基于超像素分割的田间小麦穗数统计方法

doi:10.3864/j.issn.0578-1752.2019.01.003

Abstract

Abstract:

【Objective】Wheat ears number is a major factor of yield composition and plays an important role in yield estimation and genetic improvement. Using image processing technology to accurately identify and count wheat ears in the field, a new method for obtaining agricultural information was proposed in this paper, which provided a reliable reference for the yield estimation and crop growth monitoring. 【Method】In this paper, wheat with different growth conditions after treatment with nitrogen fertilizer gradient was the research object. Firstly, the simple linear iterative cluster (SLIC) was used to segment the wheat image, and the unit of an image was transformed from pixels to superpixel block. After analyzing the color histograms, the classifiers were trained to identify wheat ears. Then, we performed a simple morphological treatment on the classification results to segment the wheat ears and performed binarization. We obtained the main body of wheat and conducted regional statistics through a series of morphological calculations, such as corrosion and expansion. The wheat ear skeleton was extracted to detect the skeleton corner points to calculate the number of wheat ears. Lastly, the results of counting wheat ears under different nitrogen levels (no nitrogen, low nitrogen, normal nitrogen, high nitrogen) were verified by linear regression analysis. 【Result】 (1) Super green value (Eg) and normalized red green index (Dgr) could be used as classification features to effectively identify wheat ears after color histogram analysis. (2) Compared to directly identifying the field image, the main body of wheat identified after superpixel segmentation was more explicit and complete in shape. (3) By comparison, wheat with better growth situation had better ear counting results, which approached 94.4% in high nitrogen level. However, the wheat ear count accuracy was low at a poor nitrogen level, which was only 81.9%. After eliminating the nitrogen-free condition, the overall growth of the mixed sample was uniform, and the wheat ear count accuracy reached 92.9%. The accuracy was improved by 8.3% compared to mixed samples with large differences in growth.【Conclusion】In the general environment, the automatic counting method of wheat ears using superpixels and color features could quickly and accurately calculate the number of wheat ears in field. While the growth vigor was too weak and the difference was too large, this method was not recommended. The research results provided a new reference for wheat field estimation.

Key words: wheat, identification, ear number, superpixel, color characteristics

DU Ying,CAI YiCheng,TAN ChangWei,LI ZhenHai,YANG GuiJun,FENG HaiKuan,HAN Dong. Field Wheat Ears Counting Based on Superpixel Segmentation Method[J].Scientia Agricultura Sinica, 2019, 52(1): 21-33.

Figures/Tables 11

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Table 1

Different types of classifier features"

分类器类型 Classifier type	预测速度 Prediction speed	内存占用 Memory usage	解释性 Interpretability	模型灵活性 Model flexibility
线性支持向量机 linSVM	二分类：快 Binary: Fast 多分类：中 Multiclass: Medium	中等 Medium	简单 Easy	低 Low 在类之间进行简单的线性分隔 Makes a simple linear separation between classes
二次多项式支持向量机 quaSVM	二分类：快 Binary: Fast 多分类：慢 Multiclass: Slow	二分类：中等 Binary: Medium 多分类：大 Multiclass: Large	困难 Hard	中等 Medium
三次多项式支持向量机 cubSVM	二分类：快 Binary: Fast 多分类：慢 Multiclass: Slow	二分类：中等 Binary: Medium 多分类：大 Multiclass: Large	困难 Hard	中等 Medium
细高斯支持向量机 finGSVM	二分类：快 Binary: Fast 多分类：慢 Multiclass: Slow	二分类：中等 Binary: Medium 多分类：大 Multiclass: Large	困难 Hard	高,随内核刻度设置而减小 High, creases with kernel scale setting 类之间精细区分,内核刻度为sqrt(P)/4 Makes finely detailed distinctions between classes, with kernel scale set to sqrt(P)/4
中度高斯支持向量机 medGSVM	二分类：快 Binary: Fast 多分类：慢 Multiclass: Slow	二分类：中等 Binary: Medium 多分类：大 Multiclass: Large	困难 Hard	中等 Medium 中度区分,内核刻度为sqrt(P) Medium distinctions, with kernel scale set to sqrt(P)
粗高斯支持向量机 coaGSVM	二分类：快 Binary: Fast 多分类：慢 Multiclass: Slow	二分类：中等 Binary: Medium 多分类：大 Multiclass: Large	困难 Hard	低 Low 在类之间粗区分,内核刻度为sqrt(P)4,其中P为预测因子数 Makes coarse distinctions between classes, with kernel scale set to sqrt(P)4, where P is the number of predictors
细 K最近邻 finKNN	中 Medium	中等 Medium	困难 Hard	类之间细微差异区分,邻域数设为1 Finely detailed distinctions between classes. The number of neighbors is set to 1
中度 K最近邻 medKNN	中 Medium	中等 Medium	困难 Hard	类之间中等差异区分,邻域数设为10 Medium distinctions between classes. The number of neighbors is set to 10
粗 K最近邻 coaKNN	中 Medium	中等 Medium	困难 Hard	类之间粗略差异区分,邻域数设为100 Coarse distinctions between classes. The number of neighbors is set to 100
余弦 K最近邻 cosKNN	中 Medium	中等 Medium	困难 Hard	使用余弦距离度量,在类之间中等区分,邻域数设为10 Medium distinctions between classes, using a cosine distance metric. The number of neighbors is set to 10
三次多项式 K最近邻 cubKNN	慢 Slow	中等 Medium	困难 Hard	使用立方距离度量,在类之间中等区分,邻域数设为10 Medium distinctions between classes, using a cubic distance metric. The number of neighbors is set to 10
加权 K最近邻 weiKNN	中 Medium	中等 Medium	困难 Hard	使用权重距离度量,在类之间中等区分,邻域数设为10 Medium distinctions between classes, using a distance weight. The number of neighbors is set to 10

Table 1

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Table 2

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Table 3

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分类器类型 Classifier type	氮水平 Nitrogen level
分类器类型 Classifier type	N1	N2	N3	N4	混合 Mix
linSVM	81.2	86.2	90.1	90.9	85.1
quaSVM	86.1	89.7	91.9	92.8	87.6
cubSVM	86.6	89.5	92.2	93.8	56.8
finGSVM	84.9	92.6	90.7	93.1	88.2
medGSVM	87.2	90.5	93.5	92.7	88.2
coaGSVM	82.4	87.0	90.6	90.8	86.3
finKNN	85.0	89.5	87.0	92.3	85.2
medKNN	85.8	89.9	92.3	93.0	85.2
coaKNN	83.4	88.4	90.8	90.9	87.2
cosKNN	84.8	89.4	91.1	93.0	87.1
cubKNN	85.5	89.8	92.0	91.4	87.1
weiKNN	86.4	91.1	92.1	92.2	88.2

分类器类型 Classifier type	总体准确率 Overall accuracy (%)	穗部准确率 Ear accuracy (%)	背景准确率 Background accuracy (%)
linSVM	87.1	88.0	86.0
quaSVM	89.2	91.0	87.0
cubSVM	84.0	77.0	91.0
finGSVM	89.2	89.0	90.0
medGSVM	89.5	91.0	88.0
coaGSVM	88.0	90.0	86.0
finKNN	87.4	87.0	87.0
medKNN	89.4	90.0	89.0
coaKNN	89.1	93.0	86.0
cosKNN	88.8	88.0	90.0
cubKNN	88.9	89.0	88.0
weiKNN	90.2	93.0	88.0

Field Wheat Ears Counting Based on Superpixel Segmentation Method

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