Kozhekin Mikhail V, Genaev Mikhail A, Komyshev Evgenii G, Zavyalov Zakhar A, Afonnikov Dmitry A
Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia.
Kurchatov Center for Genome Research, Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia.
J Imaging. 2025 Jan 20;11(1):28. doi: 10.3390/jimaging11010028.
Crop field monitoring using unmanned aerial vehicles (UAVs) is one of the most important technologies for plant growth control in modern precision agriculture. One of the important and widely used tasks in field monitoring is plant stand counting. The accurate identification of plants in field images provides estimates of plant number per unit area, detects missing seedlings, and predicts crop yield. Current methods are based on the detection of plants in images obtained from UAVs by means of computer vision algorithms and deep learning neural networks. These approaches depend on image spatial resolution and the quality of plant markup. The performance of automatic plant detection may affect the efficiency of downstream analysis of a field cropping pattern. In the present work, a method is presented for detecting the plants of five species in images acquired via a UAV on the basis of image segmentation by deep learning algorithms (convolutional neural networks). Twelve orthomosaics were collected and marked at several sites in Russia to train and test the neural network algorithms. Additionally, 17 existing datasets of various spatial resolutions and markup quality levels from the Roboflow service were used to extend training image sets. Finally, we compared several texture features between manually evaluated and neural-network-estimated plant masks. It was demonstrated that adding images to the training sample (even those of lower resolution and markup quality) improves plant stand counting significantly. The work indicates how the accuracy of plant detection in field images may affect their cropping pattern evaluation by means of texture characteristics. For some of the characteristics (GLCM mean, GLRM long run, GLRM run ratio) the estimates between images marked manually and automatically are close. For others, the differences are large and may lead to erroneous conclusions about the properties of field cropping patterns. Nonetheless, overall, plant detection algorithms with a higher accuracy show better agreement with the estimates of texture parameters obtained from manually marked images.
使用无人机（UAV）进行农田监测是现代精准农业中植物生长控制的最重要技术之一。田间监测中一项重要且广泛应用的任务是植株计数。准确识别田间图像中的植物可提供单位面积内植物数量的估计值，检测出缺苗情况，并预测作物产量。当前的方法是基于通过计算机视觉算法和深度学习神经网络从无人机获取的图像中检测植物。这些方法依赖于图像空间分辨率和植物标记的质量。自动植物检测的性能可能会影响田间种植模式下游分析的效率。在本研究中，提出了一种基于深度学习算法（卷积神经网络）的图像分割方法，用于检测通过无人机获取的图像中的五种植物。在俄罗斯的几个地点收集了12幅正射镶嵌影像并进行了标记，以训练和测试神经网络算法。此外，还使用了来自Roboflow服务的17个具有不同空间分辨率和标记质量水平的现有数据集来扩展训练图像集。最后，我们比较了人工评估和神经网络估计的植物掩膜之间的几种纹理特征。结果表明，向训练样本中添加图像（即使是分辨率和标记质量较低的图像）可显著提高植株计数的准确性。这项工作表明了田间图像中植物检测的准确性如何通过纹理特征影响其种植模式评估。对于某些特征（灰度共生矩阵均值、灰度行程长度矩阵长行程、灰度行程长度矩阵行程比），人工标记和自动标记图像之间的估计值接近。对于其他特征，差异较大，可能会导致关于田间种植模式属性的错误结论。尽管如此，总体而言，具有较高准确性的植物检测算法与从人工标记图像中获得的纹理参数估计值显示出更好的一致性。
