Detection and Analysis of Degree of Maize Lodging Using UAV-RGB Image Multi-Feature Factors and Various Classification Methods

Zixu Wang,Yadong Liu,Xun Yu,Hongwu Wang,Xiuliang Jin,Yi Bai,Yong Ao,Minghan Cheng,Chenwei Nie,Nuremanguli Tuohuti,Siyu Wang,Shuaibing Liu,Mingchao Shao

doi:10.3390/ijgi10050309

Abstract

Maize (Zea mays L.), one of the most important agricultural crops in the world, which can be devastated by lodging, which can strike maize during its growing season. Maize lodging affects not only the yield but also the quality of its kernels. The identification of lodging is helpful to evaluate losses due to natural disasters, to screen lodging-resistant crop varieties, and to optimize field-management strategies. The accurate detection of crop lodging is inseparable from the accurate determination of the degree of lodging, which helps improve field management in the crop-production process. An approach was developed that fuses supervised and object-oriented classifications on spectrum, texture, and canopy structure data to determine the degree of lodging with high precision. The results showed that, combined with the original image, the change of the digital surface model, and texture features, the overall accuracy of the object-oriented classification method using random forest classifier was the best, which was 86.96% (kappa coefficient was 0.79). The best pixel-level supervised classification of the degree of maize lodging was 78.26% (kappa coefficient was 0.6). Based on the spatial distribution of degree of lodging as a function of crop variety, sowing date, densities, and different nitrogen treatments, this work determines how feature factors affect the degree of lodging. These results allow us to rapidly determine the degree of lodging of field maize, determine the optimal sowing date, optimal density and optimal fertilization method in field production.

Highlights

Maize (Zea mays L.) is the most planted crop in the world and plays an important role in ensuring China’s national food security [1]
The following, we discuss in the detail thefor basis for selecting teristic factors, the evaluation of classification results, the factors that affect the degree of characteristic factors, the evaluation of classification results, the factors that affect the delodging, and the improvements of our experiment over previous work
The vegetation index, texture features, canopy coverage, digital surface model, and characteristic spectral bands are constructed from visible bands and for different degrees of lodging

Summary

Introduction

Maize (Zea mays L.) is the most planted crop in the world and plays an important role in ensuring China’s national food security [1]. It is vital to obtain timely and accurate information on maize lodging after such disasters to help agricultural production authorities take remedial measures quickly and reduce losses and to help insurance companies estimate losses quickly and accurately and thereby provide reasonable compensation after such disasters. Such an approach would allow us to select lodging-resistant crop varieties and improve field-management strategies

Methods

Findings

Discussion

Conclusion