Lettuce Fresh Weight Prediction in a Plant Factory Using Plant Growth Models

Abstract

This paper proposes a novel method to predict the fresh weight of lettuce at the shipping stage in a plant factory using the early-stage growth images. It is well-established that the size and shape of plants correlate with their fresh weight. The proposed method captures chlorophyll fluorescence-based growth images daily and extracts geometric features such as projection area, edge length, and skeleton length. We design a regression model to predict the fresh weight using the dimensionality-reduced historical features. However, without considering growth statuses, the dimensionality reduction approach leads to decreased predictive performance for mature and slower-growing plants. In this paper, we generate a plant growth model that simulates the growth process by integrating multiple growth records based on the comparison of growth statuses. The proposed method then reduces the dimensionality by fitting historical features to the plant growth model to obtain future features. Experimental results demonstrate that the proposed method accurately predicts the fresh weight and achieves the coefficient of determination of 0.885, root mean square error of 8.790 g, and mean absolute error of 6.684 g when predicting the fresh weight ten days ahead using growth images from the past ten days.

Keywords

• Plant factory
• growth prediction
• machine learning
• curve fitting
• regression model