and Imaging Procedure
2.3. Image Analysis
2.4. Trait Measurement
2.5. Data Analysis
3. Results
3.1. Low Throughput Phenotyping (LTP)
3.2. The Predictive Performance of High Throughput Phenotyping
3.3. Relative Growth Rate Measurements on the Basis of Low- and High-Throughput Traits
3.4. Side-Projection Area Efficiency during Growth
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Williams, R.F. The Physiology of Plant Growth with Special Reference to the Concept of Net Assimilation Rate. Ann. Bot. 1946, 10, 41–72. [Google Scholar] [CrossRef]
- Kajfeẑ-Bogataj, L.; Hočevar, A. Modelling of Net Photosynthetic Productivity for Buckwheat (Fagopyrum esculentum) Moench. Agric. Forest Meteorol. 1989, 44, 233–244. [Google Scholar] [CrossRef]
- Blackman, V.H. The Compound Interest Law and Plant Growth. Ann. Bot. 1919, 33, 353–360. [Google Scholar] [CrossRef] [Green Version]
- Wilhelm, W.W.; Ruwe, K.; Schlemmer, M.R. Comparison of Three Leaf Area Index Meters in a Corn Canopy. Crop Sci. 2000, 40, 1179–1183. [Google Scholar] [CrossRef]
- Morales, E.; Morales-Rosales, E.; Díaz-López, E.; Cruz-Luna, A.; Medina-Arias, N.; Cruz, M. Net Assimilation Rate and Sunflower Seed Yield as a Function of Urea and Slow Release Urea. Agrociencia 2015, 49, 163–176. [Google Scholar]
- Setiyono, T.D.; Weiss, A.; Specht, J.E.; Cassman, K.G.; Dobermann, A. Leaf Area Index Simulation in Soybean Grown under Near-Optimal Conditions. Field Crop. Res. 2008, 108, 82–92. [Google Scholar] [CrossRef] [Green Version]
- Apáez Barrios, P.; Escalante Estrada, J.A.; González, R.; Chávez, M. Analysis of Cowpea Growth and Production in Maize Trellis with Nitrogen and Phosphorus. Int. J. AgriSci. 2014, 4, 102–108. [Google Scholar]
- Cobb, J.N.; DeClerck, G.; Greenberg, A.; Clark, R.; McCouch, S. Next-Generation Phenotyping: Requirements and Strategies for Enhancing Our Understanding of Genotype-Phenotype Relationships and Its Relevance to Crop Improvement. Theor. Appl. Genet. 2013, 126, 867–887. [Google Scholar] [CrossRef] [Green Version]
- Addison, C.K.; Mason, R.E.; Brown-Guedira, G.; Guedira, M.; Hao, Y.; Miller, R.G.; Subramanian, N.; Lozada, D.N.; Acuna, A.; Arguello, M.N.; et al. QTL and Major Genes Influencing Grain Yield Potential in Soft Red Winter Wheat Adapted to the Southern United States. Euphytica 2016, 209, 665–677. [Google Scholar] [CrossRef]
- Hoffstetter, A.; Cabrera, A.; Sneller, C. Identifying Quantitative Trait Loci for Economic Traits in an Elite Soft Red Winter Wheat Population. Crop Sci. 2016, 56, 547–558. [Google Scholar] [CrossRef]
- Lozada, D.N.; Mason, R.E.; Sukumaran, S.; Dreisigacker, S. Validation of Grain Yield QTLs from Soft Winter Wheat Using a CIMMYT Spring Wheat Panel. Crop Sci. 2018, 58, 1964–1971. [Google Scholar] [CrossRef]
- Richards, R.; Rebetzke, G.; Watt, M.; Spielmeyer, W.; Dolferus, R. Breeding for Improved Water Productivity in Temperate Cereals: Phenotyping, Quantitative Trait Loci, Markers and the Selection Environment. Func. Plant Biol. 2010, 37, 85–97. [Google Scholar] [CrossRef]
- Fahlgren, N.; Gehan, M.A.; Baxter, I. Lights, Camera, Action: High-Throughput Plant Phenotyping is Ready for a Close-Up. Curr. Opin. Plant Biol. 2015, 24, 93–99. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gardner, F.P.; Pearce, R.B.; Mitchell, R.L. Physiology of Crop Plants, 1st ed.; Iowa State University Press: Ames, IA, USA, 1985. [Google Scholar]
- Costa, C.; Schurr, U.; Loreto, F.; Menesatti, P.; Carpentier, S. Plant Phenotyping Research Trends, a Science Mapping Approach. Front. Plant Sci. 2019, 9, 1933. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yang, W.; Guo, Z.; Huang, C.; Duan, L.; Chen, G.; Jiang, N.; Fang, W.; Feng, H.; Xie, W.; Lian, X.; et al. Combining High-Throughput Phenotyping and Genome-Wide Association Studies to Reveal Natural Genetic Variation in Rice. Nat. Commun. 2014, 5, 5087. [Google Scholar] [CrossRef] [PubMed]
- Furbank, R.T.; Tester, M. Phenomics—Technologies to Relieve the Phenotyping Bottleneck. Trends Plant Sci. 2011, 16, 635–644. [Google Scholar] [CrossRef]
- Cui, M.-L.; Copsey, L.; Green, A.A.; Bangham, J.A.; Coen, E. Quantitative Control of Organ Shape by Combinatorial Gene Activity. PLoS Biol. 2010, 8, e1000538. [Google Scholar] [CrossRef] [Green Version]
- Kang, S.B.; Quan, L. Image-Based Modeling of Plants and Trees; Synthesis Lectures on Computer Vision; Morgan & Claypool: San Rafael, CA, USA, 2010. [Google Scholar]
- Ellis, J.; Dodds, P.; Pryor, T. The Generation of Plant Disease Resistance Gene Specificities. Trends Plant Sci. 2000, 5, 373–379. [Google Scholar] [CrossRef]
- Campillo, C.; Prieto, M.H.; Daza, C.; Moñino, M.J.; García, M.I. Using Digital Images to Characterize Canopy Coverage and Light Interception in a Processing Tomato Crop. HortScience 2008, 43, 1780–1786. [Google Scholar] [CrossRef]
- Easlon, H.M.; Bloom, A.J. Easy Leaf Area: Automated Digital Image Analysis for Rapid and Accurate Measurement of Leaf Area. Appl. Plant Sci. 2014, 2, 1400033. [Google Scholar] [CrossRef]
- Tang, X.; Liu, M.; Zhao, H.; Tao, W. Leaf Extraction from Complicated Background. In Proceedings of the 2009 2nd International Congress on Image and Signal Processing, Tianjin, China, 17–19 October 2009; pp. 1–5. [Google Scholar] [CrossRef]
- Woebbecke, D.M.; Meyer, G.E.; Von Bargen, K.; Mortensen, D.A. Color Indices for Weed Identification under Various Soil, Residue, and Lighting Conditions. Trans. ASAE 1995, 38, 259–269. [Google Scholar] [CrossRef]
- Meyer, G.E.; Hindman, T.W.; Laksmi, K. Machine Vision Detection Parameters for Plant Species Identification. In Precision Agriculture and Biological Quality; International Society for Optics and Photonics: Bellingham, WA, USA, 1999; pp. 327–335. [Google Scholar] [CrossRef]
- Guijarro, M.; Pajares, G.; Riomoros, I.; Herrera, P.J.; Burgos-Artizzu, X.P.; Ribeiro, A. Automatic Segmentation of Relevant Textures in Agricultural Images. Comput. Electron. Agric. 2011, 75, 75–83. [Google Scholar] [CrossRef] [Green Version]
- Guerrero, J.M.; Pajares, G.; Montalvo, M.; Romeo, J.; Guijarro, M. Support Vector Machines for Crop/Weeds Identification in Maize Fields. Expert Syst. Appl. 2012, 39, 11149–11155. [Google Scholar] [CrossRef]
- Yang, W.; Wang, S.; Zhao, X.; Zhang, J.; Feng, J. Greenness Identification Based on HSV Decision Tree. Inf. Proc. Agric. 2015, 2, 149–160. [Google Scholar] [CrossRef] [Green Version]
- Hamuda, E.; Glavin, M.; Jones, E. A Survey of Image Processing Techniques for Plant Extraction and Segmentation in the Field. Comput. Electron. Agric. 2016, 125, 184–199. [Google Scholar] [CrossRef]
- Zhang, C.; Si, Y.; Lamkey, J.; Boydston, R.; Garland-Campbell, K.; Sankaran, S. High-Throughput Phenotyping of Seed/Seedling Evaluation Using Digital Image Analysis. Agronomy 2018, 8, 63. [Google Scholar] [CrossRef] [Green Version]
- Otsu, N. A Threshold Selection Method from Gray-Level Histograms. IEEE Trans. Syst. Man Cybern. 1979, 9, 62–66. [Google Scholar] [CrossRef] [Green Version]
- R Development Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2014. [Google Scholar]
- Rajendran, K.; Tester, M.; Roy, S.J. Quantifying the Three Main Components of Salinity Tolerance in Cereals. Plant Cell Environ. 2009, 32, 237–249. [Google Scholar] [CrossRef]
- Ahmad, S.; Ali, H.; Ur Rehman, A.; Khan, R.; Ahmad, W.; Fatima, Z.; Abbas, G.; Irfan, M.; Ali, H.; Khan, M.; et al. Measuring Leaf Area of Winter Cereals by Different Techniques: A Comparison. Pak. J. Life Soc. Sci. 2015, 13, 117–125. [Google Scholar]
- Kirk, K.; Andersen, H.J.; Thomsen, A.G.; Jørgensen, J.R.; Jørgensen, R.N. Estimation of Leaf Area Index in Cereal Crops Using Red–Green Images. Biosyst. Eng. 2009, 104, 308–317. [Google Scholar] [CrossRef]
- Hosseini, M.; McNairn, H.; Merzouki, A.; Pacheco, A. Estimation of Leaf Area Index (LAI) in Corn and Soybeans Using Multi-Polarization C- and L-Band Radar Data. Remote Sens. Environ. 2015, 170, 77–89. [Google Scholar] [CrossRef]
- Baker, B.; Olszyk, D.M.; Tingey, D. Digital Image Analysis to Estimate Leaf Area. J. Plant Physiol. 1996, 148, 530–535. [Google Scholar] [CrossRef]
- Tackenberg, O. A New Method for Non-Destructive Measurement of Biomass, Growth Rates, Vertical Biomass Distribution and Dry Matter Content Based on Digital Image Analysis. Ann. Bot. 2006, 99, 777–783. [Google Scholar] [CrossRef] [PubMed]
- Hairmansis, A.; Berger, B.; Tester, M.; Roy, S.J. Image-Based Phenotyping for Non-Destructive Screening of Different Salinity Tolerance Traits in Rice. Rice 2014, 7, 10. [Google Scholar] [CrossRef] [Green Version]
- Goggin, F.L.; Lorence, A.; Topp, C.N. Applying High-Throughput Phenotyping to Plant–Insect Interactions: Picturing More Resistant Crops. Curr. Opin. Insect Sci. 2015, 9, 69–76. [Google Scholar] [CrossRef] [Green Version]
Measurements | 21 DAP | 25 DAP | 30 DAP | 35 DAP | 39 DAP | 44 DAP | 49 DAP | 53 DAP |
---|---|---|---|---|---|---|---|---|
Yecora-Rojo | ||||||||
Leaf area (cm2) | 160 ± 17 | 367 ± 55 | 601 ± 34 | 649 ± 28 | 726 ± 49 | 756 ± 31 | 771 ± 31 | 810 ± 30 |
Leaf dry weight (mg) | 720 ± 70 | 1610 ± 280 | 3010 ± 120 | 3630 ± 110 | 4090 ± 240 | 4930 ± 100 | 5280 ± 90 | 5690 ± 90 |
Biomass (mg) | 920 ± 090 | 2290 ± 460 | 5800 ± 460 | 11,620 ± 390 | 16,220 ± 980 | 26,220 ± 740 | 36,070 ± 1540 | 45,390 ± 910 |
Side projected area (mm2) | 12,542 ± 960 | 22,359 ± 2463 | 38,115 ± 2198 | 49,408 ± 1175 | 61,093 ± 2732 | 76,230 ± 2238 | 79,450 ± 1476 | 81,479 ± 2137 |
Seri-82 | ||||||||
Leaf area (cm2) | 118 ± 13 | 290 ± 33 | 572 ± 34 | 884 ± 45 | 1173 ± 85 | 1594 ± 158 | 1887 ± 77 | 1800 ± 105 |
Leaf dry weight (mg) | 550 ± 70 | 1300 ± 150 | 3510 ± 400 | 5000 ± 300 | 7080 ± 350 | 10,000 ± 800 | 11,240 ± 520 | 11,450 ± 650 |
Biomass (mg) | 730 ± 100 | 1800 ± 200 | 5000 ± 400 | 9000 ± 500 | 12,000 ± 190 | 19,000 ± 1000 | 32,980 ± 1030 | 41,600 ± 2310 |
Side projected area (mm2) | 10,882 ± 1006 | 21,795 ± 1752 | 37,825 ± 1526 | 51,398 ± 2765 | 64,438 ± 4029 | 77,526 ± 4491 | 107,159 ± 1534 | 105,419 ± 2887 |
Source | df | Biomass | Leaf Dry Weight | Leaf Area | Side Proj. Area |
---|---|---|---|---|---|
Time point | 7 | <2.2 × 10−16 *** | <2.2 × 10−16 *** | <2.2 × 10−16 | <2.2 × 10−16 |
Genotype | 1 | 8.122 × 10−5 *** | 1.949 × 10−15 *** | 8.159 × 10−16 *** | 0.02625 * |
Genotype × Time point | 7 | 0.1372 | 8.599 × 10−10 *** | 1.179 × 10−12 *** | 0.11584 |
R2 | 0.9357 | 0.9189 | 0.9137 | 0.921 |
Trait | Yecora-Rojo | R2 | r | Seri-82 | R2 | r |
---|---|---|---|---|---|---|
LA | 0.008 SPA + 178.85 | 88.9% | 0.94 | 0.0188 SPA − 81.859 | 98.0% | 0.99 |
LDW | 0.066 SPA + 149.1 | 98.4% | 0.99 | 0.1187 SPA − 782.3 | 97.9% | 0.99 |
BIO | 0.5709 SPA − 11,956 | 85.6% | 0.93 | 0.3982 SPA − 8430.7 | 91.2% | 0.95 |
Growth Parameter | DAP | ||||||
---|---|---|---|---|---|---|---|
21–25 | 25–30 | 30–35 | 35–39 | 39–44 | 44–49 | 49–53 | |
Yecora-Rojo | |||||||
RGRLDW (mgg−1 d−1) | 309 | 173.9 | 41.2 | 31.7 | 41.1 | 14.2 | 19.4 |
RGRBIO (mgg−1 d−1) | 372.3 | 306.6 | 200.7 | 99 | 123.3 | 75.1 | 64.6 |
RGRSPA (mm2 mm−2 d−1) | 0.196 | 0.141 | 0.059 | 0.059 | 0.05 | 0.008 | 0.006 |
Seri-82 | |||||||
RGRLDW (mgg−1 d−1) | 345.5 | 335.9 | 106 | 79.6 | 76 | 29.9 | 4.8 |
RGRBIO (mgg−1 d−1) | 349.3 | 352 | 170.2 | 90.9 | 115.7 | 142.8 | 65.3 |
RGRSPA (mm2 mm−2 d−1) | 0.251 | 0.147 | 0.072 | 0.063 | 0.041 | 0.054 | −0.007 |
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Ajlouni, M.; Kruse, A.; Condori-Apfata, J.A.; Valderrama Valencia, M.; Hoagland, C.; Yang, Y.; Mohammadi, M. Growth Analysis of Wheat Using Machine Vision: Opportunities and Challenges. Sensors 2020, 20, 6501. https://doi.org/10.3390/s20226501
Ajlouni M, Kruse A, Condori-Apfata JA, Valderrama Valencia M, Hoagland C, Yang Y, Mohammadi M. Growth Analysis of Wheat Using Machine Vision: Opportunities and Challenges. Sensors. 2020; 20(22):6501. https://doi.org/10.3390/s20226501
Chicago/Turabian StyleAjlouni, Mohammad, Audrey Kruse, Jorge A. Condori-Apfata, Maria Valderrama Valencia, Chris Hoagland, Yang Yang, and Mohsen Mohammadi. 2020. "Growth Analysis of Wheat Using Machine Vision: Opportunities and Challenges" Sensors 20, no. 22: 6501. https://doi.org/10.3390/s20226501
APA StyleAjlouni, M., Kruse, A., Condori-Apfata, J. A., Valderrama Valencia, M., Hoagland, C., Yang, Y., & Mohammadi, M. (2020). Growth Analysis of Wheat Using Machine Vision: Opportunities and Challenges. Sensors, 20(22), 6501. https://doi.org/10.3390/s20226501