Monitoring Maize Lodging Grades via Unmanned Aerial Vehicle Multispectral Image

Lodging is one of the main factors affecting the quality and yield of crops.Timely and accurate determination of crop lodging grade is of great significance for the quantitative and objective evaluation of yield losses.The purpose of this study was to analyze the monitoring ability of a multispectral image obtained by an unmanned aerial vehicle(UAV)for determination of the maize lodging grade.A multispectral Parrot Sequoia camera is specially designed for agricultural applications and provides new information that is useful in agricultural decision-making.Indeed,a near-infrared image which cannot be seen with the naked eye can be used to make a highly precise diagnosis of the vegetation condition.The images obtained constitute a highly effective tool for analyzing plant health.Maize samples with different lodging grades were obtained by visual interpretation,and the spectral reflectance,texture feature parameters,and vegetation indices of the training samples were extracted.Different feature transformations were performed,texture features and vegetation indices were combined,and various feature images were classified by maximum likelihood classification(MLC)to extract four lodging grades.Classification accuracy was evaluated using a confusion matrix based on the verification samples,and the features suitable for monitoring the maize lodging grade were screened.The results showed that compared with a multispectral image,the principal components,texture features,and combination of texture features and vegetation indices were improved by varying degrees.The overall accuracy of the combination of texture features and vegetation indices is 86.61%,and the Kappa coefficient is 0.8327,which is higher than that of other features.Therefore,the classification result based on the feature combinations of the UAV multispectral image is useful for monitoring of maize lodging grades.

Application of UAV Multisensor Data and Ensemble Approach for High-Throughput Estimation of Maize Phenotyping Traits

High-throughput estimation of phenotypic traits from UAV(unmanned aerial vehicle)images is helpful to improve the screening efficiency of breeding maize.Accurately estimating phenotyping traits of breeding maize at plot scale helps to promote gene mining for specific traits and provides a guarantee for accelerating the breeding of superior varieties.Constructing an efficient and accurate estimation model is the key to the application of UAV-based multiple sensors data.

The Application of UAV-Based Hyperspectral Imaging to Estimate Crop Traits in Maize Inbred Lines

Crop traits such as aboveground biomass(AGB),total leaf area(TLA),leaf chlorophyll content(LCC),and thousand kernel weight(TWK)are important indices in maize breeding.How to extract multiple crop traits at the same time is helpful to improve the efficiency of breeding.Compared with digital and multispectral images,the advantages of high spatial and spectral resolution of hyperspectral images derived from unmanned aerial vehicle(UAV)are expected to accurately estimate the similar traits among breeding materials.This study is aimed at exploring the feasibility of estimating AGB,TLA,SPAD value,and TWK using UAV hyperspectral images and at determining the optimal models for facilitating the process of selecting advanced varieties.The successive projection algorithm(SPA)and competitive adaptive reweighted sampling(CARS)were used to screen sensitive bands for the maize traits.Partial least squares(PLS)and random forest(RF)algorithms were used to estimate the maize traits.The results can be summarized as follows:The sensitive bands for various traits were mainly concentrated in the near-red and red-edge regions.The sensitive bands screened by CARS were more abundant than those screened by SPA.For AGB,TLA,and SPAD value,the optimal combination was the CARS-PLS method.Regarding the TWK,the optimal combination was the CARS-RF method.Compared with the model built by RF,the model built by PLS was more stable.This study provides guiding significance and practical value for main trait estimation of maize inbred lines by UAV hyperspectral images at the plot level.