基于苹果树冠层计盒维数的光照分布预测
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摘要
果园精细管理中,苹果树冠层结构决定了叶幕期光照分布情况,而叶幕期光照分布又是关系到果实产量和质量的重要因素之一。该文以纺锤体苹果树为研究对象,提出了基于苹果树冠层计盒维数的光照分布预测方法。在冠层尺度内,按照网格法划分休眠期苹果树冠层三维点云数据,通过分析该数据构成的果树冠层空间结构,提出用计盒维数量化果树冠层结构的方法;通过分析休眠期冠层结构特征和叶幕期冠层相对光照分布特点,研究了休眠期苹果树三维冠层网格空间计盒维数与叶幕期冠层光照空间分布之间的关系,预测了叶幕成形期苹果树冠层光照分布。通过连续3 a的数据分析,叶幕期苹果树冠层阳面光照分布平均预测精度为76.11%,阴面平均光照分布预测精度为74.10%,该方法可为苹果树自动化修剪合理性评判提供技术支持。
The fruit tree canopy structure determines the canopy illumination spatial distribution(CISD) in every growth stage, and especially in the stable stage, the CISD degree is one of the most important factors related to fruit yield and quality. In the fine management of orchards, in the quiescent stage, trees were trimmed to ideal structures in order to get the high quality CISD in the stable stage. As we all know, the trimming effect directly affects the distribution of the CISD. To analyze the CISD associated with the spring pruning in the apple tree canopy, spindle-shaped apple tree was taken as the research object, and a predicting methodology, the illumination spatial distribution prediction method considering apple tree canopy fractal features, was proposed based on the three-dimensional(3 D) canopy structure. In this study, the canopy was divided into cell grids regularly. The Trimble TX8 was used to get the 3 D apple tree canopy point clouds, and then the tree canopy point clouds were cut into cell grids as the same size as in the orchards, too. To describe the different structures within the canopy, each cell grid was projected on a horizontal surface, which was perpendicular to the tree trunk and parallel to the ground. The projection of each grid was different, but similar with each other. Because of these characteristics, a new box-counting dimension based on 3 D point clouds projection approach of the cell grid was used to describe different cell grids' spatial structure. Onset light intensity acquisition system was used to obtain the illumination intensity in each cell grid in the stable growth stage from June to September. Further, the average illumination intensity of corresponding cell grid was calculated. In the prediction research work, a practical new hybrid model to predict trimming effect based on the relative CISD in apple tree canopy was proposed. The model was based on particle swarm optimization(PSO) in combination with support vector machines(SVMs). This optimization mechanism involved kernel parameter setting in the SVM training procedure, which significantly influences the regression accuracy. The kernel function was determined by analyzing the influence of 4 different kernel functions(linear, polynomial, RBF(radial basis function) and sigmoid) on prediction accuracy, mean squared error and squared correction coefficient. The comparative analysis result was that the RBF kernel function achieved the expected result. The prediction model was based on the statistical learning theory and goodness of fit to experimental data, and successfully used here to predict the relative CISD. This combination of 2 different descriptors, which represented 2 features of a cell-grid, was utilized for subsequent classification(invalid light area or high quality light area) by employing the model. In the field experiment, the cell grid size was 0.4 m × 0.4 m × 0.4 m. All the cell grids were divided into 2 groups, facing to the sun(FS) and backing against the sun(BS). The model in this paper was trained by the data in 2014, 2015 and 2016, and then predicted the relative CISD in 2017. At the same time, the model was trained by the data in 2015, 2016 and 2017, and then predicted the relative CISD in 2018, too. The experimental results showed that the classification prediction accuracies of the FS model and the BC model were 76.11% and 74.10%, respectively, which indicated the good performance of the proposed method. The specific method proposed in this paper can make a contribution to the fruit quality management of apple orchard.
The fruit tree canopy structure determines the canopy illumination spatial distribution(CISD) in every growth stage, and especially in the stable stage, the CISD degree is one of the most important factors related to fruit yield and quality. In the fine management of orchards, in the quiescent stage, trees were trimmed to ideal structures in order to get the high quality CISD in the stable stage. As we all know, the trimming effect directly affects the distribution of the CISD. To analyze the CISD associated with the spring pruning in the apple tree canopy, spindle-shaped apple tree was taken as the research object, and a predicting methodology, the illumination spatial distribution prediction method considering apple tree canopy fractal features, was proposed based on the three-dimensional(3 D) canopy structure. In this study, the canopy was divided into cell grids regularly. The Trimble TX8 was used to get the 3 D apple tree canopy point clouds, and then the tree canopy point clouds were cut into cell grids as the same size as in the orchards, too. To describe the different structures within the canopy, each cell grid was projected on a horizontal surface, which was perpendicular to the tree trunk and parallel to the ground. The projection of each grid was different, but similar with each other. Because of these characteristics, a new box-counting dimension based on 3 D point clouds projection approach of the cell grid was used to describe different cell grids' spatial structure. Onset light intensity acquisition system was used to obtain the illumination intensity in each cell grid in the stable growth stage from June to September. Further, the average illumination intensity of corresponding cell grid was calculated. In the prediction research work, a practical new hybrid model to predict trimming effect based on the relative CISD in apple tree canopy was proposed. The model was based on particle swarm optimization(PSO) in combination with support vector machines(SVMs). This optimization mechanism involved kernel parameter setting in the SVM training procedure, which significantly influences the regression accuracy. The kernel function was determined by analyzing the influence of 4 different kernel functions(linear, polynomial, RBF(radial basis function) and sigmoid) on prediction accuracy, mean squared error and squared correction coefficient. The comparative analysis result was that the RBF kernel function achieved the expected result. The prediction model was based on the statistical learning theory and goodness of fit to experimental data, and successfully used here to predict the relative CISD. This combination of 2 different descriptors, which represented 2 features of a cell-grid, was utilized for subsequent classification(invalid light area or high quality light area) by employing the model. In the field experiment, the cell grid size was 0.4 m × 0.4 m × 0.4 m. All the cell grids were divided into 2 groups, facing to the sun(FS) and backing against the sun(BS). The model in this paper was trained by the data in 2014, 2015 and 2016, and then predicted the relative CISD in 2017. At the same time, the model was trained by the data in 2015, 2016 and 2017, and then predicted the relative CISD in 2018, too. The experimental results showed that the classification prediction accuracies of the FS model and the BC model were 76.11% and 74.10%, respectively, which indicated the good performance of the proposed method. The specific method proposed in this paper can make a contribution to the fruit quality management of apple orchard.
引文
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[2]章兰芬,张社奇,李丙智,等.“V”字形苹果幼树二维图像的分形特征[J].西北农业学报,2012,21(11):128-134,179.Zhang Lanfen,Zhang Sheqi,Li Bingzhi,et al.Fractal characteristics of two-dimensional images of young apple trees trained to V-shaped configuration[J].Acta Agriculturae Boreali-Occidentalis Sinica,2012,21(11):128-134,179.(in Chinese with English abstract)
[3]王建新,牛自勉,李志强,等.乔砧富士苹果不同冠形相对光照强度的差异及对果实品质的影响[J].果树学报,2011,28(1):8-14.Wang Jianxin,Niu Zimian,Li Zhiqiang,et al.Influences of different canopy structures on their relative light intensity and fruit quality of Naganofuji No.2 apple[J].Journal of Fruit Science,2011,28(1):8-14.(in Chinese with English abstract)
[4]李保国,张玉青,张雪梅,等.不同群体结构苹果园光照分布状况研究[J].河北林果研究,2012,27(3):316-319.Li Baoguo,Zhang Yuqing,Zhang Xuemei,et al.Study on light distribution of different group strcture in apple orchards[J].Hebei Journal of Forestry and Orchard Research,2012,27(3):316-319.(in Chinese with English abstract)
[5]Widmer A,Krebs C.Influence of planting density and tree form on yield and fruit quality of“Golden delicious”and“oyal Gala”pples[J].Acta Horticulture,2001,557:235-241.
[6]Musacchi S,Serra S.Apple fruit quality:Overview on pre-harvest factors[J].Scientia Horticulturae,2018,234(4):409-430.
[7]Chenu K,Franck N,Dauzat J,et al.Integrated responses of rosette organogenesis,morphogenesis and architecture to reduced incident light in Arabidopsis thaliana results in higher efficiency of light interception[J].Functional Plant Biology,32(12):1123-1134.
[8]Escobargutiérrez A J,Combes D,Rakocevic M,et al.Functional relationships to estimate morphogenetically active radiation(MAR)from PAR and solar broadband irradiance measurements:The case of a sorghum crop[J].Agricultural&Forest Meteorology,2009,149(8):1244-1253.
[9]马晓丹,郭彩玲,张雪,等.基于三维点云颜色特征的苹果树冠层光照分布计算方法[J].农业机械学报,2015,46(6):263-268.Ma Xiaodan,Guo Cailing,Zhang Xue,et al.Calculation of light distribution of apple tree canopy based on color characteristics of 3D point cloud[J].Transactions of the Chinese Society for Agricultural Machinery,2015,46(6):263-268.(in Chinese with English abstract)
[10]赵春江,陆生链,郭新宇,等.数字植物研究进展:植物形态结构三维数字化[J].中国农业科学,2015,48(17):3415-3428.Zhao Chunjiang,Lu Shenglian,Guo Xinyu,et al.Advancesin research of digital plant:3D digitization of plant morphological structure[J].Scientia Agricultura Sinica,2015,48(17):3415-3428.(in Chinese with English abstract)
[11]Sinoquet H,Roux X L,Adam B,et al.RATP:A model for simulating the spatial distribution of radiation Absorption,transpiration and photosynthesis within canopies:application to an isolated tree crown[J].Plant Cell&Environment,2001,24(4):395-406.
[12]高登涛,韩明玉,李丙智,等.冠层分析仪在苹果树冠结构光学特性方面的研究[J].西北农业学报,2006(3):166-170.Gao Dengtao,Han Mingyu,Li Bingzhi,et al.The characteristic of light distribution in apple tree canopy using Wins Canopy2004a[J].Acta Agriculturae Boreali-occidentalis Sinica,2006(3):166-170.(in Chinese with English abstract)
[13]郭彩玲,宗泽,张雪,等.基于三维点云数据的苹果树冠层几何参数获取[J].农业工程学报,2017,33(3):175-181.Guo Cailing,Zong Ze,Zhang Xue,et al.Apple tree canopy geometric parameters acquirement based on 3D point clouds[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(3):175-181.(in Chinesewith English abstract)
[14]韦雪花,王永国,郑君,等.基于三维激光扫描点云的树冠体积计算方法[J].农业机械学报,2013,44(7):235-240.Wei Xuehua,Wang Yongguo,Zheng Jun,et al.Tree crown volume calculation based on 3-D laser scanning point clouds data[J].Transactions of the Chinese Society for Agricultural Machinery,2013,44(7):235-240.(in Chinese with English abstract)
[15]马晓丹,郭彩玲,张雪,等.基于三维点云颜色特征的苹果树冠层光照分布计算方法[J].农业机械学报,2015,46(6):263-268.Ma Xiaodan,Guo Cailing,Zhang Xue,et al.Calculation of light distribution of apple tree canopy based on color characteristics of 3D point cloud[J].Transactions of the Chinese Society for Agricultural Machinery,2015,46(6):263-268.(in Chinese with English abstract)
[16]廖崴,郑立华,李民赞,等.基于三维点云的苹果树冠层光照分布模型研究[J].中国农业大学学报,2017,22(12):156-162.Liao Wei,Zheng Lihua,Li Minzan,et al.Canopy light distribution modeling for apple trees based on the 3D poiont cloud[J].Journal of China Agricultural University,2017,22(12):156-162.(in Chinese with English abstract)
[17]Glenn D M,Bassett C B,Tworkoski T,et al.Tree architecture of pillar and standard peach affect canopy transpiration and water use efficiency[J].Scientia Horticulturae,2015,187:30-34.
[18]Gao Z Q,Li Z Q.Light use efficiency distribution as a function of different tree shapes in apple[J].Journal of Animal&Plant Sciences,2015,25(3):247-253.
[19]李守根,康峰,李文彬,等.果树剪枝机械化及自动化研究进展与展望[J].东北农业大学学报,2017,48(8):88-96.Li Shougen,Kang Feng,Li Wenbin,et al.Progress and prospect on pruning mechanizationan dautomation of fruit trees[J].Journal of Northeast Agricultural University,2017,48(8):88-96.(in Chinese with English abstract)
[20]Fernandez-Martinez M,Sanchez-Granero M A.Fractal dimension for fractal structures[J].Topology and Its Applications,2014,163(S1):93-111
[21]Nieto P J G,García-Gonzalo E,Fernández J R A,et al.Ahybrid PSO optimized SVM-based model for predicting a successful growth cycle of the Spirulina platensis,from raceway experiments data[J].Ecological Engineering,2015,81(1):534-542.
[22]杨信廷,刘蒙蒙,许建平,等.自动监测装置用温室粉虱和蓟马成虫图像分割识别算法[J].农业工程学报,2018,34(1):164-170.Yang Xinting,Liu Mengmeng,Xu Jianping,et al.Image segmentation and recognition algorithm of greenhouse whitefly and thrip adults for automatic monitoring device[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(1):164-170.(in Chinese with English abstract)
[23]Chang B M,Tsai H H,Yen C Y.SVM-PSO based rotation-invariant image texture classification in SVD and DWT domains[J].Engineering Applications of Artificial Intelligence,2016,52(C):96-107.
[24]黄林生,刘文静,黄文江,等.小波分析与支持向量机结合的冬小麦白粉病遥感监测[J].农业工程学报,2017,33(14):188-195.Huang Linsheng,Liu Wenjing,Huang Wenjiang,et al.Remote sensing monitoring of winter wheat powdery mildew based on wavelet analysis and support vector machine[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(14):188-195.(in Chinese with English abstract)
[25]Wang X Y,Zhang B B,Yang H Y.Active SVM-based relevance feedback using multiple classifiers ensemble and features reweighting[J].Engineering Applications of Artificial Intelligence,2013,26(1):368-381.
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