Study on Utilizing Mask R-CNN for Phenotypic Estimation of Lettuce’s Growth Status and Optimal Harvest Timing

Abstract

Lettuce is an annual plant of the family Asteraceae. It is most often grown as a leaf vegetable, but sometimes for its stem and seeds, and its growth status and quality are evaluated based on its morphological phenotypic traits. However, traditional measurement methods are often labor-intensive and time-consuming due to manual measurements and may result in less accuracy. In this study, we proposed a new method utilizing RGB images and Mask R-Convolutional Neural Network (CNN) for estimating lettuce critical phenotypic traits. Leveraging publicly available datasets, we employed an improved Mask R-CNN model to perform a phenotypic analysis of lettuce images. This allowed us to estimate five phenotypic traits simultaneously, which include fresh weight, dry weight, plant height, canopy diameter, and leaf area. The enhanced Mask R-CNN model involved two key aspects: (1) replacing the backbone network from ResNet to RepVGG to enhance computational efficiency and performance; (2) adding phenotypic branches and constructing a multi-task regression model to achieve end-to-end estimation of lettuce phenotypic traits. Experimental results demonstrated that the present method achieved high accuracy and stable results in lettuce image segmentation, detection, and phenotypic estimation tasks, with APs for detection and segmentation being 0.8684 and 0.8803, respectively. Additionally, the R2 values for the five phenotypic traits are 0.96, 0.9596, 0.9329, 0.9136, and 0.9592, with corresponding mean absolute percentage errors (MAPEs) of 0.1072, 0.1522, 0.0757, 0.0548, and 0.0899, respectively. This study presents a novel technical advancement based on digital knowledge for phenotypic analysis and evaluation of lettuce quality, which could lay the foundation for artificial intelligence expiation in fresh vegetable production.