Robust soybean seed yield estimation using high-throughput ground robot videos.

Abstract

We present a novel method for soybean [ Glycine max (L.) Merr.] yield estimation leveraging high-throughput seed counting via computer vision and deep learning techniques. Traditional methods for collecting yield data are labor-intensive, costly, and prone to equipment failures at critical data collection times and require transportation of equipment across field sites. Computer vision, the field of teaching computers to interpret visual data, allows us to extract detailed yield information directly from images. By treating it as a computer vision task, we report a more efficient alternative, employing a ground robot equipped with fisheye cameras to capture comprehensive videos of soybean plots from which images are extracted in a variety of development programs. These images are processed through the P2PNet-Yield model, a deep learning framework, where we combined a feature extraction module (the backbone of the P2PNet-Soy) and a yield regression module to estimate seed yields of soybean plots. Our results are built on 2 years of yield testing plot data-8,500 plots in 2021 and 650 plots in 2023. With these datasets, our approach incorporates several innovations to further improve the accuracy and generalizability of the seed counting and yield estimation architecture, such as the fisheye image correction and data augmentation with random sensor effects. The P2PNet-Yield model achieved a genotype ranking accuracy score of up to 83%. It demonstrates up to a 32% reduction in time to collect yield data as well as costs associated with traditional yield estimation, offering a scalable solution for breeding programs and agricultural productivity enhancement.

Plant phenotyping relevance

ロボット動画とコンピュータビジョン／深層学習によりダイズの種子収量を推定する手法を開発・評価しており、表現型取得・推定が研究の中心である。

Code and data availability

The supplied blocks describe robot-collected soybean video/image datasets and the P2PNet-Yield model, but contain no data availability statement, repository link, or deposit language for the paper's own datasets, images, code, or trained models. The supplementary material link is generic and not stated to contain pheny

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