Mohamed Farag Taha, Hanping Mao, Yafei Wang, Ahmed Islam ElManawy, Gamal Elmasry, Letian Wu, Muhammad Sohail Memon, Ziang Niu, Ting Huang, Zhengjun Qiu
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...
January 29, 2024: Plants (Basel, Switzerland)