Analyzing maize stem circumference, stem height, and stem circumference-to-height ratio using UAV, UGV, and deep learning

Abstract

Maize (Zea mays L.) is a crucial grain and economic crop with extensive applications in food, feed, and industry. Phenotypic traits such as stem circumference (SC), stem height (SH), and the stem circumference-to-height ratio (SCHR) are essential indicators for studying maize development, environmental adaptation, and lodging resistance. Traditional manual measurement methods are inefficient, costly, and unsuitable for large-scale phenotypic monitoring. While unmanned aerial vehicle (UAV)-based approaches have achieved relatively accurate SH estimation, SC estimation remains challenging using UAV technology alone, limiting SCHR estimation. This study combined digital camera sensors on unmanned ground vehicle (UGV) and UAV platforms to capture maize stem and canopy images, enabling the estimation of SC, SH, and SCHR. The primary contributions of this study are as follows: (1) We propose the maize-stem segmentation network for calculating stem diameter and circumference (MSSDCNet) to segment maize stems in images and estimate SC based on the segmentation results. (2) We process UAV-derived digital surface models to extract SH information and employ a linear regression (LR) model for SH estimation. (3) Using the estimated SC and SH, we calculate SCHR and analyze its temporal variations across different growth stages. The results demonstrate that: (1) MSSDCNet accurately segments maize stems from images and facilitates SC estimation (R² = 0.759, RMSE = 0.414 cm, nRMSE = 0.087). Temporal analysis of SC reveals a gradual decrease during the reproductive growth stage, potentially due to the transfer of photosynthetic products to the maize cob and stem water loss. (2) This study accurately estimated SH (R² = 0.941, RMSE = 0.151 m, nRMSE = 0.078). However, SH estimates during the reproductive growth stage tend to be underestimated, likely due to DSM point clouds being more sensitive to sharp features such as tassels. (3) SCHR estimation achieves R² = 0.453, RMSE = 2.287 × 10⁻³, nRMSE = 0.136. Temporal analysis reveals a general decline in SCHR from the kernel blister stage to the dough stage, with some maize materials consistently exhibiting lower SCHR levels during each growth stage. This may be related to genetic traits, planting density, soil fertility, or other environmental factors. By integrating UGV-based maize stem images and UAV-based maize canopy images with MSSDCNet and LR, this study successfully estimates SC, SH, and SCHR for various maize materials. This study provides a novel technique for maize, early lodging risk prediction, and lodging-resistant breeding lines screening, contributing to rapid and efficient maize phenotypic monitoring under field conditions.

Plant phenotyping relevance

UAV・UGV画像、DSM、深層学習によってトウモロコシの茎周囲長・草丈・比率を推定する手法を開発・評価しており、植物表現型取得が研究の中心です。

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