WSG-P2PNet: A deep learning framework for counting and locating wheat spike grains in the open field environment

Abstract

The number of spike grains is an important parameter for wheat yield estimation. However, it is challenging to automatically and intelligently count wheat spike grains in the open field environment. In this study, a deep learning framework, called Wheat Spike Grain Point-to-Point Network (WSG-P2PNet), is proposed to count and locate the wheat spike grains in the open field environment. This framework incorporates Efficient Channel Attention (ECA) and Coordinate Attention (CA) after feature extraction and feature concatenation, respectively. These mechanisms effectively highlight the channel features and positional information of the wheat spike grains while suppressing background interference from factors such as stems, leaves and wheat ears. Additionally, standard convolutions in the regression and classification branches are replaced with Spatial and Channel reconstruction Convolutions (SCConv), further enhancing representational capabilities and improving model performance. The results demonstrate that WSG-P2PNet, using VGG19_bn as the backbone network, outperforms five other state-of-the-art methods in terms of accuracy and stability, with an MAE of 1.72 (95% CI 1.67, 1.77), an Acc of 94.93% (95% CI 94.92, 94.93), an RMSE of 2.35 (95% CI 2.26, 2.44), and an R² of 0.8311 (95% CI 0.8218, 0.8404). Ablation experiments illustrate the impact of SCConv, ECA, and CA on the performance of WSG-P2PNet. Notably, WSG-P2PNet still maintains high accuracy in different varieties and growth periods, demonstrating its robustness and generalizability in real-world scenarios. Preliminary experiments also evaluated the correlation between predicted spike grain numbers and wheat yield, with an average Pearson Correlation Coefficient r of 0.7944, indicating a strong positive statistical relationship. The proposed deep learning framework enables rapid and accurate counting and localization of wheat spike grains in the open field environment, which is of great significant for integrated wheat yield estimation.

Plant phenotyping relevance

コムギ穂粒数という植物形態・収量関連形質を、圃場画像から深層学習で計数・位置推定する手法を開発し、比較・アブレーション・異品種および生育期で検証しているため、方法が中心である。

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