Predicting the greenhouse crop morphological parameters based on RGB-D Computer Vision

• Developed a new approach to predict the leafy plant traits using RGB-D features. • Vegetation indices used in arable farming were applied as features for prediction. • Analysed the computer vision features against crop morphological parameters. • Derived features had a stronger correlation with plant traits for the young plant. • Improved the accuracy of prediction by reducing confounding factors. Accurate data acquisition of crop morphological parameters is crucial for effective greenhouse management decision-making and remote sensing technologies are increasingly being applied to automate the data collection process. This research utilised an RGB-D based computer vision method to investigate the correlation between the computer vision features and the lettuce morphological parameters, including leaf area, plant height, diameter, and fresh weight. A dataset of lettuce containing over 300 RGB images and depth images of the 3rd Autonomous Greenhouse Challenge was used, and Random Forest, XGBoost and linear regression models were applied in the prediction. The best NRMSE values for diameter, dry matter content, dry weight, fresh weight, height, and leaf area are 0.08, 0.08, 0.07, 0.07, 0.08, and 0.07, which showed a promising accuracy compared to similar studies. This research demonstrates a novel approach to non-destructively estimate greenhouse leafy vegetable morphological parameters.