Productivity stabilization is a critical issue facing plant factories. As such, researchers have been investigating growth prediction with the overall goal of improving productivity. The projected area of a plant (PA) is usually used for growth prediction, by which the growth of a plant is estimated by observing the overall approximate movement of the plant. To overcome this problem, this study focused on the time-series movement of plant leaves, using optical flow (OF) analysis to acquire this information for a lettuce. OF analysis is an image processing method that extracts the difference between two consecutive frames caused by the movement of the subject. Experiments were carried out at a commercial large-scale plant factory. By using a microcomputer with a camera module placed above the lettuce seedlings, images of 338 seedlings were taken every 20 min over 9 days (from the 6th to the 15th day after sowing). Then, the features of the leaf movement were extracted from the image by calculating the normal-vector in the OF analysis, and these features were applied to machine learning to predict the fresh weight of the lettuce at harvest time (38 days after sowing). The growth prediction model using the features extracted from the OF analysis was found to perform well with a correlation ratio of 0.743. Furthermore, this study also considered a phenotyping system that was capable of automatically analyzing a plant image, which would allow this growth prediction model to be widely used in commercial plant factories.

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.

The circadian clock, an internal oscillator with a period of approximately 24 hours, plays an important role in the regulation of biological processes, and an understanding of circadian rhythms can be employed to improve the quality of plant production. Many studies have measured the circadian rhythms of plants and estimate their circadian times. However, the circadian time estimation methods used in previous studies are difficult to apply to commercial crops because they require extraction of plant contents such as RNA, which involves destroying plant tissues. In this study, we sought to develop a nondestructive method for estimating circadian time in harvested leaves of green perilla (Perilla frutescens var. crispa f. viridis). The results of RNA sequencing (RNA-Seq) show that the gene expression of perillyl alcohol depend on the circadian time. A hyperspectral camera captured the light reflectance of 141 wavebands from 350 to 1,050 nm on leaves, and machine learning using the reflectance data successfully estimated the circadian time corresponding to the harvest time. The study results demonstrate the potential for the nondestructive use of hyperspectral reflectance data in circadian time estimation and its applicability to improving the quality of plant production.