High-throughput phenotyping analysis of maize at the seedling stage using end-to-end segmentation network

Abstract

Image processing technologies are available for high-throughput acquisition and analysis of phenotypes for crop populations, which is of great significance for crop growth monitoring, evaluation of seedling condition, and cultivation management. However, existing methods rely on empirical segmentation thresholds, thus can have insufficient accuracy of extracted phenotypes. Taking maize as an example crop, we propose a phenotype extraction approach from top-view images at the seedling stage. An end-to-end segmentation network, named PlantU-net, which uses a small amount of training data, was explored to realize automatic segmentation of top-view images of a maize population at the seedling stage. Morphological and color related phenotypes were automatic extracted, including maize shoot coverage, circumscribed radius, aspect ratio, and plant azimuth plane angle. The results show that the approach can segment the shoots at the seedling stage from top-view images, obtained either from the UAV or ground high-throughput phenotyping platform. The average segmentation accuracy, recall rate, and F1 score are 0.96, 0.98, and 0.97, respectively. The extracted phenotypes, including maize shoot coverage, circumscribed radius, aspect ratio, and plant azimuth plane angle, are highly correlated with manual measurements (R 2 =0.96-0.99). This approach requires less training data and thus has better expansibility. It provides practical means for high-throughput phenotyping analysis of early growth stage crop populations.

Application of Plant Phenotype Extraction Using Virtual Data with Deep Learning

“…𝑚 𝑙=1 (10) where m represents the number of comparison objects, 𝑣 𝑙 denotes the value of manual measurement results, 𝑣 𝑙 ′ denotes the values of the phenotypic parameters extracted from the segmentation results according to the PointNet model, and 𝑣̅ 𝑙 indicates the mean value of the manual measurement results.…”

“…The conventional method of phenotype acquisition is mainly urate-based. Phenotype traits obtained using images can be used for studies such as biomass estimation [6], nutritional diagnosis [7], growth and development monitoring [8], plant structural analysis [9], quantitative description of phenotypic traits [10], and pest and disease identification [11]. In recent years, significant breakthroughs have been achieved in digital image processing using deep learning; the performance of deep learning-based image processing has been much better than traditional methods in applications such as object recognition and segmentation [12][13][14].…”

High-throughput Phenotyping for Abiotic Stress Resilience in Cereals

“…Successful application of HTP platforms for use in drought, salinity and heat/ cold stress tolerance in plants, particularly cereal crops need capacity infrastructure for data management and analysis. (Shakoor et al, 2017;Li et al, 2021). The application of HTP for abiotic stress resilience in various cereal crops has been summarized (Table 1).…”