Multi-Trait Phenotypic Extraction and Fresh Weight Estimation of Greenhouse Lettuce Based on Inspection Robot

Abstract

In situ detection of growth information in greenhouse crops is crucial for germplasm resource optimization and intelligent greenhouse management. To address the limitations of poor flexibility and low automation in traditional phenotyping platforms, this study developed a controlled environment inspection robot. By means of a SCARA robotic arm equipped with an information acquisition device consisting of an RGB camera, a depth camera, and an infrared thermal imager, high-throughput and in situ acquisition of lettuce phenotypic information can be achieved. Through semantic segmentation and point cloud reconstruction, 12 phenotypic parameters, such as lettuce plant height and crown width, were extracted from the acquired images as inputs for three machine learning models to predict fresh weight. By analyzing the training results, a Backpropagation Neural Network (BPNN) with an added feature dimension-increasing module (DE-BP) was proposed, achieving improved prediction accuracy. The R2 values for plant height, crown width, and fresh weight predictions were 0.85, 0.93, and 0.84, respectively, with RMSE values of 7 mm, 6 mm, and 8 g, respectively. This study achieved in situ, high-throughput acquisition of lettuce phenotypic information under controlled environmental conditions, providing a lightweight solution for crop phenotypic information analysis algorithms tailored for inspection tasks.