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

Ziyang Yu,Haixiang Guan,Susan L Ustin,Xiangtian Meng,Zhongchen Zhang,Xinle Zhang,Yang Cui,Huanjun Liu

doi:10.3390/s20144011

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 (R2) = 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.

Highlights

Crop yield is determined predominantly by photosynthesis of the crop canopy
PHavg, LAIavg and CVPavg represent the mean values of the plant height (PH), leaf area index (LAI) and canopy volume parameter (CVP), respectively, for all four growth stages
The R2 value and root mean square error (RMSE) of the global prediction model were 0.81 and 0.66, with the R2 value of the Global prediction modelling (GPM) method, those of the local prediction modelling (LPM) method improved by 13.6%, and the RMSE

Summary

Introduction

The leaf area index (LAI) and plant height (PH) are important indices for characterizing crop canopy structure, which affects yield production and the accumulation of photosynthesis products. PH is an important index for characterizing the competitive ability of plants to maintain a beneficial position within the canopy for the absorption and use of light energy [8], and PH is strongly correlated with both biomass and grain yield [9]. Both the LAI and PH represent the competitive ability of crop plants to secure resources for growth. The LAI and PH are limited in terms of their accuracy of final yield predictions

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Methods

Results

Conclusion