Hyperspectral reflectance and machine learning for multi-site monitoring of cotton growth

Flynn, K. Colton; Witt, Travis W.; Baath, Gurjinder S.; Chinmayi, H. K.; Smith, Douglas R.; Gowda, Prasanna H.; Ashworth, Amanda J.

doi:10.1016/j.atech.2024.100536

Title: Hyperspectral reflectance and machine learning for multi-site monitoring of cotton growth

Journal Article · 30 November 2024 · Smart Agricultural Technology

DOI: https://doi.org/10.1016/j.atech.2024.100536 · OSTI ID:2439317

; Witt, Travis W.; Baath, Gurjinder S.; Chinmayi, H. K.; Smith, Douglas R.; Gowda, Prasanna H.; Ashworth, Amanda J.

Not Available

View Journal Article

Cite

Export

Save

Sponsoring Organization:: USDOE

OSTI ID:: 2439317

Journal Information:: Smart Agricultural Technology, Journal Name: Smart Agricultural Technology Journal Issue: C Vol. 9; ISSN 2772-3755

Publisher:: ElsevierCopyright Statement

Country of Publication:: Netherlands

Language:: English

References (39)

Enhancing Upland cotton for drought resilience, productivity, and fiber quality: comparative evaluation and genetic dissection Ulloa, Mauricio; De Santiago, Luis M.; Hulse-Kemp, Amanda M. Molecular Genetics and Genomics, Vol. 295, 155–176 https://doi.org/10.1007/s00438-019-01611-6	journal	October 2019
Exploring ethyl methanesulfonate (EMS) treated cotton (Gossypium hirsutum L.) to improve drought tolerance Witt, Travis W.; Ulloa, Mauricio; Pelletier, Mathew G. Euphytica, Vol. 214 https://doi.org/10.1007/s10681-018-2206-3	journal	June 2018
Hyperspectral remote sensing in precision agriculture: present status, challenges, and future trends Singh, Prachi; Pandey, Prem Chandra; Petropoulos, George P. Hyperspectral Remote Sensing https://doi.org/10.1016/B978-0-08-102894-0.00009-7	book	January 2020
Sorghum and sunflower evapotranspiration and yield from simulated leaf area index Mailhol, Jean Claude; Olufayo, Ayorinde A.; Ruelle, Pierre Agricultural Water Management, Vol. 35, Issue 1-2 https://doi.org/10.1016/S0378-3774(97)00029-2	journal	December 1997
Chlorophyll meter to predict nitrogen sidedress requirements for short-season cotton (Gossypium hirsutum L.) Feibo, Wu; Lianghuan, Wu; Fuhua, Xu Field Crops Research, Vol. 56, Issue 3 https://doi.org/10.1016/S0378-4290(97)00108-1	journal	April 1998
Morphology-based spatial filtering for efficiency enhancement of remote sensing image fusion Pandit, Vaibhav R.; Bhiwani, R. J. Computers & Electrical Engineering, Vol. 89 https://doi.org/10.1016/j.compeleceng.2020.106945	journal	January 2021
Precision agriculture on grassland: Applications, perspectives and constraints Schellberg, Jürgen; Hill, Michael J.; Gerhards, Roland European Journal of Agronomy, Vol. 29, Issue 2-3 https://doi.org/10.1016/j.eja.2008.05.005	journal	August 2008
The yield potential of cotton (Gossypium hirsutum L.) Constable, G. A.; Bange, M. P. Field Crops Research, Vol. 182 https://doi.org/10.1016/j.fcr.2015.07.017	journal	October 2015
Response to deficit irrigation of morphological, yield and fiber quality traits of upland (Gossypium hirsutum L.) and Pima (G. barbadense L.) cotton in the Texas High Plains Witt, Travis W.; Ulloa, Mauricio; Schwartz, Robert C. Field Crops Research, Vol. 249 https://doi.org/10.1016/j.fcr.2020.107759	journal	February 2020
Information fusion techniques for change detection from multi-temporal remote sensing images Du, Peijun; Liu, Sicong; Xia, Junshi Information Fusion, Vol. 14, Issue 1 https://doi.org/10.1016/j.inffus.2012.05.003	journal	January 2013
Effect of Water Logging Stress on Cotton Leaf Area Index and Yield Guang, Cao; Xiugui, Wang; Yu, Liu Procedia Engineering, Vol. 28 https://doi.org/10.1016/j.proeng.2012.01.706	journal	January 2012
Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield Dente, L.; Satalino, G.; Mattia, F. Remote Sensing of Environment, Vol. 112, Issue 4 https://doi.org/10.1016/j.rse.2007.05.023	journal	April 2008
Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies Kokaly, Raymond F.; Asner, Gregory P.; Ollinger, Scott V. Remote Sensing of Environment, Vol. 113 https://doi.org/10.1016/j.rse.2008.10.018	journal	September 2009
Remotely estimating photosynthetic capacity, and its response to temperature, in vegetation canopies using imaging spectroscopy Serbin, Shawn P.; Singh, Aditya; Desai, Ankur R. Remote Sensing of Environment, Vol. 167 https://doi.org/10.1016/j.rse.2015.05.024	journal	September 2015
Retrieving structural and chemical properties of individual tree crowns in a highly diverse tropical forest with 3D radiative transfer modeling and imaging spectroscopy Ferreira, Matheus Pinheiro; Féret, Jean-Baptiste; Grau, Eloi Remote Sensing of Environment, Vol. 211 https://doi.org/10.1016/j.rse.2018.04.023	journal	June 2018
Hyperspectral imagery to monitor crop nutrient status within and across growing seasons Liu, Nanfeng; Townsend, Philip A.; Naber, Mack R. Remote Sensing of Environment, Vol. 255 https://doi.org/10.1016/j.rse.2021.112303	journal	March 2021
Random Forests Breiman, Leo Machine Learning, Vol. 45, Issue 1, p. 5-32 https://doi.org/10.1023/A:1010933404324	journal	January 2001
Knowledge-based analysis of microarray gene expression data by using support vector machines Brown, M. P. S.; Grundy, W. N.; Lin, D. Proceedings of the National Academy of Sciences, Vol. 97, Issue 1 https://doi.org/10.1073/pnas.97.1.262	journal	January 2000
Two new hyperspectral indices for comparing vegetation chlorophyll content Frazier, Amy E.; Wang, Le; Chen, Jin Geo-spatial Information Science, Vol. 17, Issue 1 https://doi.org/10.1080/10095020.2014.889264	journal	January 2014
Big Data and Machine Learning With Hyperspectral Information in Agriculture Ang, Kenneth Li-Minn; Seng, Jasmine Kah Phooi IEEE Access, Vol. 9 https://doi.org/10.1109/ACCESS.2021.3051196	journal	January 2021
Foliar functional traits from imaging spectroscopy across biomes in eastern North America Wang, Zhihui; Chlus, Adam; Geygan, Ryan New Phytologist, Vol. 228, Issue 2 https://doi.org/10.1111/nph.16711	journal	June 2020
Dictionary Learning for Noisy and Incomplete Hyperspectral Images Xing, Zhengming; Zhou, Mingyuan; Castrodad, Alexey SIAM Journal on Imaging Sciences, Vol. 5, Issue 1, p. 33-56 https://doi.org/10.1137/110837486	journal	January 2012
Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties Singh, Aditya; Serbin, Shawn P.; McNeil, Brenden E. Ecological Applications, Vol. 25, Issue 8 https://doi.org/10.1890/14-2098.1	journal	December 2015
Canopeo: A Powerful New Tool for Measuring Fractional Green Canopy Cover Patrignani, Andres; Ochsner, Tyson E. Agronomy Journal, Vol. 107, Issue 6 https://doi.org/10.2134/agronj15.0150	journal	November 2015
Modeling Water‐Stressed Cotton Growth Using Within‐Season Remote Sensing Data Ko, Jonghan; Maas, Stephan J.; Mauget, Steve Agronomy Journal, Vol. 98, Issue 6 https://doi.org/10.2134/agronj2005.0284	journal	November 2006
Large‐Area Maize Yield Forecasting Using Leaf Area Index Based Yield Model Baez‐Gonzalez, Alma Delia; Kiniry, James R.; Maas, Stephan J. Agronomy Journal, Vol. 97, Issue 2 https://doi.org/10.2134/agronj2005.0418	journal	March 2005
Physiological Consequences of Moisture Deficit Stress in Cotton Pettigrew, W. T. Crop Science, Vol. 44, Issue 4 https://doi.org/10.2135/cropsci2004.1265	journal	July 2004
High‐Throughput Phenotyping of Cotton in Multiple Irrigation Environments Sharma, Bablu; Ritchie, Glen L. Crop Science, Vol. 55, Issue 2 https://doi.org/10.2135/cropsci2014.04.0310	journal	March 2015
In-Season Nitrogen Status Sensing in Irrigated Cotton Bronson, Kevin F.; Chua, Teresita T.; Booker, J. D. Soil Science Society of America Journal, Vol. 67, Issue 5, 1439-1448 https://doi.org/10.2136/sssaj2003.1439	journal	September 2003
Monitoring indigenous microalgae using derivative spectrophotometry and comparison with M. aeruginosa and C. vulgaris Singh, Jaitegh; Örmeci, Banu Journal of Water Supply: Research and Technology-Aqua, Vol. 71, Issue 3 https://doi.org/10.2166/aqua.2022.128	journal	February 2022
Spectral Light Curve Simulation for Parameter Estimation from Space Debris Nussbaum, Max; Schafer, Ewan; Yoon, Zizung Aerospace, Vol. 9, Issue 8 https://doi.org/10.3390/aerospace9080403	journal	July 2022
Feasibility of Near-Infrared Spectroscopy for Rapid Detection of Available Nitrogen in Vermiculite Substrates in Desert Facility Agriculture Zhao, Pengfei; Xing, Jianfei; Hu, Can Agriculture, Vol. 12, Issue 3 https://doi.org/10.3390/agriculture12030411	journal	March 2022
Fingerprint Approaches Coupled with Chemometrics to Discriminate Geographic Origin of Imported Salmon in China's Consumer Market Fu, Xianshu; Hong, Xuezhen; Liao, Jinyan Foods, Vol. 10, Issue 12 https://doi.org/10.3390/foods10122986	journal	December 2021
Comparison of Methods for Modeling Fractional Cover Using Simulated Satellite Hyperspectral Imager Spectra Dennison, Philip E.; Qi, Yi; Meerdink, Susan K. Remote Sensing, Vol. 11, Issue 18 https://doi.org/10.3390/rs11182072	journal	September 2019
Nutrient Prediction for Tef (Eragrostis tef) Plant and Grain with Hyperspectral Data and Partial Least Squares Regression: Replicating Methods and Results across Environments Flynn, K. Colton; Frazier, Amy E.; Admas, Sintayehu Remote Sensing, Vol. 12, Issue 18 https://doi.org/10.3390/rs12182867	journal	September 2020
Machine Learning Optimised Hyperspectral Remote Sensing Retrieves Cotton Nitrogen Status Marang, Ian J.; Filippi, Patrick; Weaver, Tim B. Remote Sensing, Vol. 13, Issue 8 https://doi.org/10.3390/rs13081428	journal	April 2021
Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration Yao, Xia; Huang, Yu; Shang, Guiyan Remote Sensing, Vol. 7, Issue 11 https://doi.org/10.3390/rs71114939	journal	November 2015
Grasspea (Lathyrus sativus L.) as a Green N Source Reduces the Effects of 2,4 D in Susceptible Cotton (Gossypium hirsutum L.) Cultivars Witt, Travis W.; Northup, Brian K.; Neel, James P. S. American Journal of Plant Sciences, Vol. 12, Issue 07 https://doi.org/10.4236/ajps.2021.127070	journal	January 2021
A Hyperspectral Remote Sensing Fusion Technology Based on Spectral Normalization of gf and zy Series Satellites Liu, S.; Li, H.; Wang, X. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XLIII-B3-2022 https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-509-2022	journal	May 2022

Similar Records

Hyperspectral Leaf Reflectance as Proxy for Photosynthetic Capacities: An Ensemble Approach Based on Multiple Machine Learning Algorithms

Journal Article · 2019 · Frontiers in Plant Science · OSTI ID:1524099

Fu, Peng; Meacham-Hensold, Katherine; Guan, Kaiyu; +1 more

Characterizing transition-metal dichalcogenide thin-films using hyperspectral imaging and machine learning

Journal Article · 2020 · Scientific Reports · OSTI ID:1638397

Shevitski, Brian; Chen, Christopher T.; Kastl, Christoph; +4 more

Hyperspectral Reflectance-Based High Throughput Phenotyping to Assess Water-Use Efficiency in Cotton

Journal Article · 2024 · Agriculture · OSTI ID:2582947

Beegum, Sahila; Hassan, Muhammad; Ramamoorthy, Purushothaman; +4 more

Title: Hyperspectral reflectance and machine learning for multi-site monitoring of cotton growth

Citation Formats

References (39)

Similar Records

Related Subjects