High Efficient Grapevine Growth Monitor and In-lane Deficiency Localization by UAV Hyperspectral Remote Sensing

doi:10.16420/j.issn.0513-353x.2021-0441

Acta Horticulturae Sinica ›› 2021, Vol. 48 ›› Issue (8): 1626-1634.doi: 10.16420/j.issn.0513-353x.2021-0441

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High Efficient Grapevine Growth Monitor and In-lane Deficiency Localization by UAV Hyperspectral Remote Sensing

WANG Haomiao¹, SONG Miaoyu¹, LI Xiang², HU Chaoyang¹, LU Renxiang¹, WANG Xiang³, MA Huiqin¹^,^*()

¹College of Horticulture,China Agricultural University,Beijing 100193,China
²Zhongshan Hankun Intelligent Technology Co.,Ltd,Beijing 100193,China
³Huailai Amethyst Manor,Huailai,Hebei 075400,China

Received:2021-05-06 Revised:2021-08-11 Online:2021-08-25 Published:2021-09-06
Contact: MA Huiqin E-mail:hqma@cau.edu.cn

Abstract

Abstract:

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.

Key words: grape, vegetative vigor, UAV remote sensing, NDVI, in-lane deficiency localization, precise agriculture

CLC Number:

S663.1

WANG Haomiao, SONG Miaoyu, LI Xiang, HU Chaoyang, LU Renxiang, WANG Xiang, MA Huiqin. High Efficient Grapevine Growth Monitor and In-lane Deficiency Localization by UAV Hyperspectral Remote Sensing[J]. Acta Horticulturae Sinica, 2021, 48(8): 1626-1634.

Figures/Tables 6

References 32

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品种 Variety	地块编号 Plot code	定植年份 Planting year	最小值 Min	最大值 Max	平均值 Average	标准差 Standard deviation
赤霞珠Cabernet Sauvignon	1	2008	0.351	0.912	0.733	0.098
赤霞珠Cabernet Sauvignon	5	2008	0.426	0.851	0.766	0.109
马瑟兰Marselan	2	2010	0.414	0.993	0.765	0.109
马瑟兰Marselan	4	2013	0.422	0.898	0.741	0.100
品丽珠Cabernet Franc	3	2008	0.564	0.892	0.727	0.089
品丽珠Cabernet Franc	6	2008	0.401	0.863	0.748	0.088
霞多丽Chardonnay	7	2009	0.462	0.932	0.743	0.091

品种 Variety	地块编号 Plot code	定植年份 Planting year	最小值 Min	最大值 Max	平均值 Average	标准差 Standard deviation
赤霞珠Cabernet Sauvignon	1	2008	0.351	0.912	0.733	0.098
赤霞珠Cabernet Sauvignon	5	2008	0.426	0.851	0.766	0.109
马瑟兰Marselan	2	2010	0.414	0.993	0.765	0.109
马瑟兰Marselan	4	2013	0.422	0.898	0.741	0.100
品丽珠Cabernet Franc	3	2008	0.564	0.892	0.727	0.089
品丽珠Cabernet Franc	6	2008	0.401	0.863	0.748	0.088
霞多丽Chardonnay	7	2009	0.462	0.932	0.743	0.091

品种 Variety	地块编号 Plot code	最小值 Min	最大值 Max	平均值 Average	标准差 Standard deviation
赤霞珠Cabernet Sauvignon	1	0.078（-0.273）	0.751（-0.161）	0.435（-0.298）	0.140（0.042）
赤霞珠Cabernet Sauvignon	5	0.052（-0.374）	0.731（-0.120）	0.431（-0.335）	0.139（0.030）
马瑟兰Marselan	2	0.081（-0.333）	0.753（-0.240）	0.453（-0.312）	0.134（0.025）
马瑟兰Marselan	4	0.061（-0.361）	0.692（-0.206）	0.415（-0.326）	0.133（0.033）
品丽珠Cabernet Franc	3	0.033（-0.531）	0.710（-0.182）	0.505（-0.177）	0.139（0.050）
品丽珠Cabernet Franc	6	0.055（-0.346）	0.703（-0.160）	0.414（-0.344）	0.132（0.044）
霞多丽Chardonnay	7	0.075（-0.387）	0.671（-0.261）	0.405（-0.338）	0.131（0.040）

品种 Variety	地块编号 Plot code	最小值 Min	最大值 Max	平均值 Average	标准差 Standard deviation
赤霞珠Cabernet Sauvignon	1	0.078（-0.273）	0.751（-0.161）	0.435（-0.298）	0.140（0.042）
赤霞珠Cabernet Sauvignon	5	0.052（-0.374）	0.731（-0.120）	0.431（-0.335）	0.139（0.030）
马瑟兰Marselan	2	0.081（-0.333）	0.753（-0.240）	0.453（-0.312）	0.134（0.025）
马瑟兰Marselan	4	0.061（-0.361）	0.692（-0.206）	0.415（-0.326）	0.133（0.033）
品丽珠Cabernet Franc	3	0.033（-0.531）	0.710（-0.182）	0.505（-0.177）	0.139（0.050）
品丽珠Cabernet Franc	6	0.055（-0.346）	0.703（-0.160）	0.414（-0.344）	0.132（0.044）
霞多丽Chardonnay	7	0.075（-0.387）	0.671（-0.261）	0.405（-0.338）	0.131（0.040）

子区 Block	均值 Average	方差 Variance	葡萄长势级别 Vegetative vigor
CS2008-1-1	0.733	0.099	良好Medium
CS2008-1-2	0.746	0.091	良好Medium
CS2008-1-3	0.751	0.088	良好Medium
CS2008-1-4	0.743	0.096	良好Medium
CS2008-1-5	0.739	0.090	良好Medium
CS2008-1-6	0.730	0.092	良好Medium
CS2008-5-1	0.656	0.112	良好Medium
CS2008-5-2	0.550	0.120	良好Medium
CS2008-5-3	0.626	0.079	良好Medium
CS2008-5-4	0.492	0.089	一般Weak
CS2008-5-5	0.741	0.088	良好Medium
CS2008-5-6	0.762	0.076	优良Vigorous

High Efficient Grapevine Growth Monitor and In-lane Deficiency Localization by UAV Hyperspectral Remote Sensing

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行数 Line	NDVI	行数 Line	NDVI	行数 Line	NDVI	行数 Line	NDVI	行数 Line	NDVI	行数 Line	NDVI	行数 Line	NDVI	行数 Line	NDVI
1	0.660	11	0.625	21	0.604	31	0.599	41	0.922	51	0.652	61	0.543	71	0.612
2	0.573	12	0.631	22	0.670	32	0.726	42	0.846	52	0.581	62	0.708	72	0.757
3	0.864	13	0.803	23	0.817	33	0.764	43	0.961	53	0.630	63	0.641	73	0.662
4	0.954	14	0.729	24	0.709	34	0.864	44	0.845	54	0.643	64	0.607	74	0.689
5	0.856	15	0.663	25	0.870	35	0.949	45	0.841	55	0.597	65	0.598	75	0.633
6	0.829	16	0.662	26	0.870	36	0.912	46	0.862	56	0.692	66	0.593	76	0.811
7	0.641	17	0.657	27	0.750	37	0.685	47	0.670	57	0.583	67	0.682	77	0.756
8	0.654	18	0.777	28	0.750	38	0.650	48	0.708	58	0.503	68	0.810	78	0.709
9	0.685	19	0.817	29	0.811	39	0.915	49	0.587	59	0.502	69	0.656	79	0.668
10	0.865	20	0.852	30	0.674	40	0.787	50	0.825	60	0.509	70	0.655	80	0.661

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