Integrating 3D detection networks and dynamic temporal phenotyping for wheat yield classification and prediction

Abstract

Automated phenotyping of wheat growth stages from 3D point clouds is still limited. The study presents a concise framework that reconstructs multi-view UAS imagery into 3D point clouds (jointing to maturity) and performs plot-level phenotyping. A novel 3D wheat plot detection network—integrating spatial–channel coordinated attention and area attention modules—improves depth-direction feature recognition, and a point-cloud-density-based row segmentation algorithm enables planting-row-scale plot delineation. A supporting software system facilitates 3D visualization and automated extraction of phenotypic parameters. We introduce a dynamic phenotypic index of five temporal metrics (growth stage, slow growth stage, height/area reduction stage, maximum height/area difference stage, and height/area change rate) for growth-stage classification and yield prediction using static and time-series models. Experiments show strong agreement between predicted and measured plot heights (R 2 = 0.937); the detection net achieved AP 3D = 94.15 % and AP BEV = 95.35 % in “easy” mode; and a Bi-LSTM incorporating dynamic traits reached 82.37 % prediction accuracy for leaf area and yield, a 6.14 % improvement over static-trait models. This workflow supports high-throughput 3D phenotyping and reliable yield estimation for precision agriculture. • Developed a novel 3D wheat plot detection net with spatial–channel coordinated attention and area-attention modules, reaching 94.15% AP 3D and 95.35% AP BEV in high-precision mode, outperforming traditional methods. The CFPT 3D module boosts depth-direction feature extraction for dense planting. • Introduced 5 temporal phenotypic metrics (e.g., growth stage transitions, height/area change rates) to capture dynamic growth patterns. • Bi-LSTM models using these traits predicted yield with 82.37% accuracy, 6.14% higher than static-trait models. • Released a PyQt5-based 3D phenotype extraction tool for automated parameter calculation (height, canopy area, LAI) and visualization. • Proposed a density-based row segmentation algorithm enabling accurate row-level phenotyping, validated in single- and multi-row systems.

Plant phenotyping relevance

3D画像・点群から小麦区画の形態形質を抽出する手法、検出・行分割アルゴリズム、動的形質指標、ソフトウェアを中心的に開発・検証しているため。

Code and data availability

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