Evaluation of damage degree of Chilo suppressalis on early rice based on unmanned aerial vehicle multispectral remote sensing

doi:10.3969/j.issn.1004-1524.20240107

Acta Agriculturae Zhejiangensis ›› 2025, Vol. 37 ›› Issue (3): 603-611.DOI: 10.3969/j.issn.1004-1524.20240107

• Plant Protection • Previous Articles Next Articles

Evaluation of damage degree of Chilo suppressalis on early rice based on unmanned aerial vehicle multispectral remote sensing

CAO Mengjiao¹(), BAI Shi², TANG Panpan², WANG Yeqing¹, XU Hongxing³^,^*(), ZHOU Guoxin⁴

1. Jiaxing Soil Fertilizer, Plant Protection and Rural Energy Station, Jiaxing 314100, Zhejiang, China
2. Big Data Technology Research Center, Nanhu Laboratory, Jiaxing 314100, Zhejiang, China
3. Institute of Plant Protection and Microbiology, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
4. College of Advanced Agricultural Sciences, Zhejiang A&F University, Hangzhou 311300, China

Received:2024-01-25 Online:2025-03-25 Published:2025-04-02

Abstract

Abstract:

To clarify the applicability of unmanned aerial vehicle (UAV) multispectral remote sensing in the assessment of the damage severity caused by Chilo suppressalis on early rice, diversified damage ratios were artificially created by spraying different pesticides with varying frequencies. The reflectance of 6 bands, namely, b1 (450 nm), b2 (555 nm), b3 (660 nm), b4 (720 nm), b5 (750 nm) and b6 (840 nm) was acquired by utilizing UAV multispectral image, and the crop growth and environmental information were obtained in the field. Six machine learning models, including linear regression, support vector machine, random forest, ridge regression, Lasso regression (least absolute shrinkage and selection operator regression) and Bayesian regression, were employed to establish relationships between the multispectral data acquired during three periods and the determined damage ratio of Chilo suppressalis. It was shown that the support vector machine with two-phase (heading stage and wax ripening stage) data could better reflect the real damage of Chilo suppressalis on early rice, and the predicted result was relatively consistent with the real situation in the field. This study preliminarily proved that the support vector machine with two-phase data obtained by UAV multispectral remote sensing could be used to evaluate the damage degree of Chilo suppressalis on early rice, which could provide theoretical basis and reference for the development of intelligent plant protection.

Key words: Chilo suppressalis, damage ratio, multispectral, modeling

CLC Number:

S431.9

CAO Mengjiao, BAI Shi, TANG Panpan, WANG Yeqing, XU Hongxing, ZHOU Guoxin. Evaluation of damage degree of Chilo suppressalis on early rice based on unmanned aerial vehicle multispectral remote sensing[J]. Acta Agriculturae Zhejiangensis, 2025, 37(3): 603-611.

Figures/Tables 6

References 24

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编号 No.	为害率 Damage ratio/%	编号 No.	为害率 Damage ratio/%	编号 No.	为害率 Damage ratio/%	编号 No.	为害率 Damage ratio/%	编号 No.	为害率 Damage ratio/%
1	1.39	2	1.67	3	0.69	4	2.36	5	3.16
6	2.22	7	2.78	8	2.78	9	3.19	10	7.92
11	2.08	12	3.47	13	3.61	14	3.75	15	6.81
16	28.33	17	45.14	18	3.19	19	2.78	20	7.36
21	3.61	22	16.53	23	1.81	24	11.53	25	2.92
26	1.25	27	2.08	28	1.67	29	1.11	30	1.94
31	1.81	32	4.03	33	9.44	34	2.22	35	3.06
36	11.67	37	3.33	38	3.33	39	1.25	40	8.33
41	2.78	42	4.03	43	12.22	44	2.36	45	4.03
46	41.60	47	28.30	48	24.60	49	4.60	50	29.60
51	7.60	52	11.50	53	12.50	54	15.70	55	19.70
56	30.20	57	31.60	58	23.90	59	20.00

编号 No.	为害率 Damage ratio/%	编号 No.	为害率 Damage ratio/%	编号 No.	为害率 Damage ratio/%	编号 No.	为害率 Damage ratio/%	编号 No.	为害率 Damage ratio/%
1	1.39	2	1.67	3	0.69	4	2.36	5	3.16
6	2.22	7	2.78	8	2.78	9	3.19	10	7.92
11	2.08	12	3.47	13	3.61	14	3.75	15	6.81
16	28.33	17	45.14	18	3.19	19	2.78	20	7.36
21	3.61	22	16.53	23	1.81	24	11.53	25	2.92
26	1.25	27	2.08	28	1.67	29	1.11	30	1.94
31	1.81	32	4.03	33	9.44	34	2.22	35	3.06
36	11.67	37	3.33	38	3.33	39	1.25	40	8.33
41	2.78	42	4.03	43	12.22	44	2.36	45	4.03
46	41.60	47	28.30	48	24.60	49	4.60	50	29.60
51	7.60	52	11.50	53	12.50	54	15.70	55	19.70
56	30.20	57	31.60	58	23.90	59	20.00

数据 Data	不同时期的相关系数 Correlation coefficients of different periods
数据 Data	2023-07-05	2023-07-11	2023-07-31
b1	0.19	0.02	0.16
b2	0.10	0.05	0.17
b3	0.06	0.08	0.15
b4	0.06	0.09	0.18
b5	0.01	0.04	0.18
b6	0.09	0.16	0.19
NDVI	0.04	0.17	0.24

数据 Data	不同时期的相关系数 Correlation coefficients of different periods
数据 Data	2023-07-05	2023-07-11	2023-07-31
b1	0.19	0.02	0.16
b2	0.10	0.05	0.17
b3	0.06	0.08	0.15
b4	0.06	0.09	0.18
b5	0.01	0.04	0.18
b6	0.09	0.16	0.19
NDVI	0.04	0.17	0.24

回归方法 Regression method	基于三时相数据的相关系数 Correlation coefficient obtained by triple temporal phases data		基于双时相数据的相关系数 Correlation coefficient obtained by double temporal phases data		基于单时相数据的相关系数 Correlation coefficient obtained by single temporal phase data
回归方法 Regression method	训练集 Training set	验证集 Verification set	训练集 Training set	验证集 Verification set	训练集 Training set	验证集 Verification set
线性回归Linear regression	0.88	0.45	0.81	0.45	0.65	0.31
支持向量机Support vector machine	0.70	0.85	0.92	0.94	0.82	0.87
随机森林Random forest	0.92	0.56	0.92	0.42	0.90	0.58
岭回归Ridge regression	0.73	0.60	0.61	0.66	0.72	0.66
Lasso回归	0.40	0.56	0.65	0.66	0.61	0.66
least absolute shrinkage and selection operator (Lasso) regression
贝叶斯回归Bayesian regression	0.72	0.45	0.68	0.48	0.74	0.45

Evaluation of damage degree of Chilo suppressalis on early rice based on unmanned aerial vehicle multispectral remote sensing

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