%0 Journal Article %A Haomiao WANG %A Miaoyu SONG %A Xiang LI %A Chaoyang HU %A Renxiang LU %A Xiang WANG %A Huiqin MA %T High Efficient Grapevine Growth Monitor and In-lane Deficiency Localization by UAV Hyperspectral Remote Sensing %D 2021 %R 10.16420/j.issn.0513-353x.2021-0441 %J Acta Horticulturae Sinica %P 1626-1634 %V 48 %N 8 %X

Precise orchard management requires efficient,comprehensive and accurate growth data acquisition from the entire orchard. Traditional methods in such data collection are inefficient and with subjective bias. To solve the problem,we tested the use of unmanned aerial vehicle(UAV)to carry out growth monitoring and in-lane deficiency localization in a vineyard for two consecutive years. The main results go as:a total of 69 296 004 pixels have been obtained over an area of 187 015 m2 using a multi-spectral camera carrying by a UAV with a flying height of 70 meters and a speed of 5 m · s-1. The data was processed with EnsoMOSAIC software to obtain the Normalized Difference Vegetation Index(NDVI). Taking NDVI > 0.75 as the criterion for good growth,the areas of the vineyard evaluated as“ordinary”,“good”and“excellent”in 2019 were 20.51%,46.15% and 35.90% respectively. Early frost at the beginning of October in 2020 resulted in significant decrease in NDVI and increased standard error,demonstrating that NDVI can be used for timely frost impact assessment. Based on a threshold of continuity < 0.6,in-lane deficiency localization was tested in a Cabernet Sauvignon plot,12 lanes were identified with in-lane deficiency,among of them 10 lanes were located in“ordinary”growth plots,which was in consistent with the evaluation results of the overall growth of the vineyard. Taking together,our results showed that UAV-based hyperspectral remote sensing imaging technology has good application prospects in vineyard plant growth analysis and frost assessment.

%U https://www.ahs.ac.cn/EN/10.16420/j.issn.0513-353x.2021-0441