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Open AccessArticle

Detection of Potassium Deficiency and Momentary Transpiration Rate Estimation at Early Growth Stages Using Proximal Hyperspectral Imaging and Extreme Gradient Boosting

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Porter School of Environment and Earth Sciences, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
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Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, Rishon LeZion 7528809, Israel
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The Robert H Smith Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, Rehovot 7610001, Israel
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Department of Soil and Water Sciences, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot 76100, Israel
*
Author to whom correspondence should be addressed.
Academic Editor: Biswajeet Pradhan
Sensors 2021, 21(3), 958; https://doi.org/10.3390/s21030958
Received: 2 December 2020 / Revised: 21 January 2021 / Accepted: 27 January 2021 / Published: 1 February 2021
(This article belongs to the Special Issue Recent Advances in Multi- and Hyperspectral Image Analysis)
Potassium is a macro element in plants that is typically supplied to crops in excess throughout the season to avoid a deficit leading to reduced crop yield. Transpiration rate is a momentary physiological attribute that is indicative of soil water content, the plant’s water requirements, and abiotic stress factors. In this study, two systems were combined to create a hyperspectral–physiological plant database for classification of potassium treatments (low, medium, and high) and estimation of momentary transpiration rate from hyperspectral images. PlantArray 3.0 was used to control fertigation, log ambient conditions, and calculate transpiration rates. In addition, a semi-automated platform carrying a hyperspectral camera was triggered every hour to capture images of a large array of pepper plants. The combined attributes and spectral information on an hourly basis were used to classify plants into their given potassium treatments (average accuracy = 80%) and to estimate transpiration rate (RMSE = 0.025 g/min, R2 = 0.75) using the advanced ensemble learning algorithm XGBoost (extreme gradient boosting algorithm). Although potassium has no direct spectral absorption features, the classification results demonstrated the ability to label plants according to potassium treatments based on a remotely measured hyperspectral signal. The ability to estimate transpiration rates for different potassium applications using spectral information can aid in irrigation management and crop yield optimization. These combined results are important for decision-making during the growing season, and particularly at the early stages when potassium levels can still be corrected to prevent yield loss. View Full-Text
Keywords: hyperspectral remote sensing; phenomics; functional phenotyping; transpiration rate; potassium; XGboost; reflectance hyperspectral remote sensing; phenomics; functional phenotyping; transpiration rate; potassium; XGboost; reflectance
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MDPI and ACS Style

Weksler, S.; Rozenstein, O.; Haish, N.; Moshelion, M.; Wallach, R.; Ben-Dor, E. Detection of Potassium Deficiency and Momentary Transpiration Rate Estimation at Early Growth Stages Using Proximal Hyperspectral Imaging and Extreme Gradient Boosting. Sensors 2021, 21, 958. https://doi.org/10.3390/s21030958

AMA Style

Weksler S, Rozenstein O, Haish N, Moshelion M, Wallach R, Ben-Dor E. Detection of Potassium Deficiency and Momentary Transpiration Rate Estimation at Early Growth Stages Using Proximal Hyperspectral Imaging and Extreme Gradient Boosting. Sensors. 2021; 21(3):958. https://doi.org/10.3390/s21030958

Chicago/Turabian Style

Weksler, Shahar; Rozenstein, Offer; Haish, Nadav; Moshelion, Menachem; Wallach, Rony; Ben-Dor, Eyal. 2021. "Detection of Potassium Deficiency and Momentary Transpiration Rate Estimation at Early Growth Stages Using Proximal Hyperspectral Imaging and Extreme Gradient Boosting" Sensors 21, no. 3: 958. https://doi.org/10.3390/s21030958

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