利用智能手机摄影估算水稻新冠层结构参数。

Estimation of a New Canopy Structure Parameter for Rice Using Smartphone Photography.

机构信息

School of Public Administration and Law, Northeast Agricultural University, Harbin 150030, China.

Center for Spatial Technologies and Remote Sensing (CSTARS), John Muir Institute of the Environment, University of California, Davis, CA 95616, USA.

出版信息

Sensors (Basel). 2020 Jul 19;20(14):4011. doi: 10.3390/s20144011.

DOI:10.3390/s20144011

PMID:32707649

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7412381/

Abstract

The objective of this study was to develop a low-cost method for rice growth information obtained quickly using digital images taken with smartphone. A new canopy parameter, namely, the canopy volume parameter (CVP), was proposed and developed for rice using the leaf area index (LAI) and plant height (PH). Among these parameters, the CVP was selected as an optimal parameter to characterize rice yields during the growth period. Rice canopy images were acquired with a smartphone. Image feature parameters were extracted, including the canopy cover (CC) and numerous vegetation indices (VIs), before and after image segmentation. A rice CVP prediction model in which the CC and VIs served as independent variables was established using a random forest (RF) regression algorithm. The results revealed the following. The CVP was better than the LAI and PH for predicting the final yield. And a CVP prediction model constructed according to a local modelling method for distinguishing different types of rice varieties was the most accurate (coefficient of determination (R) = 0.92; root mean square error (RMSE) = 0.44). These findings indicate that digital images can be used to track the growth of crops over time and provide technical support for estimating rice yields.

摘要

本研究旨在开发一种低成本的方法，利用智能手机拍摄的数字图像快速获取水稻生长信息。提出并开发了一种新的冠层参数，即冠层体积参数（CVP），用于利用叶面积指数（LAI）和植株高度（PH）来描述水稻。在这些参数中，CVP 被选为描述水稻生长期间产量的最佳参数。使用智能手机获取水稻冠层图像。在图像分割之前和之后，提取了包括冠层覆盖率（CC）和大量植被指数（VIs）在内的图像特征参数。使用随机森林（RF）回归算法建立了以 CC 和 VIs 为自变量的水稻 CVP 预测模型。结果表明，CVP 比 LAI 和 PH 更适合预测最终产量。根据区分不同类型水稻品种的局部建模方法构建的 CVP 预测模型最为准确（决定系数（R）=0.92；均方根误差（RMSE）=0.44）。这些发现表明，数字图像可用于随时间跟踪作物的生长，并为估计水稻产量提供技术支持。