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

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 R2 ≥ 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.

Assessing Contents of Sugars, Vitamins, and Nutrients in Baby Leaf Lettuce from Hyperspectral Data with Machine Learning Models

“…Correlations for N, P, and K content determined in the current study using PLSR and PCR models are, however, in close agreement with the results from other laboratories [21]. The accuracy of the PLSR and PCR models were higher for N and P, but lower for K in our study than previously reported accuracies for the PLSR models [40].…”

“…phase (r = 0.9808 … 0.9998), testing (r = 0.7500 … 0.9357), and validation (r = 0.8480 … 0.9400) (Figure 7). The accuracy of the estimation models for N, P, and K contents is in good agreement with the accuracy of the models developed using back propagation neural networks and random forest algorithms [40]. The lowest correlation between the estimated and ground truth values was found for Vit-C, Glu, Fru, 55C DM, 105C DM, and the square root of SPAD, which also showed much higher NRMSE values (Figure 7).…”

Assessing Contents of Sugars, Vitamins, and Nutrients in Baby Leaf Lettuce from Hyperspectral Data with Machine Learning Models

“…al., 2023). The accuracy of the PLSR and PCR models were higher for N and P, but lower for K in our study than previously reported accuracies for the PLSR models (Taha, et. al., 2022).…”

“…When the original data and the ANN model were applied, the highest correlations between the estimated nutrient values and laboratory-measured values were detected for fresh leaf weight (FLW), log2(ACI), N, P, K, SPAD, and β-carotene in the model training phase (r = 0.9808 … 0.9998), testing (r = 0.7500…0.9357), and validation (r = 0.8480…0.9400) (Figure 7). The accuracy of the estimation models for N, P, K contents are in good agreement with the accuracy of the models developed using back propagation neural network and random forest algorithms (Taha, et. al., 2022).…”

Design and Development of Large-Band Dual-MSFA Sensor Camera for Precision Agriculture

“…Previous research has shown that, for agricultural applications, most of the necessary information is obtained in the visible (VIS) and near-infrared (NIR) parts of the spectrum [ 4 , 5 , 6 , 7 , 8 , 9 ]. In this regard, much research has been conducted on the use of VIS and NIR spectral data, such as the development of spectral indices [ 10 , 11 , 12 ], the detection of plant diseases [ 13 , 14 , 15 , 16 ], weed detection [ 7 , 17 , 18 ], nutrient content estimation [ 19 , 20 ], monitoring water stress [ 21 ], phenotyping [ 22 , 23 ], and the quality measurement of agricultural products [ 24 , 25 ]. Thomas et al [ 26 ], in their study on the benefits of hyperspectral imaging for disease detection, pointed out that, in addition to the visible part of spectral data (i.e., 400–700 nm), the NIR wavelengths (i.e., 700–1000 nm) significantly represent the pathogens influencing the cellular structure of plants.…”