Shao Hongmin, Tang Rong, Lei Yujie, Mu Jiong, Guan Yan, Xiang Ying
College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China.
Sichuan Key Laboratory of Agricultural Information Engineering, Ya'an 625000, China.
Plants (Basel). 2021 Aug 6;10(8):1625. doi: 10.3390/plants10081625.
The real-time detection and counting of rice ears in fields is one of the most important methods for estimating rice yield. The traditional manual counting method has many disadvantages: it is time-consuming, inefficient and subjective. Therefore, the use of computer vision technology can improve the accuracy and efficiency of rice ear counting in the field. The contributions of this article are as follows. (1) This paper establishes a dataset containing 3300 rice ear samples, which represent various complex situations, including variable light and complex backgrounds, overlapping rice and overlapping leaves. The collected images were manually labeled, and a data enhancement method was used to increase the sample size. (2) This paper proposes a method that combines the LC-FCN (localization-based counting fully convolutional neural network) model based on transfer learning with the watershed algorithm for the recognition of dense rice images. The results show that the model is superior to traditional machine learning methods and the single-shot multibox detector (SSD) algorithm for target detection. Moreover, it is currently considered an advanced and innovative rice ear counting model. The mean absolute error (MAE) of the model on the 300-size test set is 2.99. The model can be used to calculate the number of rice ears in the field. In addition, it can provide reliable basic data for rice yield estimation and a rice dataset for research.
田间稻穗的实时检测与计数是估算水稻产量的最重要方法之一。传统的人工计数方法存在诸多缺点：耗时、效率低且主观。因此，利用计算机视觉技术可以提高田间稻穗计数的准确性和效率。本文的贡献如下。（1）本文建立了一个包含3300个稻穗样本的数据集，这些样本代表了各种复杂情况，包括光照变化、复杂背景、稻穗重叠和叶片重叠。对采集到的图像进行了人工标注，并采用数据增强方法增加样本数量。（2）本文提出了一种将基于迁移学习的LC-FCN（基于定位的计数全卷积神经网络）模型与分水岭算法相结合的方法，用于识别密集的水稻图像。结果表明，该模型优于传统机器学习方法和用于目标检测的单阶段多框检测器（SSD）算法。此外，它目前被认为是一种先进且创新的稻穗计数模型。该模型在300大小测试集上的平均绝对误差（MAE）为2.99。该模型可用于计算田间稻穗数量。此外，它可以为水稻产量估算提供可靠的基础数据，并为研究提供一个水稻数据集。
