Leaf-Movement-Based Growth Prediction Model Using Optical Flow Analysis and Machine Learning in Plant Factory

Abstract

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.

Plant phenotyping relevance

光学フローによる葉の動きの画像特徴抽出と機械学習によるレタス生体重予測が中心で、自動フェノタイピングシステムも検討しているため。

Code and data availability

The article describes an MPI imaging system, optical flow/normal-vector analysis, and GBR/SVR growth prediction for lettuce, but provides no public deposit of the image dataset, extracted features, trained models, or analysis code. The only link is the generic Frontiers supplementary-material page, with no statement it

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