基于B样条曲线的水稻叶片几何参数测量系统

引用本文

路艳, 杨红云, 周琼, 孙玉婷, 殷华. 基于B样条曲线的水稻叶片几何参数测量系统[J]. 浙江农业学报, 2019,31(6): 996-1004.
LU Yan, YANG Hongyun, ZHOU Qiong, SUN Yuting, YIN Hua. Rice leaf geometric parameter measurement system based on B-spline curve[J]. ACTA AGRICULTURAE ZHEJIANGENSIS, 2019,31(6): 996-1004. 复制到剪切板

doi: 10.3969/j.issn.1004-1524.2019.06.18

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基于B样条曲线的水稻叶片几何参数测量系统

路艳^1,², 杨红云^2,^3,^*, 周琼^2,^3,^*, 孙玉婷^1,², 殷华^2,³

1.江西农业大学计算机与信息工程学院,江西南昌 330045

2.江西省高等学校农业信息技术重点实验室,江西南昌 330045

3.江西农业大学软件学院,江西南昌 330045

*通信作者,杨红云,E-mail: nc_yhy@163.com

作者简介:路艳(1995—),女,江西上饶人,硕士研究生,研究方向为数字图像处理与农业信息技术。E-mail: 18770913658@163.com

收稿日期: 2018-12-03

基金资助: 国家自然科学基金(61562039); 江西省教育厅科技项目(GJJ160374,GJJ170279,GJJ150425)

摘要

基于B样条曲线的水稻叶片几何参数测量系统能无损、精确获取水稻叶片几何参数,可为农学研究提供精准数据。该研究分析了B样条曲线拟合基本原理,采用1次B样条曲线获取水稻最大叶宽,基于3次B样条曲线交互绘制手势轨迹曲线逼近叶脉获取水稻叶长,控制点数量和位置可人工干预,实现轨迹曲线实时调整。运用变异系数和均方根误差评价测量结果,结果显示,叶长变异系数为2.4%,叶宽变异系数为5.4%,均小于15%,测量结果精度较高;建立系统预测值和实测值线性回归拟合模型,水稻叶长均方根误差为1.534 0 cm,叶宽均方根误差为0.101 9 cm,系统预测能力较好。利用B样条曲线测量叶片几何参数,前期图像获取简便,能够真正实现便捷、无损、准确获取水稻叶长、叶宽,为水稻叶片几何参数的测定提供了一种新的测量方法,可满足农业科学研究基本需要。

关键词: 水稻叶片; 叶长; 叶宽; B样条曲线; 轨迹曲线

中图分类号:S511 文献标志码:A 文章编号:1004-1524(2019)06-0996-09

Rice leaf geometric parameter measurement system based on B-spline curve

LU Yan^1,², YANG Hongyun^2,^3,^*, ZHOU Qiong^1,², SUN Yuting^1,², YIN Hua^2,³

1. School of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang 330045, China

2. Key Laboratory of Agricultural Information Technology of Colleges and Universities in Jiangxi Province, Nanchang 330045, China

3. School of Software, Jiangxi Agricultural University, Nanchang 330045, China

Abstract

In order to obtain accurate geometric parameters of rice leaves non-destructively and to provide accurate data for agronomic research, the rice leaf geometric parameter measurement system based on B-spline curve was developed. The basic principle of B-spline curve fitting was elaborated, and the maximum leaf width of rice was obtained by using first order B-spline curve and the leaf length was obtained with gesture trajectory curve drawn to approximate the leaf vein based on cubic B-spline curve. Moreover, the number and position of control points could be manually intervened, therefore to realize real-time adjustment of the trajectory curve. In the results analysis, coefficient of variation (CV) and root mean square error (RMSE) were applied to evaluate the precision and accuracy of results. The leaf length CV was 2.4%, the leaf width CV was 5.4%, both were less than 15%, which meant that the results were of high precision; Meanwhile, linear regression fitting model of system prediction and measured values was established, found that the RMSE of leaf length was 1.534 0 cm and the RMSE of leaf width was 0.101 9 cm, which demonstrated that the system had excellent prediction ability. The results showed that the B-spline curve based measurement system could truly realize accurate acquisition of rice leaf length and leaf width conveniently and non-destructively and without much restriction in photo shooting, which provided a new measurement method for the determination of rice leaf geometric parameters and could satisfy the basic needs of agricultural scientific research.

Keyword: rice leaf; leaf length; leaf width; B-spline curves; trajectory curve

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植株形态结构是育种表型分析中的重要表型性状, 植株叶片几何参数测量分析是作物超高产育种研究的重要环节之一, 准确获取叶片几何参数, 分析作物生长状况, 有利于指导农业生产研究^{[1, 2]}。传统的叶片几何参数测量方法有直尺法^[3]、网格法^{[4, 5]}和叶面积仪法^{[6, 7]}等, 但这些方法耗时长, 价格高昂, 影响植株正常生长, 不利于育种工作开展^{[8, 9, 10]}。不少学者为能快速、准确获取植株几何参数做了大量研究^{[11, 12, 13, 14, 15]}。刘浩等^[16]通过扫描仪获取叶片图像, 使用图像处理软件AutoCAD获取叶长和叶宽, 发现图像处理软件与实测方法测定结果呈显著相关性。牛珂^[17]采用基于图像处理的植物叶片参数测量系统获取叶长和叶宽, 测定得到叶长和叶宽与实测平均相对误差小于3.15%。基于计算机的图像处理方法, 图像获取多依赖数码相机和扫描仪等精密仪器, 价格昂贵, 且操作复杂, 不易于推广^{[18, 19, 20]}。随着智能手机的普及, 基于Android系统获取叶片形态特征参数的应用也越来越多^{[21, 22, 23]}。夏永泉等^[24]基于智能手机根据叶片表征进行病害识别, 识别效果较好。周超超^[25]依据叶片颜色特征建立了玉米叶片含氮量检测系统, 可以指导玉米正确、合理施氮。王洋洋等^[26]基于Android拍摄柽柳照片, 计算柽柳俯视面积, 实现了无损、快速估测柽柳生物量。目前, 基于Android获取植物叶片几何参数研究也较多。龚爱平等^[27]基于Android获取叶片面积, 测量精度达到0.001 cm², 具备良好的叶面积测量能力, 但需借助工字架等工具, 不适用于野外操作, 而且采集图像时为保证叶片完全舒展, 采用亚克力板压平叶片, 操作不当, 易损伤叶片。徐义鑫等^[28]使用手机智能相机获取叶片图像, 根据植物叶片轮廓求叶片、叶宽, 与实际值相比, 误差小于2%, 具备精度高、成本低、无损测量等优点, 但野外环境下图像获取易受光照、角度、参照物和拍摄背景等因素影响, 对图像获取条件要求较高, 非专业人员操作复杂, 不易推广。虽已有人对叶片几何参数进行了大量研究, 但利用手势路径获取叶片几何参数的研究还鲜见报道。本研究提供基于B样条的水稻叶片几何参数测量系统, 采用Android智能相机获取水稻叶片自然生长状态图像, 前期图像获取简便, 无需对获取后图像进行预处理, 通过绘制手势轨迹曲线计算叶长和叶宽, 提供了一种准确、无损的植株叶片几何参数测量方法, 可为水稻超高产育种提供参考。

1 材料与方法

1.1 试验设计

水稻田间试验于2018年在江西农业大学农学试验站进行, 以自然状态下水稻叶片为研究对象。选用Android智能手机在自然光照下获取水稻叶片活体图像, 其型号是m2 note2, 版本5.1, 处理器八核1.3 GHz, 摄像头分辨率1920 pixel× 1080 pixel, 运行内存2 GB。App应用在win7操作系统下使用Android SDK+JDK+Eclipse+ADT开发完成。

1.2 试验方法及原理

系统工作流程如图1所示。结合Android SDK类提供的Linear Layout、Spinner类, 采用ImageView、TextView等实现水稻叶片几何参数测量系统人机交互界面。系统功能分为图像获取、曲线操作和叶片几何参数计算3大部分。

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	图1 系统流程图Fig.1 Workflow of system

1.2.1 叶片图像获取

水稻叶片图像分为相机和相册2种获取方式。经反复试验, 现提出以下图像采集标准:

(1)选择参照物。本文参照物是2.5 cm× 2.5cm的黑色正方形, 参照物形状、大小可人工干预。

(2)设置相机参数。相机分辨率设置为1 920× 1 080, 相机分辨率可调, 关闭闪光灯减少干扰。

(3)获取图像。无需规定最佳拍摄时间段, 可全天进行拍摄。拍摄时尽量保持相机与参照物平行, 获取水稻长度时, 获取水稻大致轮廓即可, 获取水稻宽度仅需拍摄包含水稻最大宽度部分。拍摄距离不限, 得到被测物体与参照物清晰完整图像即可。具体操作方式如图2所示。

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	图2 手机相机获取图像Fig.2 Image acquisition by phone camera

1.2.2 轨迹曲线实现原理

轨迹平滑处理采用的曲线多为抛物样条曲线和参数样条曲线, 但这2种方法在曲线平滑度处理上存在一定缺陷^[29]。本文拟采用B样条曲线绘制手势轨迹曲线, 获取水稻叶片几何参数。B样条曲线兼备贝塞尔曲线的一切优点, 同时克服了贝塞尔曲线无法进行局部修改的缺点^[30]。其由一组控制点求得, 改变控制点, 叶片轨迹曲线也会发生变化^[31]。B样条曲线数学定义为:

$E (u) = \overset{n}{\sum_{t = 0}} c_{t} V_{t, k} (u)$ 。 (1)

式(1)中, c_t(t=0, 1, ..., n)为样条曲线的控制顶点, ${V_{t}}_{, k}$ (t=0, 1, ..., n)为k次B样条基函数, 基函数由非递减参数序列U: u₀≤ u₁≤ …≤ ${u_{n}}_{+ k + 1}$ 决定, 其Cox-deBoor递归公式定义为:

V_t_{, 0}(u)= $\{\begin{array}{l} 1 u_{t} \leq u \leq u_{t + 1} \\ 0 其他 \end{array}$ ; (2)

V_t_,_k(u)= $\frac{u - u_{t}}{u_{t + k} - u_{t}}$ V_t_,_k_-1(u)+ $\frac{u_{t + k + 1} - u}{u_{t + k + 1} - u_{t + 1}}$ V_t_+1,_k_-1(u)。 (3)

公式(2)(3)分别表示若次数k为0, 基函数为阶梯函数; 当k大于0时, V_t_,_k基于V_t_,_k_-1和V_t_+1,_k_-1进行计算。

1.2.3 轨迹曲线实现过程

(1)调整控制点。采用B样条曲线依据水稻叶片形态绘制叶脉和最大叶宽轨迹曲线, B样条控制点坐标可调, 保证曲线最大程度贴合叶片叶脉与最大叶宽(如图3所示)。曲线控制点调整原理为:所有的控制点保存至集合, 获取当前手势点坐标, 遍历集合中所有控制点, 使用欧氏距离^{[32, 33]}计算当前点与集合中所有点的距离, 当距离小于一定范围时, 则认为当前点与集合中满足条件的点为相同点, 移动当前手势点, 将移动结束时的点坐标覆盖原先集合中满足条件的点坐标, 达到调整样条轨迹曲线的目的。

d= $\sqrt[]{(c_{2} - c_{1})^{2} + (r_{2} - r_{1})^{2}}$ 。 (4)

公式(4)中, d为当前点(c₁, r₁)到集合中控制点(c₂, r₂)间的距离。

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	图3 调整控制点 a, 调整前; b, 调整后。Fig.3 Adjustment of control points a, Unadjusted; b, Adjusted.

(2) 删除、清空控制点。曲线操作除控制点可调外, 还包括删除控制点和清空曲线2大功能(如图4所示)。基于控制点绘制手势轨迹曲线易偏离目标轨迹, 为减小误差, 点击删除控制点按钮可对当前最新控制点进行删除, 及时矫正曲线走向。采用B样条曲线获取水稻叶片几何参数, 需借助参照物, 清空曲线操作用于绘制完叶片长度轨迹后, 对曲线进行清空, 进而对参照物长度进行绘制, 第2次进行清空时, 界面将会显示叶片长度数据, 叶片最大宽度同理可得。

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	图4 删除、清空控制点 a, 删除前; b, 删除后; c, 清空控制点。Fig.4 Deletion and clear operation of control points a, Before deleting; b, After deleting; c, Clear control points.

1.2.4 几何参数算法设计

根据水稻形态结构选取恰当阶数的B样条曲线。水稻叶片具有“ 长、窄” 的特征^[34], 易产生弯曲, 4阶3次B样条曲线可得到平滑度较好的连续曲线, 适用于叶片长度获取。水稻叶片宽度过窄, 为保证测定结果的准确性, 采用符合水稻叶片宽度特征的2阶1次B样条曲线(2个控制点确定1条直线)进行获取。其中4阶3次B样条曲线基函数和拟合曲线分别为公式(5)和公式(6)。水稻叶片轨迹曲线示意图如图5所示。

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	图5 叶片几何参数轨迹曲线示意图 a, 叶长轨迹曲线; b, 叶宽轨迹曲线。Fig.5 Image of blade geometry parameter trajectory curve a, Trajectory curve of leaf length; b, Trajectory curve of leaf width.

4阶3次B样条曲线基函数为

$\{\begin{array}{l} V_{0, 3} (u) = \frac{{(1 - u)}^{3}}{6} \\ V_{1, 3} (u) = \frac{(3 u^{3} - 6 u^{2} + 4)}{6} \\ V_{2, 3} (u) = \frac{(- 3 u^{3} + 3 u^{2} + 3 u + 1)}{6} \\ V_{3, 3} (u) = \frac{u^{3}}{6} \end{array}$ 。 (5)

4阶3次B样条拟合曲线为:

E(u)=c₀× V_{0, 3}(u)+c₁× V_{1, 3}(u)+c₂× V_{2, 3}(u)+c₃× V_{3, 3}(u)。 (6)

其中, 公式(5)函数伪代码如下:

if(0< =u)And(u< 1)

then return a* u* u* u

else if(1< =u)And(u< 2)

then return a* (-3* Math.pow(u-1, 3)+3* Math.pow(u-1, 2)+3* (u-1)+1)

else if(2< =u & & u< 3)

then return a* (3* Math.pow(u-2, 3)-6* Math.pow(u-2, 2)+4)

else if(3< =u & & u< 4)

then return a* Math.pow(4-u, 3)

else return 0;

end if

手势轨迹曲线绘制完成后, 计算水稻叶片和参照物长、宽轨迹曲线长度, 参照物实际长、宽已知, 通过比例计算获取叶片真实长度和最大叶宽。叶长、叶宽计算公式为公式(7)(8)。水稻叶片几何参数获取结果如图6所示。

L= $\frac{L_{1} \times L_{0}}{L_{2}}$ ; (7)

W= $\frac{W_{1} \times W_{0}}{W_{2}}$ 。 (8)

公式(7)(8)中, L、W、L₁、W₁分别代表叶片和参照物实际长宽, L₀、W₀、L₂、W₂分别为叶片与参照物轨迹曲线的长度和宽度。

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	图6 叶片几何参数计算结果 a, 叶长计算结果; b, 叶宽计算结果。Fig.6 Results of blade geometric parameters a, Calculation result of leaf length; b, Calculation result of leaf width.

2 结果与分析

2.1 精确性分析

精确性是对同一物体进行重复多次测量, 测量结果彼此间的接近程度。变异系数为多组数据标准差与平均值之比, 采用变异系数(CV)对系统精确度进行衡量, 变异系数越小, 表明系统测量结果越稳定, 精确度越高。表1是采用本文测量方法对同一叶片进行10次重复测量的测定结果。由表1可知, 使用系统方法获取水稻叶片几何参数, 叶长变异系数为2.4%, 最大叶宽变异系数为5.4%, 均小于15%, 表明数据正常, 测试结果稳定。

表1 精确性测试结果 Table 1 Result of accuracy test

2.2 准确性分析

准确性是系统测试结果与叶片实测值的差异度量, 为验证系统准确性, 以系统测量得到的水稻数据为分析样本, 随机选取30组, 与人工测量获取得到的叶长、叶宽进行回归分析(图7), 以拟合精度评估参数均方根误差, 以R²评估系统获取几何参数的准确性。结果显示, 叶长线性拟合模型与叶宽线性拟合模型的决定系数分别为0.973 2和0.770 5, 均方根误差分别为1.534 cm和0.101 9 cm。通过决定系数可发现系统获取叶片几何参数与叶片实际几何参数的数据模型拟合度高, 表明系统具有较好的数据处理和预测能力。

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	图7 水稻叶片几何参数线性拟合模型 a, 叶长线性拟合模型; b, 叶宽线性拟合模型。Fig.7 Linear fitting models of rice leaf geometry parameters a, Linear fitting model of leaf length; b, Linear fitting model of leaf width.

2.3 试验方法比较

本文方法与基于轮廓最小邻接矩形获取水稻叶片几何参数的方法进行比较。基于B样条曲线测量过的图片, 采用基于轮廓获取水稻叶片几何参数的方法进行处理, 发现前期图像获取对于光照、角度、拍摄背景要求较高, 需将水稻平展开, 要求拍摄完整清晰水稻叶片和参照物图像, 从图8-c可以看出, 拍摄背景较为复杂, 参照物和目标叶片受到遮挡情况下, 无法准确识别、获取目标叶片几何参数。本研究系统方法对环境要求低, 无需干扰水稻自然生长状态, 拍摄水稻完整图片即可手势获取目标水稻叶片几何参数, 拍摄条件简单, 操作简便。

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图8 不同方法对复杂背景下图片处理结果
a, 叶长轨迹曲线; b, 基于B样条曲线叶长计算结果; c, 提取叶片轮廓; d, 基于轮廓计算结果。Fig.8 Image processing results by different methods for complex environments
a, Trajectory curve of leaf length; b, Calculation result of leaf length based on B-spline curve; c, Extracting leaf contours; d, Calculation result based on leaf contour.

单一背景下, 如图9所示, 对同一叶片分别使用轮廓最小邻接矩形和系统方法进行测量, 获取30片水稻叶片几何数据, 对2组数据进行分析研究, 比较各自性能, 比较结果见表2。

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	图9 不同方法对单一背景下图片处理结果 a, 基于B样条曲线计算结果; b, 基于轮廓计算结果。Fig.9 Image processing results by different methods for clean environments a, Calculation result of leaf length based on B-spline curve; b, Calculation result based on leaf contour.

表2 不同方法结果比较 Table 2 Comparison of results of different acquisition methods

由表2看出, 2种方法测定结果平方相关系数、均方根误差非常相近, 基于B样条曲线获取水稻叶片几何参数, 叶长的决定系数和均方根误差分别为99.19%和0.542 8, 叶宽的决定系数和均方根误差分别为80.96%和0.054 7, 整体略优于基于轮廓获取叶片几何参数方法。时间为拍摄到结果获取总时间, 系统方法获取叶片几何参数时间小于2 min, 基于轮廓获取几何参数前期图像获取拍摄要求较高, 非专业人员操作困难, 获取有效图像需耗费大量时间; 系统方法前期图像获取, 操作简便, 可节省大量时间。

3 讨论

本研究提出基于B样条曲线的水稻叶片几何参数测量方法, 使用matlab建立系统预测值与实测值回归拟合模型, 结果表明, 系统预测结果良好。目前植物叶片几何参数获取方法研究较多。朱波等^[35]使用直尺测量法获取铁皮石斛叶长、叶宽, 为计算叶面积提供了可靠的依据。采用传统方法获取叶片几何参数有利有弊, 虽能很大程度保证数据的准确性, 但费时费工, 且操作复杂。本研究基于Android获取水稻几何参数, 手机拍照采集水稻图像即可获取水稻几何参数。郭斯羽等^[36]基于叶片骨架提取叶长, 测量误差低于10%, 但对叶片外部状态要求较高, 要求叶片外部形态完整且对称, 不可发生弯曲, 否则将会产生较大的误差。基于骨架获取叶片几何参数, 对被测物体环境也具有严格的要求, 若叶片较为茂盛, 相互重合, 或被测物体与背景颜色融合, 需人工干预作物正常生长状态获取所需图像, 易对植物叶片造成损害, 影响作物后期生长。基于B样条获取叶长、叶宽, 对叶片外部形态要求低, 不干预水稻叶片自然生长状态也能较好地获取叶片几何参数。徐义鑫等^[28]采用Android基于OpenCV无损获取叶片几何参数, 借助叶片轮廓最小外接矩形获取叶片几何参数, 长、宽误差小于2%, 但易受光照、角度等因素影响, 还需利用拍摄背景和胶带等外部手段辅助拍摄完整、平坦的叶片图像, 操作过程复杂。本研究通过绘制水稻叶片主脉和最大叶宽处手势轨迹曲线获取叶片几何参数, 背景对叶片影响较小, 获取叶片大致轮廓与走向即可准确获取叶长、叶宽, 无需借助拍摄背景且受光照、角度的影响较小, 操作简便。

基于B样条曲线的水稻叶片几何参数测量系统, 采集水稻叶片图像无需干扰水稻叶片自然生长状态, 拍摄参照物和水稻叶片的完整清晰轮廓无需预处理即可获取叶长、叶宽, 且受光照影响较小, 通过1次B样条曲线交互绘制最大叶宽处手势轨迹曲线, 基于3次B样条曲线绘制叶脉轨迹曲线, 获取水稻叶片长、宽。计算叶片几何参数变异系数, 分析系统精确度, 结果均小于15%, 测试结果稳定; 建立预测值与实测值之间的线性回归拟合模型, 发现系统预测值和实测值呈显著性相关, 水稻叶长均方根误差为1.534 0 cm, 叶宽均方根误差为0.101 9 cm, 表明基于B样条曲线获取水稻叶片几何参数的方法测量结果准确, 为后期叶面积的预测提供了精准数据, 同时也为视觉获取水稻叶片几何参数及其他农作物的数据测量提供了新的理论研究, 具有较好的实用价值和应用前景。

The authors have declared that no competing interests exist.

参考文献

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2014

0.0

... 植株形态结构是育种表型分析中的重要表型性状,植株叶片几何参数测量分析是作物超高产育种研究的重要环节之一,准确获取叶片几何参数,分析作物生长状况,有利于指导农业生产研究^[1,2] ...

2016

0.0

2004

0.0

... 传统的叶片几何参数测量方法有直尺法^[3]、网格法^[4,5]和叶面积仪法^[6,7]等,但这些方法耗时长,价格高昂,影响植株正常生长,不利于育种工作开展^[8,9,10] ...

2002

0.0

杨劲峰, 陈清, 韩晓日, 等. 数字图像处理技术在蔬菜叶面积测量中的应用[J]. 农业工程学报, 2002, 18(4): 155-158.

YANG J

, CHEN

, HAN X

, et al. Measurement of vegetable leaf area using digital image processing techniques[J]. Transactions of the Chinese Society of Agricultural Engineering, 2002, 18(4): 155-158. (in Chinese with English abstract)

A novel method for measuring individual leaf area of vegetables was developed using digital image processing techniques and scanner. Meanwhile, the measured data from digital image analysis was compared with the values from grid-counting method, leaf-copy and weighing method, instrumental scanning method, etc. The results show that there are close relationships between digital image processing method and the other traditional methods. Relative low variation in measured data was the dominant advantage of image processing method. It can be concluded that digital image analysis technique is suitable to measure leaf area of vegetable in combination with “Maximum length×width”ruler method through correlation and the calibration coefficients for rape (Brassica napus L) and water spinach (Ipomoea aquatica Forsk) were 0.792 and 0.818, respectively.

采用平台扫描仪获取叶片的数字图像，建立运用数字图像处理技术测定蔬菜叶面积的方法。同时与目前较常用的交叉网格法、CID仪器法、复印称重法和生产上常用的直尺法进行比较分析。结果表明：图像处理方法和上述传统的叶面积测定方法的测定结果呈极显著的线性相关关系，适用于叶面积的测量工作；和其它方法相比具有准确、快速、适用范围广、容易普及等优点，适用于科研和生产推广使用。试验还得出采用直尺法时，油菜和空心菜的叶面积的校正系数分别为0.792和0.818。

2017

0.0

2018

0.0

2016

0.0

2018

0.0

2018

0.0

2012

0.0

... 不少学者为能快速、准确获取植株几何参数做了大量研究^{[11,12,13,14,15]} ...

1997

0.0

... 不少学者为能快速、准确获取植株几何参数做了大量研究^{[11,12,13,14,15]} ...

2014

0.0

乐通潮, 张会儒, 谭芳林. 基于自动分类法的红树植物叶片信息测定[J]. 林业科学, 2014, 50(5): 34-40.

LE T

, ZHANG H

, TAN F

. Application of automatic classification method to measuring leaf parameters of mangrove[J]. Scientia Silvae Sinicae, 2014, 50(5): 34-40. (in Chinese with English abstract)

A novel tool, named as "Calculator of leaf parameter", for measuring leaf information of mangrove was developed by using automatic classification method and spatial analysis model in ArcGIS10 software. To verify the feasibility of the automatic classification method, 50 different types of reference polygons were measured by the tool. The relative error is less than 1.3%. The area, perimeter, length and width of 900 leaves from six species of mangrove ( Aegiceras corniculatum, Avicennia marina, Acanthus ilicifolius, Kandelia candel, Hibiscus tiliaceus, Bruguiera gymnorrhiza ) in the Fujian Zhangjiang River Estuary Mangrove National Natural Reserve were measured by using the automatic classification, grid paper, photoshop software. The results showed that the leaf of B. gymnorrhiza was largest in the six species and A. marina 's was least. The leaf sizes of A. corniculatum and A. marina were relative uniform. The leaf shapes of B. gymnorrhiza and A. ilicifolius were long and narrow and the leaf of H. tiliaceus was nearly circular. It took 10 s for the automatic classification method to measure a leaf information, it took 20 s for photoshop, and it took 600 s for grid paper. The least time was needed by the automatic classification, but the longest time was used by grid paper among the three methods. The results of the three methods were significantly correlated with each other, however the automatic classification method was faster, higher efficiency and more information than the others.

1. Research Institute of Forest Resources Information Techniques, CAF Beijing 100091; 2. Fujian Academy of Forestry Fuzhou 350012

利用GIS的空间分析功能和影像自动分类原理，在软件ArcGIS10中构建“叶片信息测定器”，并利用此工具测定50个不同类型参照面的面积、周长、长度和宽度，来验证自动分类法的可行性和精度。结果表明：面积、周长、长度和宽度的相对误差小于1.3%。分别采用自动分类法、网格法和Photoshop法测定福建漳江口红树林国家级自然保护区的6种不同红树植物叶片（桐花树、白骨壤、老鼠簕、秋茄、黄槿和木榄叶片样本各150个，共900个样本）的面积、周长、叶长和叶宽等叶片信息。结果表明：木榄叶片最大，白骨壤叶片最小，桐花树和白骨壤的叶片大小较为均一，木榄和老鼠簕的叶形较为狭长，黄槿的叶形最接近圆形；自动分类法最为快速，平均测定每张叶片信息只需10 s，网格法最慢，每张叶片需要用时600 s，Photoshop法每片用时约20 s；自动分类法、网格法和Photoshop法的变异系数没有显著差异，3种方法所测结果的相关性极显著；与网格法和Photoshop法相比，自动分类法具有更快捷、高效、信息更丰富的特点。

... 不少学者为能快速、准确获取植株几何参数做了大量研究^{[11,12,13,14,15]} ...

2009

0.0

冯辉, 张婷, 王维佳, 等. 基于图像处理技术的番茄部分株型信息的获取[J]. 园艺学报, 2009, 36(6): 923-928.

FENG

, ZHANG

, WANG W

, et al. Some plant-type parameters measurement based on image technology in tomato[J]. Acta Horticulturae Sinica, 2009, 36(6): 923-928. (in Chinese with English abstract)

A measurement software“ToPM-1”was designed and used to measure the plant-type indexes of tomato, including base angle, opening angle, droop ing angle, opening length, droop ing length, leaf length, leafwidth, plant height, plant range, node length and stem diameter. Itwas indicated in the comparative analysis that the difference in the data collected from the manual and the software measurements was not significant. The software measurement is characterized by high efficiency, facility and accuracy, low cost, and non-destruction to p lants. It is an efficient tool in the research on tomato plant-type.

(College of Horticulture, Shenyang Agricultural University, Shenyang 110161, China)

将作者设计的番茄株型测量软件ToPM-1用于测量番茄叶片的基角、开张角、垂角、开张度、垂度、叶长、叶宽、株高、株幅、节间长和茎粗等。软件测量和手工测量结果比较分析表明, 两种测量方法的数据间差异不显著。软件测量具有快速、方便、准确、低成本、不破坏植株等优点, 可用于高效采集番茄株型信息。

... 不少学者为能快速、准确获取植株几何参数做了大量研究^{[11,12,13,14,15]} ...

2018

0.0

... 不少学者为能快速、准确获取植株几何参数做了大量研究^{[11,12,13,14,15]} ...

2009

0.0

刘浩, 孙景生, 段爱旺, 等. 基于AutoCAD软件确定番茄与青椒叶面积的简易方法[J]. 中国农学通报, 2009, 25(5): 287-293.

LIU

, SUN J

, DUAN A

, et al. Simple model for tomato and green pepper leaf area based on AutoCAD software[J]. Chinese Agricultural Science Bulletin, 2009, 25(5): 287-293. (in Chinese with English abstract)

In order to get tomato and green pepper leaf area accurately and conveniently, scanner was used to get leaf images and AutoCAD was used to get actual leaves area. Regression analysis were carried on between actual leaf areas and leaf length, breadth, product of length-breadth measured in the field respectively, and regression models were established between leaf length, leaf breadth, product of leaf length-breadth and the actual leaves area respectively. The results showed that relations between the leaf length, the leaf breadth and the leaf area all assumes the power function, and relation between the product of leaf length-breadth and the leaf area assumes the linear. Errors analysis was carried on for three kinds of estimating models, and the results showed that accuracy of simulated value were bad for regression models of leaf length and leaf breadth. However, the estimated errors were much lower and precision was highest for the product of leaf length-breadth regression models for two kinds of crop, actual size of leaf area can be reflected truly, and the reduction coefficients were 0.6393 and 0.6509 respectively for tomato and the green pepper.

为准确而方便的获取番茄和青椒的叶面积，本研究利用扫描仪获取叶片图像，通过AutoCAD获取叶片的实际面积，并与田间实测叶长、叶宽及叶片长宽乘积分别进行回归分析，分别建立了叶长、叶宽及长宽乘积与实际叶面积的回归模型。研究结果表明，叶长、叶宽与叶面积呈幂函数关系，长宽乘积与叶面积呈线性关系；分别对3种估算模型模拟值进行误差分析结果显示，叶长、叶宽回归模型的模拟精度较差，而长宽乘积回归模型的模拟误差很小，精度较高，可以较真实地反映番茄和青椒叶面积的实际大小，叶片长宽乘积估算叶面积的折减系数分别为0.6393和0.6509。

... 刘浩等^[16]通过扫描仪获取叶片图像,使用图像处理软件AutoCAD获取叶长和叶宽,发现图像处理软件与实测方法测定结果呈显著相关性 ...

2011

0.0

... 牛珂^[17]采用基于图像处理的植物叶片参数测量系统获取叶长和叶宽,测定得到叶长和叶宽与实测平均相对误差小于3 ...

2008

0.0

... 基于计算机的图像处理方法,图像获取多依赖数码相机和扫描仪等精密仪器,价格昂贵,且操作复杂,不易于推广^[18,19,20] ...

2009

0.0

... 基于计算机的图像处理方法,图像获取多依赖数码相机和扫描仪等精密仪器,价格昂贵,且操作复杂,不易于推广^[18,19,20] ...

2006

0.0

... 基于计算机的图像处理方法,图像获取多依赖数码相机和扫描仪等精密仪器,价格昂贵,且操作复杂,不易于推广^[18,19,20] ...

2017

0.0

李洋, 李岳阳, 罗海驰, 等. 基于形状特征的植物叶片在线识别方法[J]. 计算机工程与应用, 2017, 53(2): 162-165.

, LI Y

, LUO H

, et al. Online plant left recognition based on shape features[J]. Computer Engineering and Applications, 2017, 53(2): 162-165. (in Chinese with English abstract)

As to the problems of traditional plant recognition methods for heavy workload, inefficiency and hard to guarantee the objectivity of the data, a plant leaf recognition algorithm based on shape features is presented and an Android application of online plant leaf recognition based on C/S model is developed in this paper. Firstly, the leaf image is preprocessed to get the leaf contour. Then, the traditional shape features and two new ones, the contour convex ratio for plant leaf and the variance of contour curvature, are extracted. Finally, the plant is recognized by using KNN-SVM. Experimental results show that the higher recognition rate can be obtained by using the proposed method compared to some existing algorithms. Also the Android application is stable and reliable for user’s requirement.

1.Ministry of Education Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University, Wuxi, Jiangsu 214122, China 2.Engineering Research Center of Warp Knitting Technology Ministry of Education, Jiangnan University, Wuxi, Jiangsu 214122, China

针对传统植物识别方法工作任务量大，效率低下以及难以保证数据客观性的问题，提出了一种基于形状特征的植物叶片识别算法，并开发了一款C/S模式的植物叶片在线识别Android应用。叶片图像经预处理后，提取叶片的轮廓凸包顶点比、轮廓曲率方差等形状特征，采用KNN-SVM对叶片进行分类识别。实验结果表明，相比于一些已有识别算法，该算法可以达到更高的识别率；该Android应用稳定可靠，可以满足用户的需求。

... 随着智能手机的普及,基于Android系统获取叶片形态特征参数的应用也越来越多^[21,22,23] ...

2016

0.0

... 随着智能手机的普及,基于Android系统获取叶片形态特征参数的应用也越来越多^[21,22,23] ...

0.0

... 随着智能手机的普及,基于Android系统获取叶片形态特征参数的应用也越来越多^[21,22,23] ...

2014

0.0

... 夏永泉等^[24]基于智能手机根据叶片表征进行病害识别,识别效果较好 ...

2014

0.0

... 周超超^[25]依据叶片颜色特征建立了玉米叶片含氮量检测系统,可以指导玉米正确、合理施氮 ...

2017

0.0

... 王洋洋等^[26]基于Android拍摄柽柳照片,计算柽柳俯视面积,实现了无损、快速估测柽柳生物量 ...

2013

0.0

... 龚爱平等^[27]基于Android获取叶片面积,测量精度达到0 ...

2015

0.0

... 徐义鑫等^[28]使用手机智能相机获取叶片图像,根据植物叶片轮廓求叶片、叶宽,与实际值相比,误差小于2%,具备精度高、成本低、无损测量等优点,但野外环境下图像获取易受光照、角度、参照物和拍摄背景等因素影响,对图像获取条件要求较高,非专业人员操作复杂,不易推广 ...

... 徐义鑫等^[28]采用Android基于OpenCV无损获取叶片几何参数,借助叶片轮廓最小外接矩形获取叶片几何参数,长、宽误差小于2%,但易受光照、角度等因素影响,还需利用拍摄背景和胶带等外部手段辅助拍摄完整、平坦的叶片图像,操作过程复杂 ...

2018

0.0

... 轨迹平滑处理采用的曲线多为抛物样条曲线和参数样条曲线,但这2种方法在曲线平滑度处理上存在一定缺陷^[29] ...

0.0

... B样条曲线兼备贝塞尔曲线的一切优点,同时克服了贝塞尔曲线无法进行局部修改的缺点^[30] ...

2017

0.0

... 其由一组控制点求得,改变控制点,叶片轨迹曲线也会发生变化^[31] ...

2013

0.0

张闯, 王婷婷, 孙冬娇, 等. 基于欧氏距离图的图像边缘检测[J]. 中国图象图形学报, 2013, 18(2): 176-183.

ZHANG

, WANG T

, SUN D

, et al. Image edge detection based on the Euclidean distance graph[J]. Journal of Image and Graphics, 2013, 18(2): 176-183. (in Chinese with English abstract)

The results of image edge detection directly affects the subsequent image processing operations. Effectively extracting the information carried by the image target is the main purpose of the image edge detection. In order to realize the extraction of a target outline, an image edge detection method based on a Euclidean distance graph is proposed. The Euclidean distance between points in the image is calculated, and the distance graph is obtained. The scenery outside the outline is well described in the graph. The scenery outline can be effectively extracted by using an improved Canny operator for edge detection. Compared with other general edge detection algorithms, the method can effectively realize the detection of a target's outline. Because small and trivial details are suppressed, the method gives a better basis for further image target outline recognition.

图像边缘检测技术直接影响以目标识别为目的的后续图像处理操作,有效地提取出图像中所携带的目标信息是图像边缘检测的主要目的。为了实现目标轮廓的有效提取,提出一种基于欧氏距离图的图像边缘检测算法。该方法计算图像内像素点之间的欧氏距离,得到图像的距离图,距离图很好地描述了图中景物的外部轮廓;对距离图进行改进的Canny算子边缘检测,可以有效地得到图中物体的轮廓。与一般的边缘检测算法相比,本文算法能够抑制过于细小和琐碎的细节,并能够准确地提取目标的整体轮廓信息,为后续目标识别奠定了良好基础。

... 曲线控制点调整原理为:所有的控制点保存至集合,获取当前手势点坐标,遍历集合中所有控制点,使用欧氏距离^[32,33]计算当前点与集合中所有点的距离,当距离小于一定范围时,则认为当前点与集合中满足条件的点为相同点,移动当前手势点,将移动结束时的点坐标覆盖原先集合中满足条件的点坐标,达到调整样条轨迹曲线的目的 ...

1995

0.0

... 的特征^[34],易产生弯曲,4阶3次B样条曲线可得到平滑度较好的连续曲线,适用于叶片长度获取 ...

2015

0.0

朱波, 华金渭, 吉庆勇, 等. 铁皮石斛杂交育种F₁代叶面积回归测定方法研究[J]. 浙江农业学报, 2015, 27(10): 1761-1764.

ZHU

, HUA J

, JI Q

, et al. Study on regression analysis of leaf area in F₁ generation of Dendrobium officinale cross breeding[J]. Acta Agriculturae Zhejiangensis, 2015, 27(10): 1761-1764. (in Chinese with English abstract)

In the present study， correlations among leaf length， width and weight of leaves， product and sum of leaf length and width， leaf area， stem height， number of leaves and total leaf area in tissue culture plantlet of Dendrobium officinale were studied by regression simulation analysis. It was shown that there was a significant linear relationship between leaf area and product of leaf length and width，of which the determination coefficient was 0987 7. While other determination coefficient was 0938 5（leaf weight）， 0921 8（sum of leaf length and width）， 0723 5 （leaf length）， 0767 8 （leaf width）， respectively. The regression equation between leaf area （y5） and product of leaf length and width （x5） was y5=07977 x5+2405. There was a power function relationship between leaf numbers and total leaf areas， of which the determination coefficient was 0793 1， and the regression equation was y=14764x1666 8. In the early test of Dendrobium officinale cross breeding， leaf areas could be estimated by measuring leaf length and width， and the total leaf areas per stem could be estimated by counting the number of leaves， and therefore to estimate the total leaves of one plantlet or a family. Via the above regression equations， the leaf areas of plantlet could be estimated and measured vividly and quickly， which would provide basis for the selection of superior crossing groups.

（1 Nurturing Station for the State Key Laboratory of Subtropical Silviculture， Research and Development Center for Natural Medicine， Zhejiang A&F University, Linan 311300， China; 2 Lishui Academy of Agricultural Sciences， Lishui 323000， China; 3 Pharmacological College， Zhejiang Chinese Medical University， Hangzhou 310053， China; 4 Zhejiang Senyu Pharmaceutical Co.，Ltd.， Yiwu 322000，China）

以铁皮石斛F1代组培苗为试材，采用回归模拟分析方法，研究叶长、叶宽、叶重、叶长宽积、叶长宽和与单片叶面积、茎高、茎叶片数与茎总叶面积的相关性。结果表明，单片叶面积与叶长宽积的线性关系达极显著水平，判定系数达0987 7；其他变量判定系数分别为0938 5（叶重），0921 8（叶长宽和），0723 5（叶长），0767 8（叶宽）；叶面积（y5）与叶长宽积（x5）回归方程为y5=0797 7 x5+2405；茎叶片数目与茎总叶面积呈幂函数关系，判定系数为0793 1，回归方程为y=14764 x1666 8。在铁皮石斛杂交育种早代测验中，可通过测量叶片长宽来估算叶面积，通过统计每根茎上叶片数目来估算该茎的总叶面积，进而估算整株苗与家系的叶面积。通过该模型的使用，可实现铁皮石斛组培苗叶面积的活体快速估测，为优良杂交组合的选择提供依据。

... 朱波等^[35]使用直尺测量法获取铁皮石斛叶长、叶宽,为计算叶面积提供了可靠的依据 ...

2015

0.0

... 郭斯羽等^[36]基于叶片骨架提取叶长,测量误差低于10%,但对叶片外部状态要求较高,要求叶片外部形态完整且对称,不可发生弯曲,否则将会产生较大的误差 ...