Accurate rice grain counting in natural morphology: A method based on image classification and object detection

Abstract

Accurately counting the number of grains per panicle is crucial for evaluating rice yield and selecting superior germplasm resources. Traditional measurement methods are labor-intensive, time-consuming, and prone to errors. To address this challenge, computer vision-based methods have emerged as a promising approach for seed counting. However, achieving precise grain counting is particularly challenging due to their natural morphology, which involves occlusion and substantial variations in size, shape and orientation. This often requires additional steps, such as manual shaping or threshing. Therefore, we propose an innovative approach for precisely counting rice grains in their natural form by integrating object detection, image classification and regression equations. Initially, we trained the Yolov7-tiny model for grain counting. Subsequently, we introduced a classification system based on the variability in the natural morphology of rice panicles using the EfficientNetV2 network, enabling the classification of rice panicles into five distinct classes. Furthermore, we devised a set of univariate linear regression equations for the different classes of rice panicles, utilizing data from 2920 diverse rice germplasm to establish correlations predicted and actual values. Experimental findings demonstrated a counting accuracy of 92.60% with an average absolute percentage error of 7.69%. And, this study revealed that utilizing two-sided images of panicles did not significantly improve counting accuracy. This study represents a successful endeavor in achieving precise and efficient counting of rice panicles within their natural morphology, offering a novel solution for detecting and counting dense objects.

Plant phenotyping relevance

イネ穂の粒数という植物形質を、画像分類・物体検出・回帰により非破壊推定する手法を開発し、精度評価も行っており、フェノタイピング手法が中心である。

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