Image-Based High-Throughput Detection and Phenotype Evaluation Method for Multiple Lettuce Varieties.

Abstract

The yield and quality of fresh lettuce can be determined from the growth rate and color of individual plants. Manual assessment and phenotyping for hundreds of varieties of lettuce is very time consuming and labor intensive. In this study, we utilized a "Sensor-to-Plant" greenhouse phenotyping platform to periodically capture top-view images of lettuce, and datasets of over 2000 plants from 500 lettuce varieties were thus captured at eight time points during vegetative growth. Here, we present a novel object detection-semantic segmentation-phenotyping method based on convolutional neural networks (CNNs) to conduct non-invasive and high-throughput phenotyping of the growth and development status of multiple lettuce varieties. Multistage CNN models for object detection and semantic segmentation were integrated to bridge the gap between image capture and plant phenotyping. An object detection model was used to detect and identify each pot from the sequence of images with 99.82% accuracy, semantic segmentation model was utilized to segment and identify each lettuce plant with a 97.65% F1 score, and a phenotyping pipeline was utilized to extract a total of 15 static traits (related to geometry and color) of each lettuce plant. Furthermore, the dynamic traits (growth and accumulation rates) were calculated based on the changing curves of static traits at eight growth points. The correlation and descriptive ability of these static and dynamic traits were carefully evaluated for the interpretability of traits related to digital biomass and quality of lettuce, and the observed accumulation rates of static straits more accurately reflected the growth status of lettuce plants. Finally, we validated the application of image-based high-throughput phenotyping through geometric measurement and color grading for a wide range of lettuce varieties. The proposed method can be extended to crops such as maize, wheat, and soybean as a non-invasive means of phenotype evaluation and identification.

Plant phenotyping relevance

CNNによる画像取得・分割からレタスの形態・色・成長動態形質を抽出する高スループット手法を開発・評価しており、フェノタイピング手法が研究の中心である。

Code and data availability

The supplied blocks describe a lettuce high-throughput phenotyping pipeline (YOLOv3 pot detection, U-Net segmentation, trait extraction) but contain no data availability statement, no public dataset/image deposit, and no author code or model release with a URL. The only URLs present are the article DOI and a cited Alex

No evidence-backed public reproduction asset is currently recorded.