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

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...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 21; no. 3; p. 958
Main Authors: Weksler, Shahar, Rozenstein, Offer, Haish, Nadav, Moshelion, Menachem, Wallach, Rony, Ben-Dor, Eyal
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 01.02.2021
MDPI
Subjects:
Agricultural production
Algorithms
Cameras
Crops, Agricultural
functional phenotyping
Greenhouses
Humidity
Hyperspectral Imaging
hyperspectral remote sensing
Irrigation
Laboratories
phenomics
Physiology
Potassium
Potassium Deficiency
Salinity
Soil
Spectrum analysis
Support vector machines
transpiration rate
Vegetation
Water
XGboost
Israel
XGboost
functional phenotyping
hyperspectral remote sensing
potassium
reflectance
transpiration rate
phenomics
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s21030958