|Table of Contents|

Analysis of Association Rules of Strawberry Leaf Moisture Content Based on Spectral Reflection Characteristics

《北方园艺》[ISSN:1001-0009/CN:23-1247/S]

Issue:
2020年19
Page:
146-151
Research Field:
Publishing date:

Info

Title:
Analysis of Association Rules of Strawberry Leaf Moisture Content Based on Spectral Reflection Characteristics
Author(s):
LI Jun; LIU He; ZHU Liangkuan
(Mechanical and Electrical Engineering，Northeast Forestry University，Harbin，Heilongjiang 150040)
Keywords:
strawberry; leaf moisture content; reflectance spectrum; association rules
PACS:
-
DOI:
10.11937/bfyy.20194451
Abstract:
In order to measure the moisture content of strawberry leaves more conveniently and accurately,the analysis of leaf moisture content was used to guide the irrigation of strawberries in each growth cycle.By studying the Apriori association rule algorithm in data mining algorithms and its application in analyzing the moisture status of strawberry leaves,a D operator model based on the Apriori algorithm is proposed.Collect the spectral reflectance values of different water conditions of strawberry leaves,two correlation indexes of correlation coefficient and error rate were established.The proposed operator model was used to correlate the potential relationship between the water content of canopy leaves and the spectral reflectance of different bands.The rule analysis found out the correlation between the spectral reflectance characteristics of strawberry and leaf water status under four different water treatment methods：drought，mild drought，moderate and overflow.The results confirmed that the water state of the leaves could be judged by the spectral reflectance in the band of 2 037.25-2 560.039 nm，that was，when the spectral reflectance was between 101.554 and 101.560，the water content of the strawberry leaves was drought；when the spectral reflectance was 98.574.When the temperature was between 98.574-99.140，the water content of strawberry leaves was slightly drought；when the spectral reflectance was between 96.379-97.587，the water content of strawberry leaves was moderate；when the spectral reflectance was between 94.583-95.902，the water content of strawberry leaves.The empirical analysis of the overflow showed that the proposed algorithm simplifies the actual operation steps，saves the data space，and proves the feasibility and effectiveness of the proposed algorithm for analyzing the water content of strawberry leaves.

References:

[1]COLOMBO R，MERONI M，MARCHESI A，et al.Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling[J].Remote Sensing of Environment，2008,112(4):1820-1834.[2]SEPULCRE-CANT G，ZARCO-TEJADA P J，JIMNEZ-MUOZ J C,et al.Detection of water stress in an olive orchard with thermal remote sensing imagery[J].Agricultural and Forest Meteorology,2006,136(1):31-44.[3]AREVALO R T,VILLACRES J,FUENTES A,et al.Moisture content estimation of Pinus radiata and Eucalyptus globulus from reconstructed leaf reflectance in the SWIR region[J].Biosystems Engineering,2020,193:187-205.[4] TIE C B,TAO W,YOU Q C,et al.Comparison of near-infrared spectrum pretreatment methods for jujube leaf moisture content detection in the sand and dust area of southern Xinjiang[J].Spectroscopy and Spectral Analysis,2018,39(4):1323-1328.[5] LING L W,RAYMOND H E,JOHN J Q,et al.Estimating dry matter content of fresh leaves from the residuals between leaf and water reflectance[J].Remote Sensing Letters,2011,2(2):137-145.[6]TIAN Q，TONG Q，PU R，et al.Spectroscopic determination of wheat water status using 1650-1850 nm spectral absorption features[J].International Journal of Remote Sensing，2001,22(12)：2329-2339.[7]苏毅,王克,李少昆,等.棉花植株水分含量的高光谱监测模型研究[J].棉花学报,2010,22(6):550-565.[8]SIKHA B，PROAL C D.Positive and negative association rule mining in Hadoop′s MapReduce environment[J].Journal of Big Data,2019,6(1):1-16.[9]龙小勇,蔡良才,沈勇,等.基于关联规则挖掘的水泥混凝土道面综合病害关系研究[J].西华大学学报(自然科学版),2019(5):1-11.[10]陈文敬.基于Apriori算法的电力经济发展关联规则研究[J].科技经济导刊,2019,27(24):4-5.[11]王斌,马俊杰,房新秀,等.基于时间戳和垂直格式的关联规则挖掘算法[J].计算机科学:2019,46(10):71-76.[12]刘晓维,陈俊丽,屈世富,等.一种改进的Apriori算法[J].计算机工程与应用,2011,47(11):150-151.[13]董晨,夏凯.基于Apriori算法的浙西杉木用材林立地及生长因子关联分析[J].浙江农林大学学报,2019,36(4):741-748.[14]梁杨,钱晓东.多最小支持度关联规则改进算法[J].西南大学学报(自然科学版),2019,41(7):131-141.[15]罗海洋.大数据环境下关联规则挖掘算法及其应用研究[D].长沙:湖南大学,2017.[16]郭秀娟,张树彬,岳俊华.基于Apriori数据挖掘算法研究[J].吉林建筑工程学院学报,2010,27(3):55-60.[17]SHU LING Z.Research on data mining of education technical ability training for physical education students based on Apriori algorithm[J].Cluster Computing,2019,22(6):14811-14818.[18]李珺,宋文龙.基于光谱反射特征的草莓叶片含水率模型[J].东北林业大学学报,2016,44(1):72-74.

Memo

Memo:
-
Last Update: 2020-12-24