Monitoring the Growth and Yield of Fruit Vegetables in a Greenhouse Using a Three-Dimensional Scanner.

Abstract

Monitoring the growth of fruit vegetables is essential for the automation of cultivation management, and harvest. The objective of this study is to demonstrate that the current sensor technology can monitor the growth and yield of fruit vegetables such as tomato, cucumber, and paprika. We estimated leaf area, leaf area index (LAI), and plant height using coordinates of polygon vertices from plant and canopy surface models constructed using a three-dimensional (3D) scanner. A significant correlation was observed between the measured and estimated leaf area, LAI, and plant height (R 2 > 0.8, except for tomato LAI). The canopy structure of each fruit vegetable was predicted by integrating the estimated leaf area at each height of the canopy surface models. A linear relationship was observed between the measured total leaf area and the total dry weight of each fruit vegetable; thus, the dry weight of the plant can be predicted using the estimated leaf area. The fruit weights of tomato and paprika were estimated using the fruit solid model constructed by the fruit point cloud data extracted using the RGB value. A significant correlation was observed between the measured and estimated fruit weights (tomato: R 2 = 0.739, paprika: R 2 = 0.888). Therefore, it was possible to estimate the growth parameters (leaf area, plant height, canopy structure, and yield) of different fruit vegetables non-destructively using a 3D scanner.

Plant phenotyping relevance

3Dスキャナーと点群・3Dモデル解析を用いて、葉面積、LAI、草丈、樹冠構造、果実重量を非破壊推定し、実測値との相関で技術検証しているため、植物表現型取得法が中心である。

Code and data availability

The article describes 3D-scanner point cloud data, surface/solid models, and a Python program for estimating leaf area, LAI, plant height, canopy structure, and fruit weight of tomato, cucumber, and paprika. However, no public phenotype dataset, point cloud/image deposit, analysis code repository, or trained model is提及

No evidence-backed public reproduction asset is currently recorded.