article · Plants
Leaf chlorophyll content serves as an essential indicator of photosynthetic capacity, growth stage, and nitrogen health in precision farming. Evaluating lettuce cultivated in aquaponic systems across varied nutrient gradients, automated machine learning models were developed using hyperspectral reflectance and standard colour imagery. An open-source automated machine learning framework was benchmarked against traditional approaches, including random forest, support vector machines, back-propagation neural networks, and partial least squares regression. Combining sensitive spectral vegetation indices with colour indices yielded the strongest predictive accuracy, achieving prediction coefficients of determination between 0.89 and 0.98. Ground-truth chlorophyll readings demonstrated a strong correlation with calibrated handheld sensor measurements. The results confirm that non-destructive optical data, processed through automated machine learning, reliably tracks plant chlorophyll levels and provides a robust mechanism to monitor crop health.
Monitoring crop nutrients non-destructively helps indoor and aquaponic farms maintain healthy growth without damaging plants or relying on slow laboratory testing. By coupling standard digital camera images and spectral measurements with automated machine learning, growers can rapidly track plant nitrogen and photosynthetic health. This approach simplifies high-throughput plant assessment and supports more efficient resource management in controlled agricultural environments.
This research demonstrates applied and tested predictive models capable of integration into embedded devices for aquaponic and indoor vertical farming operations. System operators and precision agriculture technology developers could use these models alongside standard RGB cameras and spectral sensors to automate nutrient cycle control. While the analytical framework is validated on experimental datasets, deploying it commercially requires packaging the software into robust on-farm monitoring hardware.
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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.
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DOI: 10.3390/plants13030392
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