MaizeEar-SAM: Zero-Shot Maize Ear Phenotyping

Quantifying the variation in yield component traits of maize (Zea mays L.), which together determine the overall productivity of this globally important crop, plays a critical role in plant genetics research, plant breeding, and the development of improved farming practices. Grain yield per acre is calculated by multiplying the number of plants per acre, ears per plant, number of kernels per ear, and the average kernel weight. The number of kernels per ear is determined by the number of kernel rows per ear multiplied by the number of kernels per row. Traditional manual methods for measuring these two traits are time-consuming, limiting large-scale data collection. Recent automation efforts using image processing and deep learning encounter challenges such as high annotation costs and uncertain generalizability. We tackle these issues by exploring Large Vision Models for zero-shot, annotation-free maize kernel segmentation. By using an open-source large vision model, the Segment Anything Model (SAM), we segment individual kernels in RGB images of maize ears and apply a graph-based algorithm to calculate the number of kernels per row. Our approach successfully identifies the number of kernels per row across a wide range of maize ears, showing the potential of zero-shot learning with foundation vision models combined with image processing techniques to improve automation and reduce subjectivity in agronomic data collection. All our code is open-sourced to make these affordable phenotyping methods accessible to everyone.

Disentangling Genotype and Environment Specific Latent Features for Improved Trait Prediction using a Compositional Autoencoder

This study introduces a compositional autoencoder (CAE) framework designed to disentangle the complex interplay between genotypic and environmental factors in high-dimensional phenotype data to improve trait prediction in plant breeding and genetics programs. Traditional predictive methods, which use compact representations of high-dimensional data through handcrafted features or latent features like PCA or more recently autoencoders, do not separate genotype-specific and environment-specific factors. We hypothesize that disentangling these features into genotype-specific and environment-specific components can enhance predictive models. To test this, we developed a compositional autoencoder (CAE) that decomposes high-dimensional data into distinct genotype-specific and environment-specific latent features. Our CAE framework employs a hierarchical architecture within an autoencoder to effectively separate these entangled latent features. Applied to a maize diversity panel dataset, the CAE demonstrates superior modeling of environmental influences and 5-10 times improved predictive performance for key traits like Days to Pollen and Yield, compared to the traditional methods, including standard autoencoders, PCA with regression, and Partial Least Squares Regression (PLSR). By disentangling latent features, the CAE provides powerful tool for precision breeding and genetic research. This work significantly enhances trait prediction models, advancing agricultural and biological sciences.