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

中国农业科学 ›› 2019, Vol. 52 ›› Issue (1): 21-33.doi: 10.3864/j.issn.0578-1752.2019.01.003

• 耕作栽培·生理生化·农业信息技术 • 上一篇下一篇

基于超像素分割的田间小麦穗数统计方法

杜颖^1,²(),蔡义承¹(),谭昌伟¹(),李振海²,杨贵军²,冯海宽²,韩东²

1江苏省粮食作物现代产业技术协同创新中心/江苏省作物遗传生理国家重点实验室培育点/教育部农业与农产品安全国际合作联合实验室/扬州大学农业科技发展研究院,江苏扬州 225009
2农业部农业遥感机理与定量遥感重点实验室/北京农业信息技术研究中心,北京 100097

收稿日期:2018-08-25 接受日期:2018-09-28 出版日期:2019-01-01 发布日期:2019-01-12
基金资助:
国家重点研发计划(2016YFD0300405);江苏高校优势学科建设工程资助项目(PAPD);江苏高校品牌专业建设工程资助项目(PPZY2015A060);国家自然科学基金(31771711);国家自然科学基金(61661136003);扬州大学教学改革研究课题(YZUJX2017-22B)

Field Wheat Ears Counting Based on Superpixel Segmentation Method

DU Ying^1,²(),CAI YiCheng¹(),TAN ChangWei¹(),LI ZhenHai²,YANG GuiJun²,FENG HaiKuan²,HAN Dong²

1 Jiangsu Provincial Collaborative Innovation Center of Modern Crop Science and Technology/National Key Laboratory of Crop Genetics and Physiology of Jiangsu Province/Joint Laboratory of International Cooperation on Agriculture and Agricultural Safety of Ministry of Education/Institute of Agricultural Science and Technology Development, Yangzhou University, Yangzhou 225009, Jiangsu
2 Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture/Beijing Research Center for Information Technology in Agriculture, Beijing 100097

Received:2018-08-25 Accepted:2018-09-28 Published:2019-01-01 Online:2019-01-12

摘要/Abstract

摘要：

【目的】小麦穗数是产量构成的重要因素。通过图像处理技术快速准确地统计小麦穗数,为作物长势监测和产量估测提供重要依据。【方法】本研究以经氮肥梯度处理后不同长势的小麦为研究对象,首先,通过简单线性迭代聚类算法（simple linear iterative cluster,SLIC）对田间小麦图像进行超像素分割的预处理;提取并分析图像的部分颜色特征参数,选择适宜的颜色特征参数训练分类器;选择准确率最高的分类器对图像进行分类处理,识别麦穗。其次,对麦穗识别结果进行二值化;经腐蚀、膨胀等一系列形态学计算提取麦穗主体并进行区域统计;提取麦穗骨架,检测骨架角点数,结合角点数与区域统计结果计算小麦穗数;最后,通过线性回归分析方法验证了无氮（0）、低氮（1/2常规施氮量）、正常氮（常规施氮量）、高氮（2倍的常规施氮量）4个氮水平麦穗统计结果。【结果】（1）利用超绿值（E_g）和归一化红绿指数（D_gr）作为分类特征可以有效地识别麦穗、土壤和叶片;（2）相较于直接基于像素进行图像处理,经超像素分割处理后麦穗识别结果更理想,识别出麦穗主体清晰,形态更为完整;（3）经比较,高氮水平下小麦长势较好,穗数统计准确率最高,为94.4%,无氮水平下小麦长势较差,穗数统计准确率最低,仅为81.9%;排除无氮情况后,长势较均匀的氮水平混合样本中麦穗计数准确率达到92.9%,相较于长势差异较大的混合样本准确率提高了8.3%。【结果】在一般环境下,利用超像素和颜色特征的麦穗自动统计方法可以快速准确地对大田小麦进行穗数计算,长势过弱以及差异过大区域不推荐使用,研究结果为小麦大田估产提供了新的参考。

关键词: 小麦, 识别, 穗数, 超像素, 颜色特征

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

杜颖,蔡义承,谭昌伟,李振海,杨贵军,冯海宽,韩东. 基于超像素分割的田间小麦穗数统计方法[J]. 中国农业科学, 2019, 52(1): 21-33.

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.

图/表 11

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图2

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表1

不同类型分类器特征"

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

表1

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表2

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表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

基于超像素分割的田间小麦穗数统计方法

Field Wheat Ears Counting Based on Superpixel Segmentation Method

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