Study on band screening of pitaya fruit based on unmanned aerial vehicle hyperspectral image

doi:10.14088/j.cnki.issn0439-8114.2022.22.028

Abstract

Abstract: In order to solve the problems of low resolution, long acquisition period and high price of satellite hyperspectral image, the advantages of high resolution, low price and good timeliness could be obtained by using the hyperspectral sensor mounted on the UAV. Taking the Dragon Fruit Planting Demonstration Park in Lean Village, Shangguan Town, Guanling County as the research area, the six-rotor UAV M600 mounted with the PikaxC2 sensor was used to obtain high-resolution hyperspectral image data. By analyzing the band standard deviation and correlation of hyperspectral images, 185 bands suitable for the analysis and research of pitaya fruit were selected preliminarily. Among them, 109 were visible bands and 76 were near-infrared bands. The distribution of correlation coefficients of bands was high in the middle and low on both sides. The mean values of correlation coefficients in visible light bands were greater than those within the near-infrared bands.

Key words: hyperspectral images, band, UAV, pitaya（Hylocereus undatus‘Foo-Lon’）

CLC Number:

TP751
S667.9

CHEN Zhi-hu, SHU Tian, LIU Chun-yan, XU Yuan-hong. Study on band screening of pitaya fruit based on unmanned aerial vehicle hyperspectral image[J]. HUBEI AGRICULTURAL SCIENCES, 2022, 61(22): 158-162.

References

[1] 张海艳,兰玉彬,文晟,等.植保无人机水稻田间农药喷施的作业效果[J].华南农业大学学报,2019,40(1):116-124.
[2] 赵莲英. 植保无人机喷施纳米农药防治水稻主要病虫的药效评[J].安徽农业科学,2020,48(14):144-146,155.
[3] 陶惠林,冯海宽,徐良骥,等.基于无人机高光谱遥感数据的冬小麦生物量估算[J].江苏农业学报,2020,36(5):1154-1162.
[4] 陈晓凯,李粉玲,王玉娜,等.无人机高光谱遥感估算冬小麦叶面积指数[J].农业工程学报,2020,36(22):40-49.
[5] 奚雪,赵庚星.基于无人机多光谱遥感的冬小麦叶绿素含量反演及监测[J].中国农学通报,2020,36(20):119-126.
[6] 牛鲁燕,蒋风伟,张俊丽,等.基于无人机多光谱遥感的小麦长势监测研究[J].山东农业科学,2021,53(3):139-145.
[7] 边琳,叶飞,王建雄.基于无人机低空多光谱遥感监测的烤烟株高研究[J].山东农业科学,2020,52(12):126-132.
[8] 田军仓,杨振峰,冯克鹏,等.基于无人机多光谱影像的番茄冠层SPAD预测研究[J].农业机械学报,2020,51(8):178-188.
[9] 杨丽丽,王振鹏,吴才聪.基于多源数据的新疆棉田螨害大范围监测研究[J].光谱学与光谱分析,2021,41(12):3949-3956.
[10] 王靖,彭漪,刘小娟,等.基于无人机多光谱数据的水稻LAI反演与应用[J].中国农业大学学报,2021,26(12):145-156.
[11] 杨圣慧,郑永军,刘星星,等.基于无人机近地面多光谱图像的蛇龙珠葡萄成熟度判别[J].光谱学与光谱分析,2021,41(10):3220-3226.
[12] 何诚,刘柯珍,舒立福,等.基于红外热成像的林地余火死灰复燃点诊断方法研究[J].光谱学与光谱分析,2018,38(1):326-332.
[13] 王娇娇,宋晓宇,梅新,等.基于高斯回归分析的水稻氮素敏感波段筛选及含量估算[J].光谱学与光谱分析,2021,41(6):1722-1729.
[14] WANG M,YAN Z,LUO J, et al.A band selection approach based on wavelet support vector machine ensemble model and membrane whale optimization algorithm for hyperspectral image[J]. Applied intelligence, 2021, 51(11): 7766-7780.
[15] LIU Y,LI X,FENG Y,et al.Representativeness and redundancy-based band selection for hyperspectral image classification[J]. International journal of remote sensing,2021,42(9): 3534-3562.
[16] SHI Y, ZHAO J, SONG X, et al.Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm[J]. PloS one, 2021,16(6): e0253385.
[17] 吴伟斌,刘文超,李泽艺,等.基于高光谱的茶叶含水量检测模型建立与试验研究[J].河南农业大学学报,2018,52(5):818-824.
[18] 吴叶兰,陈怡宇,廉小亲,等.高光谱成像的柑橘病虫害叶片识别方法[J].光谱学与光谱分析,2021,41(12):3837-3843.
[19] 解文武. 不同微生物作用下玉米叶片含水量高光谱定量估测[J].湖北农业科学,2018,57(24):151-155.
[20] 梁雪剑,张晔,张钧萍.高光谱图像相对含水量反演引导的精细分类[J].遥感学报,2021,25(11):2283-2302.
[21] 赵庆展,江萍,王学文,等.基于无人机高光谱遥感影像的防护林树种分类[J].农业机械学报,2021,52(11):190-199.
[22] 熊淑萍,丁绍强,郭建彪,等.基于高光谱遥感的小麦子粒谷氨酰胺合成酶活性估算研究[J].河南农业大学学报,2021,55(5):821-829.
[23] 赵庆展,刘伟,尹小君,等.基于无人机多光谱影像特征的最佳波段组合研究[J].农业机械学报,2016,47(3):242-248,291.
[24] 邓小玲,曾国亮,朱梓豪,等.基于无人机高光谱遥感的柑橘患病植株分类与特征波段提取[J].华南农业大学学报,2020, 41(6):100-108.
[25] 田奥,李苇洁,王加国,等.关岭县石漠化地区立地因子对白刺花群落特征的影响[J].西部林业科学,2021,50(3):34-41.
[26] 陈起伟,熊康宁,周梅,等.关岭县不同等级石漠化区土壤侵蚀特征[J].水土保持研究,2018,25(5):24-28.
[27] 姚永慧,索南东主,张俊瑶,等.2010—2015年贵州省关岭县石漠化时空演变及人类活动影响因素[J].地理科学进展,2019,38(11):1759-1769.