Optical Method for Estimating the Chlorophyll Contents in Plant Leaves

Abstract

This work introduces a new vision-based approach for estimating chlorophyll contents in a plant leaf using reflectance and transmittance as base parameters. Images of the top and underside of the leaf are captured. To estimate the base parameters (reflectance/transmittance), a novel optical arrangement is proposed. The chlorophyll content is then estimated by using linear regression where the inputs are the reflectance and transmittance of the leaf. Performance of the proposed method for chlorophyll content estimation was compared with a spectrophotometer and a Soil Plant Analysis Development (SPAD) meter. Chlorophyll content estimation was realized for Lactuca sativa L., Azadirachta indica, Canavalia ensiforme, and Lycopersicon esculentum. Experimental results showed that—in terms of accuracy and processing speed—the proposed algorithm outperformed many of the previous vision-based approach methods that have used SPAD as a reference device. On the other hand, the accuracy reached is 91% for crops such as Azadirachta indica, where the chlorophyll value was obtained using the spectrophotometer. Additionally, it was possible to achieve an estimation of the chlorophyll content in the leaf every 200 ms with a low-cost camera and a simple optical arrangement. This non-destructive method increased accuracy in the chlorophyll content estimation by using an optical arrangement that yielded both the reflectance and transmittance information, while the required hardware is cheap.

Plant phenotyping relevance

植物葉のクロロフィル含量という形質を画像・光学計測で推定する手法を開発し、分光光度計およびSPADメーターと比較検証しており、表現型取得手法が中心である。

Code and data availability

The article describes a vision-based chlorophyll estimation method with leaf image acquisition and regression analysis, but contains no data availability statement, no public dataset or image repository, and no author code release. The only URL present is the CC BY license link, which is not a paper-specific asset.

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