Li Yunlong, Yang Baohua, Zhou Shuaijun, Cui Qiang
School of Information and Computer, Anhui Agricultural University, Hefei, China.
Front Plant Sci. 2022 Sep 27;13:968479. doi: 10.3389/fpls.2022.968479. eCollection 2022.
Wheat is one of the important food crops, and it is often subjected to different stresses during its growth. Lodging is a common disaster in filling and maturity for wheat, which not only affects the quality of wheat grains, but also causes severe yield reduction. Assessing the degree of wheat lodging is of great significance for yield estimation, wheat harvesting and agricultural insurance claims. In particular, point cloud data extracted from unmanned aerial vehicle (UAV) images have provided technical support for accurately assessing the degree of wheat lodging. However, it is difficult to process point cloud data due to the cluttered distribution, which limits the wide application of point cloud data. Therefore, a classification method of wheat lodging degree based on dimensionality reduction images from point cloud data was proposed. Firstly, 2D images were obtained from the 3D point cloud data of the UAV images of wheat field, which were generated by dimensionality reduction based on Hotelling transform and point cloud interpolation method. Then three convolutional neural network (CNN) models were used to realize the classification of different lodging degrees of wheat, including AlexNet, VGG16, and MobileNetV2. Finally, the self-built wheat lodging dataset was used to evaluate the classification model, aiming to improve the universality and scalability of the lodging discrimination method. The results showed that based on MobileNetV2, the dimensionality reduction image from point cloud obtained by the method proposed in this paper has achieved good results in identifying the lodging degree of wheat. The F1-Score of the classification model was 96.7% for filling, and 94.6% for maturity. In conclusion, the point cloud dimensionality reduction method proposed in this study could meet the accurate identification of wheat lodging degree at the field scale.
小麦是重要的粮食作物之一，在其生长过程中常遭受不同胁迫。倒伏是小麦灌浆期和成熟期常见的灾害，不仅影响小麦籽粒品质，还会导致严重减产。评估小麦倒伏程度对产量估算、小麦收割及农业保险理赔具有重要意义。特别是，从无人机（UAV）图像中提取的点云数据为准确评估小麦倒伏程度提供了技术支持。然而，由于点云数据分布杂乱，难以进行处理，这限制了点云数据的广泛应用。因此，提出了一种基于点云数据降维图像的小麦倒伏程度分类方法。首先，通过基于霍特林变换和点云插值方法的降维，从麦田无人机图像的三维点云数据中获取二维图像。然后，使用三种卷积神经网络（CNN）模型实现小麦不同倒伏程度的分类，包括AlexNet、VGG16和MobileNetV2。最后，利用自建的小麦倒伏数据集对分类模型进行评估，旨在提高倒伏判别方法的通用性和可扩展性。结果表明，基于MobileNetV2，本文提出的方法所获得的点云降维图像在识别小麦倒伏程度方面取得了良好效果。分类模型在灌浆期的F1分数为96.7%，在成熟期为94.6%。总之，本研究提出的点云降维方法能够满足田间尺度下小麦倒伏程度的准确识别。
