Abstract
Growth-related traits, such as aboveground biomass and leaf area, are critical indicators to characterize the growth of greenhouse lettuce. Currently, nondestructive methods for estimating growth-related traits are subject to limitations in that the methods are susceptible to noise and heavily rely on manually designed features. In this study, a method for monitoring the growth of greenhouse lettuce was proposed by using digital images and a convolutional neural network (CNN). Taking lettuce images as the input, a CNN model was trained to learn the relationship between images and the corresponding growth-related traits, i.e., leaf fresh weight (LFW), leaf dry weight (LDW), and leaf area (LA). To compare the results of the CNN model, widely adopted methods were also used. The results showed that the values estimated by CNN had good agreement with the actual measurements, with R2 values of 0.8938, 0.8910, and 0.9156 and normalized root mean square error (NRMSE) values of 26.00, 22.07, and 19.94%, outperforming the compared methods for all three growth-related traits. The obtained results showed that the CNN demonstrated superior estimation performance for the flat-type cultivars of Flandria and Tiberius compared with the curled-type cultivar of Locarno. Generalization tests were conducted by using images of Tiberius from another growing season. The results showed that the CNN was still capable of achieving accurate estimation of the growth-related traits, with R2 values of 0.9277, 0.9126, and 0.9251 and NRMSE values of 22.96, 37.29, and 27.60%. The results indicated that a CNN with digital images is a robust tool for the monitoring of the growth of greenhouse lettuce.
Highlights
Growth monitoring is essential for optimizing management and maximizing the production of greenhouse lettuce
The results showed strong correlations between the actual measurements of the growth-related traits and those estimated by the convolutional neural network (CNN) model
The CNN model was better at estimating the growth-related traits of Flandria and Tiberius than Locarno, which might be due to the differences in the leaf shape of the lettuce
Summary
Growth monitoring is essential for optimizing management and maximizing the production of greenhouse lettuce. Monitoring the growth of greenhouse lettuce by accurately obtaining growth-related traits (LFW, LDW, and LA) is of great practical significance for improving the yield and quality of lettuce[3]. The traditional methods for measuring growth-related traits, which are relatively straightforward, can achieve relatively accurate results[4]. The image-based approaches extract low-level features from digital images and establish the relationship between the low-level features and manually measured growth-related traits, such as LA, LFW, and LDW. Based on this relationship, the image-derived features can estimate the growth-related traits, achieving nondestructive growth monitoring. Based on the above features, they built multiple models, i.e., support vector regression (SVR), random forest (RF), multivariate linear regression (MLR), and multivariate adaptive regression splines, to estimate barley biomass
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