Estimation of rice seedling growth traits with an end-to-end multi-objective deep learning framework - Ye, Ziran; Tan, Xiangfeng; Dai, Mengdi; Lin, Yue; Chen, Xuting; Nie, Pengcheng; Ruan, Yunjie; Kong, Dedong (Frontiers in Plant Science, 2023)
Growth-related traits such as height and biomass are important indicators for quantifying the growth of rice seedlings. Nowadays, the development of image-based plant phenotyping has received increasing attention, however, there is still room for improvement in plant phenotyping methods to meet the demand for rapid, robust and low-cost extraction of phenotypic measurements from images in environmentally-controlled plant factories. In this study, a method based on convolutional neural networks (CNNs) and digital images was proposed to monitor the growth of rice seedlings in a controlled environment. Specifically, an end-to-end framework consisting of hybrid CNNs took color images, scaling factor and image acquisition distance as input and directly predicted the shoot height (SH) and shoot fresh weight (SFW) after image segmentation. The results on the rice seedlings dataset collected by different optical sensors demonstrated that the proposed method outperformed compared random forest (RF) and regression CNN models (RCNN), with R2 values of 0.980 and 0.717, and normalized root mean square error (NRMSE) values of 2.64% and 17.23%. The proposed method can learn the relationship between digital images and seedling growth traits, which is promising to provide a convenient and flexible estimation tool for the non-destructive monitoring of seedling growth in controlled environments.