Development of Lettuce Growth Monitoring Model Based on Three-Dimensional Reconstruction Technology

Abstract

Crop monitoring can promptly reflect the growth status of crops. However, conventional methods of growth monitoring, although simple and direct, have limitations such as destructive sampling, reliance on human experience, and slow detection speed. This study estimated the fresh weight of lettuce (Lactuca sativa L.) in a plant factory with artificial light based on three-dimensional (3D) reconstruction technology. Data from different growth stages of lettuce were collected as the training dataset, while data from different plant forms of lettuce were used as the validation dataset. The partial least squares regression (PLSR) method was utilized for modeling, and K-fold cross-validation was performed to evaluate the model. The testing dataset of this model achieved a coefficient of determination (R2) of 0.9693, with root mean square error (RMSE) and mean absolute error (MAE) values of 3.3599 and 2.5232, respectively. Based on the performance of the validation set, an adaptation was made to develop a fresh weight estimation model for lettuce under far-red light conditions. To simplify the estimation model, reduce estimation costs, enhance estimation efficiency, and improve the lettuce growth monitoring method in plant factories, the plant height and canopy width data of lettuce were extracted to estimate the fresh weight of lettuce in addition. The testing dataset of the new model achieved an R2 value of 0.8970, with RMSE and MAE values of 3.1206 and 2.4576.

Plant phenotyping relevance

3D再構成からレタスの生体重・草丈・キャノピー幅を推定するモデルの開発と検証が研究の中心であり、植物表現型取得・推定手法に該当する。

Code and data availability

The paper describes lettuce 3D reconstruction and fresh weight estimation (PLSR in MATLAB, Agisoft Metashape), but the supplied blocks contain no public dataset, image, or code deposit; no availability statement or authors' URL for assets appears anywhere in the text.

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