Phenology-aware in-season crop yield estimation through UAV multispectral imagery and deep neural networks

Abstract

Timely and accurate crop yield estimation is important for sustainable agricultural planning and resource optimization. This study is motivated by the need for a scalable, non-destructive, phenology-aware yield estimation pipeline that can outperform spectral index-based methods. A novel in-season crop yield estimation framework is presented that uses high-resolution UAV-based multispectral imagery and deep neural networks. The pipeline integrates automated phenological stage mapping using a custom Spatial Phenology Attention and Feature Cross (SPARC) Network, canopy structure modeling, and wheat head segmentation via a U-Net model fine-tuned on masks generated with SAM 2. UAV imagery is collected across 18 timestamps, processed to produce reflectance maps, vegetation indices (VIs), canopy height models (CHMs), and fractional cover maps. Plot-level phenological and morphological features are extracted to train multiple regression models for in-season yield estimation. Results show that combining temporal phenological features with structural head metrics significantly improve estimation accuracy, with Gradient Boosting Regression achieving an R2 of 0.89. The proposed approach not only improves the granularity and timeliness of in-season yield estimations but also enables scalable, non-destructive crop monitoring solutions, providing practical information for both farmers and breeders alike.

Plant phenotyping relevance

UAV画像と深層学習を用いて、作物のフェノロジー、形態特徴、穂形状を抽出し、圃場区画レベルの収量を推定する技術パイプラインが研究の中心である。

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