High-Throughput Analysis of Leaf Chlorophyll Content in Aquaponically Grown Lettuce Using Hyperspectral Reflectance and RGB Images

Abstract

Chlorophyll content reflects plants’ photosynthetic capacity, growth stage, and nitrogen status and is, therefore, of significant importance in precision agriculture. This study aims to develop a spectral and color vegetation indices-based model to estimate the chlorophyll content in aquaponically grown lettuce. A completely open-source automated machine learning (AutoML) framework (EvalML) was employed to develop the prediction models. The performance of AutoML along with four other standard machine learning models (back-propagation neural network (BPNN), partial least squares regression (PLSR), random forest (RF), and support vector machine (SVM) was compared. The most sensitive spectral (SVIs) and color vegetation indices (CVIs) for chlorophyll content were extracted and evaluated as reliable estimators of chlorophyll content. Using an ASD FieldSpec 4 Hi-Res spectroradiometer and a portable red, green, and blue (RGB) camera, 3600 hyperspectral reflectance measurements and 800 RGB images were acquired from lettuce grown across a gradient of nutrient levels. Ground measurements of leaf chlorophyll were acquired using an SPAD-502 m calibrated via laboratory chemical analyses. The results revealed a strong relationship between chlorophyll content and SPAD-502 readings, with an R2 of 0.95 and a correlation coefficient (r) of 0.975. The developed AutoML models outperformed all traditional models, yielding the highest values of the coefficient of determination in prediction (Rp2) for all vegetation indices (VIs). The combination of SVIs and CVIs achieved the best prediction accuracy with the highest Rp2 values ranging from 0.89 to 0.98, respectively. This study demonstrated the feasibility of spectral and color vegetation indices as estimators of chlorophyll content. Furthermore, the developed AutoML models can be integrated into embedded devices to control nutrient cycles in aquaponics systems.

Non-Destructive Prediction of Anthocyanin Content of Rosa chinensis Petals Using Digital Images and Machine Learning Algorithms

“…Wood et al [47] observed that the RGB indices enabled the estimation of the a, b, and total chlorophyll concentrations of microalgal cultures in situ. Taha et al [48] demonstrated the feasibility of using RGB indices to estimate the chlorophyll content of lettuce. In this study, the correlations between 28 RGB indices derived from digital camera images and the anthocyanin content were strong, indicating that these indices were suitable for establishing predictive models of the anthocyanin content.…”

Performance of Neural Networks in the Prediction of Nitrogen Nutrition in Strawberry Plants

“…Variations in green hues due to foliar nitrogen (N) content can result in class separation in an RGB image bank when it comes to computer vision. Thus, this pigment becomes essential in the context of precision agriculture because it serves as a basis for quantifying foliar N content [4][5][6]. A practical and quick way to determine the existence of nutrient deficiency in plants is through visual diagnosis.…”