Optimizing plant health monitoring: improved accuracy and the computational efficiency with stacked machine learning models and feature filtering - Sangeetha, Tupili; Ezhumalai, Periyathambi (Bulletin of Electrical Engineering and Informatics, 2025)
Plant cultivation can be effectively achieved with the help of hydroponic farming that allows growing soilless and organic plant veggies. However, maintaining optimal plant health in such controlled environments requires continuous monitoring and assessment techniques. This paper provides a comprehensive description of how to determine and categorize the health of hydroponic plants based on a wide range of parameters, such as temperature, pH, electrical conductivity (EC), leaf count, plant height, and vegetative indices. We present a novel approach termed “Hybrid XGBoosting” that combines the multi-classification algorithm extreme gradient boosting (XGBoost) with gradient-based one-side sampling (GOSS) methods to improve accuracy and processing efficiency. This approach first adopts a feature correlation method known as “Pearson’s correlation” for reducing repeated data that are directly proportional or inversely proportional to each other. Finally, we perform a thorough comparative study using well-known algorithms including traditional XGBoost, AdaBoost, and gradient boosting. We demonstrate the better prediction capabilities of Hybrid XGBoosting with 97.93% accuracy through rigorous testing and evaluation, showing its potential for improving hydroponic plant health assessment approaches. Additionally, our research employs comprehensive algorithm assessment measures, such as root mean squared scaled error (RMSSEE), to guarantee the stability and reliability of the results.