Optimized Excess-Green Image Binarization for Accurate Estimation of Lettuce Seedling Leaf-Area in a Plant Factory

Abstract

Real-time, continuous young seedling-growth measurement improves plant factory stabilization and productivity. Projected leaf area (PLA) based on seedling top-view images is a useful growth index, and easy to measure continuously for large seedling populations. However, it is difficult to automatically determine PLA with a high degree of accuracy, because RGB image color-balance fluctuates with plant growth, leaf movement, and environment. Therefore, we developed a technique for determining PLA on nursery-grown lettuce seedlings. Using a Raspberry Pi 3 microcomputer with a camera module placed above the seedlings, RGB images of 153 seedlings were obtained every 20 min from day 6 to 15 after sowing. Seedling PLA images were obtained by binarization and separation of the leaves from the background. To assess binarization accuracy, we used an Intersection over Union (IoU) index to compare the standard Excess Green (ExG) method, an optimized ExG method (O-ExG), and the artificial neural network U-Net method. Results showed that O-ExG was optimal under the experimental conditions tested. PLA and circadian rhythm amplitude extracted from PLA-image time-series data were independent, implying they can be used together for growth prediction. These findings improve the accuracy of imagebased growth prediction and have practical application in plant factories.

Code and data availability

The article describes lettuce seedling PLA image capture (17,280 Raspberry Pi images), O-ExG/U-Net binarization analysis, and circadian rhythm extraction, but contains no data availability statement, no public dataset or image deposit, and no author code/model release with a public URL. The only URL mentioned is the un

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