A neural network approach employed to classify soybean plants using multi-sensor images

Abstract

Counting soybean plants is a crucial strategy for assessing sowing quality and supporting high production. Despite its importance, the laborious nature of traditional assessment methods makes them unreliable and not scalable. Additionally, innovative image-based solutions have demonstrated limitations in detecting dense crops such as soybeans. Therefore, in this study, we developed neural network models to analyze a set of RGB and multispectral images and perform plant classification in a comprehensive dataset, which included data collected at three vegetative stages of soybean (VC, V1, and V2). Our results demonstrated high accuracy in classifying plants using either RGB (98%) or multispectral images (92%). A significant strength of this study is the ability to classify highly dense plants, without a trend for misclassification. Clearly, our findings provide stakeholders with a timely and effective approach to counting soybean plants, reducing labor and time, while increasing reliability.

Plant phenotyping relevance

RGB・マルチスペクトル画像とニューラルネットワークによるダイズ個体数の推定手法を開発・評価しており、植物形質取得が研究の中心である。

Code and data availability

The paper describes UAV RGB/multispectral soybean imagery and MLP classification in Orange, but contains no data availability statement, no public dataset or code repository, and no author-provided URL for images, ground-truth counts, models, or workflows. All referenced URLs are citations of prior work, not paper-own-

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