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作 者:张新[1] 高超[1] Martin Kraft 赵燕东[1]
机构地区:[1]北京林业大学工学院,北京100083 [2]德国联邦农业林业和渔业研究所农业技术分所,布伦瑞克38116
出 处:《农业机械学报》2016年第11期322-328,共7页Transactions of the Chinese Society for Agricultural Machinery
基 金:国家自然科学基金项目(31371537);北京市教育委员会科学研究与研究生培养共建项目(BLCXY201520)
摘 要:为实现植物生命需水状况的无损检测,通过定量辨识植物叶态萎蔫特征反映植物亏水胁迫状况,基于分形理论"双毯法"定义了萎蔫指数LDBM,利用基于激光三角原理的三维激光扫描仪获取植物叶片表型点云数据,以西葫芦、秋葵、葫芦、南瓜4种植物为研究对象,结合饱和水汽压差与光合有效辐射2种微环境参数,进行了植物叶态萎蔫定量辨识研究。试验分析了4种植物萎蔫指数LDBM的日变化曲线,证明LDBM用于表征叶态萎蔫状况是合理和可行的;分析了萎蔫指数LDBM与微环境参数的相关性,4种植物LDBM与饱和水汽压差相关系数均大于0.81,与光合有效辐射相关系数均大于0.71,与2种参数都有很强的正相关性;并且与基于离散时域傅里叶变换定义的萎蔫指数L2DFT进行了对比,LDBM与L2DFT呈负相关性,4种植物的相关系数均大于0.82,表明LDBM能够准确地定量表征叶态萎蔫。Plant leaf wilting is a frequent symptom responding to drought stress. To realize non- destructive testing of leaf wilting, wilting index LDBM was customized based on double blanket method from fractal theory. A laser scanner was used to acquire three-dimensional point cloud data of plant leaf. In the experiment, zucchini, okra, gourd and pumpkin were chosen as research objects to test the index. Meanwhile, the vapor pressure deficit and photo synthetically active radiation were collected as the microenvironment parameters. The seeds were individually sown in the same pots. After the young plant grew, they were placed in a plastic-covered tunnel. The soil water content for these plants was maintained at about 30%. The point cloud data was gathered at 1 h interval between 08:00 and 18:00. The results showed that the index LDBM was capable of sensing various leaf wilting levels. Regression results showed that Loser had a linear relationship with vapor pressure deficit and photo synthetically active radiation. And the correlation coefficients of four plants were [ 0. 810, 0. 879 ] and [ 0. 712, 0. 825 ], respectively. Furthermore, LDOM had a negative linear relationship with LDOM, which was tested to be a reasonable wilting index in previous studies. The correlation coefficients of four plants were [ 0. 826, 0. 944]. This paper suggested a non-destructive, real-time and in situ method for monitoring plant water deficit stress.
分 类 号:S24[农业科学—农业电气化与自动化] Q945.174[农业科学—农业工程]
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