Using Machine Learning for Nutrient Content Detection of Aquaponics-Grown Plants Based on Spectral Data

Mohamed Farag Taha, Ahmed Islam ElManawy, Khalid S. Alshallash, Gamal ElMasry, Khadiga Alharbi, Lei Zhou, Ning Liang and Zhengjun Qiu ()
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Mohamed Farag Taha: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Ahmed Islam ElManawy: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Khalid S. Alshallash: College of Science and Humanities-Huraymila, Imam Mohammed Bin Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
Gamal ElMasry: Agricultural Engineering Department, Faculty of Agriculture, Suez Canal University, Ismailia 41522, Egypt
Khadiga Alharbi: Department of Biology, College of science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Lei Zhou: College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
Ning Liang: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Zhengjun Qiu: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China

Sustainability, 2022, vol. 14, issue 19, 1-17

Abstract: Nutrients derived from fish feed are insufficient for optimal plant growth in aquaponics; therefore, they need to be supplemented. Thus, estimating the amount of supplementation needed can be achieved by looking at the nutrient contents of the plant. This study aims to develop trustworthy machine learning models to estimate the nitrogen (N), phosphorus (P), and potassium (K) contents of aquaponically grown lettuce. A FieldSpec4, Pro FR portable spectroradiometer (ASD Inc., Analytical Spectral Devices Boulder, Boulder, CO, USA) was used to measure leaf reflectance spectra, and 128 lettuce seedlings given four NPK treatments were used for spectra acquisition and total NPK estimation. Principal component analysis (PCA), genetic algorithms (GA), and sequential forward selection (SFS) were applied to select the optimal wavebands. Partial least squares regression (PLSR), back-propagation neural network (BPNN), and random forest (RF) approaches were used to develop the predictive models of NPK contents using the selected optimal wavelengths. Good and significantly correlated predictive accuracy was obtained in comparison with the laboratory-measured freshly cut lettuce leaves with R 2 ≥ 0.94. The proposed approach provides a pathway toward automatic nutrient estimation of aquaponically grown lettuce. Consequently, aquaponics will become more intelligent, and will be adopted as a precision agriculture technology.

Keywords: aquaponics; machine learning; lettuce; optimal wavelengths; spectral data; regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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