Yang Baohua, Chen Runchao, Gao Zhiwei, Zhi Hongbo
School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, China.
Front Plant Sci. 2024 Oct 10;15:1435042. doi: 10.3389/fpls.2024.1435042. eCollection 2024.
Wheat is one of the important food crops in the world, and the stability and growth of wheat production have a decisive impact on global food security and economic prosperity. Wheat counting is of great significance for agricultural management, yield prediction and resource allocation. Research shows that the wheat ear counting method based on deep learning has achieved remarkable results and the model accuracy is high. However, the complex background of wheat fields, dense wheat ears, small wheat ear targets, and different sizes of wheat ears make the accurate positioning and counting of wheat ears still face great challenges. To this end, an automatic positioning and counting method of wheat ears based on FIDMT-GhostNet (focal inverse distance transform maps - GhostNet) is proposed. Firstly, a lightweight wheat ear counting network using GhostNet as the feature extraction network is proposed, aiming to obtain multi-scale wheat ear features. Secondly, in view of the difficulty in counting caused by dense wheat ears, the point annotation-based network FIDMT (focal inverse distance transform maps) is introduced as a baseline network to improve counting accuracy. Furthermore, to address the problem of less feature information caused by the small ear of wheat target, a dense upsampling convolution module is introduced to improve the resolution of the image and extract more detailed information. Finally, to overcome background noise or wheat ear interference, a local maximum value detection strategy is designed to realize automatic processing of wheat ear counting. To verify the effectiveness of the FIDMT-GhostNet model, the constructed wheat image data sets including WEC, WEDD and GWHD were used for training and testing. Experimental results show that the accuracy of the wheat ear counting model reaches 0.9145, and the model parameters reach 8.42M, indicating that the model FIDMT-GhostNet proposed in this study has good performance.
小麦是世界上重要的粮食作物之一，小麦产量的稳定与增长对全球粮食安全和经济繁荣具有决定性影响。麦穗计数对于农业管理、产量预测和资源分配具有重要意义。研究表明，基于深度学习的麦穗计数方法取得了显著成果，模型准确率较高。然而，麦田背景复杂、麦穗密集、麦穗目标小以及麦穗大小不一等因素，使得麦穗的精确定位和计数仍面临巨大挑战。为此，提出了一种基于FIDMT-GhostNet（焦点逆距离变换图 - GhostNet）的麦穗自动定位与计数方法。首先，提出了一种以GhostNet作为特征提取网络的轻量级麦穗计数网络，旨在获取多尺度的麦穗特征。其次，鉴于密集麦穗导致的计数困难，引入基于点标注的网络FIDMT（焦点逆距离变换图）作为基线网络以提高计数准确率。此外，为解决小麦穗目标小导致的特征信息少的问题，引入密集上采样卷积模块以提高图像分辨率并提取更详细的信息。最后，为克服背景噪声或麦穗干扰，设计了局部最大值检测策略以实现麦穗计数的自动处理。为验证FIDMT-GhostNet模型的有效性，使用构建的包括WEC、WEDD和GWHD的小麦图像数据集进行训练和测试。实验结果表明，麦穗计数模型的准确率达到0.9145，模型参数达到8.42M，表明本研究提出的模型FIDMT-GhostNet具有良好的性能。
