Sorghum-grain-count: A large image dataset and benchmark for sorghum grain count estimation

Abstract

Trait-based breeding has been shown to enhance and sustain yield potential of various crops under current and future changing climate. To be successful, trait-based breeding requires extensive phenotyping of plants in large-scale field trials that may include hundreds of genotypes. Computer vision approaches have been used extensively for image-based high-throughput phenotyping of diverse traits. However, studies focused on estimating grain count, a trait that directly influences the overall yield, are limited due to a lack of benchmark grain image datasets. In this work, we focus on grain count estimation in sorghum, a crop that holds immense significance for both food and energy production. We introduce a large Sorghum-Grain-Count (SGC) dataset consisting of (front and back, with and without flash) images of 1264 panicles from 316 genotypes (i.e., 4 panicles per genotype), for a total of approximately 5000 images, as well as approximately 12,500 images containing the corresponding threshed grains, together with machine counts per panicle. To develop baseline models, we manually annotated grains in images from 100 genotypes using bounding boxes. We used the manually annotated images to train baseline models for small object detection and counting. We also trained regression-based models for grain count estimation. The best overall model for count estimation from panicle images was a regression model, which achieved a mean absolute percent error of 29.97 and an R 2 value of 0.75. We make our dataset and baselines publicly available to facilitate further research on grain count estimation in sorghum and other crops. • We curated the Sorghum-Grain-Count dataset, which includes ∼17,500 panicle and threshed grain images covering 316 genotypes. • This is the largest dataset for grain count estimation and can help advance research on small object count estimation. • We trained strong baseline object detection models, as well as regression-based models for grain count estimation. • Our best model for grain count estimation from panicle images was a regression model that achieved an R 2 value of 0.75. • Our models provide a foundation for non-destructive yield estimation tools, which are greatly needed by breeding programs.

Plant phenotyping relevance

ソルガムの穂・粒画像から粒数という植物収量関連形質を推定する大規模データセット、ベンチマーク、検出・回帰モデルを開発・公開しており、表現型取得・推定手法が研究の中心です。

Code and data availability

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