Plant Density Estimation Using UAV Imagery and Deep Learning

Abstract

Plant density is a significant variable in crop growth. Plant density estimation by combining unmanned aerial vehicles (UAVs) and deep learning algorithms is a well-established procedure. However, flight companies for wheat density estimation are typically executed at early development stages. Further exploration is required to estimate the wheat plant density after the tillering stage, which is crucial to the following growth stages. This study proposed a plant density estimation model, DeNet, for highly accurate wheat plant density estimation after tillering. The validation results presented that (1) the DeNet with global-scale attention is superior in plant density estimation, outperforming the typical deep learning models of SegNet and U-Net; (2) the sigma value at 16 is optimal to generate heatmaps for the plant density estimation model; (3) the normalized inverse distance weighted technique is robust to assembling heatmaps. The model test on field-sampled datasets revealed that the model was feasible to estimate the plant density in the field, wherein a higher density level or lower zenith angle would degrade the model performance. This study demonstrates the potential of deep learning algorithms to capture plant density from high-resolution UAV imageries for wheat plants including tillers.

Plant phenotyping relevance

UAV画像と深層学習を用いてコムギの植物密度を推定するモデルを開発・検証しており、表現型取得手法が研究の中心である。

Code and data availability

The supplied blocks describe UAV imagery, field-sampled wheat density datasets, and the DeNet model, but contain no public deposit, availability statement, or authors' URL for the imagery, annotations, trained model, or code. The only URLs present (CUDA toolkit, Keras) are generic tool references, not paper-specific.

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