Classification of Forecasting of Water Streets in Tomato Plants Using Bioristor Data

In this study, we propose a novel approach for the classification and forecasting of water stress in tomato plants utilizing Bioristor data. Bioristor, a cutting-edge sensor technology, provides real-time insights into plant physiological responses. We explore the application of four machine learning algorithms to enhance the accuracy of water stress prediction Random Forests  demonstrate their proficiency in discerning subtle patterns in Bioristor data, while Random Forests exhibit robustness in handling complex relationships. Additionally, Gradient Boosting techniques showcase their ability to improve model performance through iterative refinement. Furthermore, Random Forests are employed to extract spatial features from the sensor data, offering a deep learning perspective on water stress classification. Our results indicate promising advancements in the amalgamation of Bioristor technology and machine learning algorithms for the accurate classification and forecasting of water stress in tomato plants, paving the way for precision agriculture practices.

Keywords: Water stress classification, Bioristor data, tomato plants, machine learning, Random Forests, Gradient Boosting, precision agriculture, plant physiological responses, sensor technology, spatial features.