Scientia Agricultura Sinica ›› 2019, Vol. 52 ›› Issue (1): 21-33.doi: 10.3864/j.issn.0578-1752.2019.01.003

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Field Wheat Ears Counting Based on Superpixel Segmentation Method

DU Ying1,2(),CAI YiCheng1(),TAN ChangWei1(),LI ZhenHai2,YANG GuiJun2,FENG HaiKuan2,HAN Dong2   

  1. 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 Online:2019-01-01 Published:2019-01-12
  • Contact: ChangWei TAN E-mail:duying94@126.com;357663951@qq.com;tanwei010@126.com

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

Fig. 1

Experimental design (1) Variety treatment——P1: Zhongmai 175; P2: Zhongyou 206. (2) Nitrogen treatment——N1: 0; N2: 1/2 Normal; N3: 1 Normal; N4: 2 Normal"

Fig. 2

Ears manual counting process"

Fig. 3

Ears segmentation process"

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

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

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

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

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

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

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

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

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

Fig. 4

Ears counting process"

Fig. 5

Color histogram a: Super green index, Eg; b: Normalized red green index, Dgr; c: Normalized blue green index, Dgb"

Fig. 6

Ears segmentation results"

Table 2

Classifier training results (Classification accuracy, %)"

分类器类型
Classifier type
氮水平 Nitrogen level
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

Fig. 7

Ears counting results a. Nitrogen-free, b. Low-nitrogen, c. Normal nitrogen, d. High nitrogen, e. Nitrogen level mixing"

Table 3

Classification training results of nitrogen application samples (n=10 000)"

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

Fig. 8

Ears counting result in nitrogen application"

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