Image‐Analysis‐Based Evaluation of Wheat Growth Status

Abstract

Nondestructive acquisition agronomic parameters of wheat (Triticum aestivum L.) growth status and appropriate evaluation are important to wheat management. This study was performed to construct a model for the estimation of wheat dry weight (DW), leaf area index (LAI), tiller number (TN), and nitrogen accumulation (NC) using image analysis techniques. Wheat groups were constructed under different levels of planting density and nitrogen fertilizer treatments. Images were taken during the early tillering stage using a digital camera. The estimation model for agronomic parameters was then constructed using the stepwise linear regression method, and the evaluation model for wheat group growth status was built using a back‐propagation (BP) neural network. The results show that estimation model proposed in this study offers more accurate simulation of agronomic parameters than the single‐parameter model; the R2 values of DW, LAI, and NC are all >0.8, and the TNs reached 0.72. The average R2 value of the analog values and the measured values using the wheat population growth state evaluation model based on the BP neural network were 0.83. The wheat population growth state evaluation model established using these four agronomic parameters can reflect the status of the wheat groups and provide new evidence for wheat nondestructive diagnosis and field management.