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| Estimation of Above-Ground Biomass and Chlorophyll Content of
Different Alfalfa Varieties Based on UAV Multi-Spectrum |
| SHEN Si-cong, ZHANG Jing-xue, CHEN Ming-hui, LI Zhi-wei, SUN Sheng-nan, YAN Xue-bing* |
College of Animal Science and Technology, Yangzhou University, Yangzhou 225127, China
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Abstract Above-ground biomass and chlorophyll are important indexes in alfalfa's growth process, which can effectively help the dynamic monitoring and management of alfalfa growth. As the most important forage crop, how to effectively and accurately predict the status of alfalfa by using modern spectral intelligence technology is an important issue in the planting process of alfalfa. However, in the development process of spectroscopy, its progress in agriculture is relatively slow. Therefore, establishing a rigorous and accurate prediction model based on spectroscopy knowledge requires certain algorithms, training, testing and verification. Therefore, this experiment studied the estimation results of above-ground biomass and chlorophyll content of different alfalfa varieties based on UAV multi-spectrum and established the prediction model. In this experiment, a total of 21 alfalfa varieties were studied. The UAV equipped with a multi-spectral camera was used to take images in sunny weather without wind, and the images captured by the UAV were analyzed by ENVI 5.3 software. NDVI,EVI, SAVI, Green NDVI,NDGI, DVI, NGBDI, OSAVI, NDRE and MSR. These 10 vegetation indexes and 5 based bands (blue, green, red, red edge and near-infrared) which UAV cameras were analyzed, and then Matlab 2020b software was used to analyze these indexes. A support vector machine (SVM) was used to build the prediction model of above-ground biomass and chlorophyll content in the different alfalfa varieties. In the actual operation, it was found that the accuracy of the prediction model built by SVM was not ideal. Therefore, this experiment used intelligent algorithms whale (WOA) and Gray Wolf (GWO) to optimize the SVM prediction model. The results showed that all prediction models could roughly predict the above-ground biomass and chlorophyll content of different varieties of alfalfa. Among the three models, the SVM prediction model optimized by WOA intelligent algorithms had the highest accuracy in estimating above-ground biomass and chlorophyll content of different alfalfa varieties. Therefore, this experiment can provide certain guidelines for the selection of alfalfa varieties with better quality in the future agriculture. It also provides effective help and reasonable reference for the UVA multi-spectral estimation of alfalfa biomass and its related physiological and ecological indicators in the future.
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Received: 2022-04-11
Accepted: 2022-11-11
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Corresponding Authors:
YAN Xue-bing
E-mail: yxbbjzz@163.com
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