基于改进K-means聚类算法的大田麦穗自动计数

单位种植面积的小麦麦穗数量是评估小麦产量和小麦种植密度的一个重要参量。为了实现高效、自动地麦穗计数,该文提出了基于改进K-means的小麦麦穗计数方法。该方法建立从图像低层颜色特征到图像中包含麦穗的一个直接分类关系,从而不需要再对图像进行分割或检测。以颜色特征聚类为基础的这种方法能够估计麦穗在空间局部区域中数量,并且在不需要训练的情况下更具有可扩展性。统计试验结果表明,该文算法能够适应不同光照环境,麦穗计数的准确率达到94.69%,超过了传统基于图像颜色特征和纹理特征分割的麦穗计数方法 93.1%的准确率。

Field wheat ear counting automatically based on improved K-means clustering algorithm

Abstract: The amount of wheat ears per unit area is an important parameter for assessing wheat yield and wheat planting density. Generally, phenotype parameters should be obtained by manual count technique which is time-consuming and needs great effort. Aiming at the traditional image segmentation method based on color feature, texture feature and Haar feature, in this paper, we proposed an improved K-means algorithm for estimating the number of wheat ears. This method established a direct mapping relationship from the low-level features of the image to the number of wheat ears in the image through the color feature so that the target did not need to be segmented or detected. This type of method was more suitable for complex lighting and dense wheat ears, and had higher computational efficiency than counting methods based on image segmentation. This method made full use of the color feature of wheat ear image, took the area feature of local region extracted from local region as the basis of wheat ear judgment, and used the number of sub-regions in clustering region as the estimation value of wheat ear number, thus avoiding the task of target detection and location, and greatly improving the accuracy of Wheat ear counting. According to classification results of K-means method, the wheat ear image was divided into three regions. The green area pixel value representing the wheat eat region was set to 255, the pixel value of other areas was set to 0, so the binary image of wheat ears was obtained. Most of the binarized areas in the image were wheat ears, and a small part of the binarized area that was too small or too large was caused by the leaves. Following the steps were used to count wheat ears: 1) extracting the connected regions of the binary image and labeling them; 2) calculating the area of each connected region, the area was represented by the number of pixels in the region; 3) using the following method to filter out the connected region where the area was too small or too large; 4) finally, the number of wheat ears was obtained by counting the retained binary regions. This method established a direct classification relation from the image color feature of the lower layer to the image containing wheat ears so that the image did not need to be segmented or detected. Based on color feature clustering, this method could estimate the number of wheat ears in local area of space, and it was more scalable without training. The average prediction precision of the total wheatear number in a wheatear image for 12 wheatear images was 94.69%. And the Statistical error of the total wheatear number was between 2.17% and 7.41%, the average statistical error was 5.31%. The statistical experiment results showed that the accuracy of this algorithm to wheat ear counting was better than the traditional method based on image color feature and texture feature.