数字农科院2.0

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

文献类型： 外文期刊

作者： Ziqiu Kang;Bo Zhou;Shulang Fei;Nan Wang

作者机构：

关键词： Image processing;Machine learning;Plant morphology;Remote sensing;Urban agriculture

期刊名称： Smart Agricultural Technology

ISSN： 2772-3755

年卷期： 2025 年 11 卷

页码：

收录情况： ESCI（2025版）

摘要： 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.

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