Estimation of Cotton Yield Based on Unmanned Aerial Vehicle RGB Images

doi:10.13304/j.nykjdb.2021.0918

Abstract

Abstract:

At present， unmanned aerial vehicle （UAV） system has been applied to crop yield estimation， the RGB camera carried by UAV was used to collect cotton canopy images from 3 heights （10， 20 and 30 m） at both flowering and boll development stage and boll-opening stage. The color and texture features were extracted from the images and integrated， and then the stepwise regression analysis and factor analysis were respectively performed to screen the most important features and establish cotton yield estimation models. By comparing the yield estimation models at different height and growth stages， the most appropriate growth stage and image acquisition height were determined. The results showed that the fitting degrees and precisions of the yield model at the flowering and boll development stage were better than those at the boll-opening stage at 20 and 30 m. However， the fitting degrees of models at two growth periods were relatively close at the height of 40 m， but the validation of the model was not significant at the boll-opening stage. By comparing the yield estimation models at the flowering and boll development stage at 20 and 30 m and the boll-opening stage at 40 m， it was found that the model established at 30 m at the flowering and boll stage by the stepwise regression analysis had the best fitting effect. Thus， it indicated that the flowering and boll development stage was the optimal growth period for cotton yield estimation， and 30 m was the best height for image collection. In summary， this paper showed that UAV RGB image could estimate cotton yield accurately and quickly， which provided theoretical and technical foundation for the estimation of cotton yield based on visible image， and provided reference for other crops to establish the yield estimation model.

Key words: UAV, image feature, stepwise regression analysis, factor analysis, cotton yield model

摘要：

目前，无人机系统已应用于作物产量估算，利用无人机搭载的RGB相机在花铃期和吐絮期从3个高度（10、20和30 m）分别采集棉花冠层图像，提取图像的颜色指数和纹理特征，进而对提取的特征分别进行逐步回归分析和因子分析，筛选出重要特征并构建棉花产量估算模型。通过对比分析2个生育时期和3个高度的产量估算模型，最终确定利用RGB图像对棉花进行产量估算的最佳生育时期和最佳高度。结果表明， 20 和30 m高度下花铃期图像建立的产量模型拟合度以及模型精度均比吐絮期好，而40 m高度下2个生育时期的模型拟合度接近，但花铃期的验证结果不显著；对比20和30 m高度下花铃期以及40 m高度下吐絮期的产量估算模型发现，30 m高度下花铃期通过SWR方法建立的模型拟合效果最佳，由此表明，棉花产量估算的最佳生育时期为花铃期，图像采集的最佳高度为30 m。综上，利用无人机RGB图像能准确快速估算棉花产量，为基于可见光图像的棉花产量估算提供了理论和技术参考，并为其他农作物估产模型的建立提供借鉴。

关键词: 无人机, 图像特征, 逐步回归, 因子分析, 棉花产量模型

CLC Number:

S126

Jing ZHANG, Simeng GUO, Yingchun HAN, Yaping LEI, Fangfang XING, Wenli DU, Yabing LI, Lu FENG. Estimation of Cotton Yield Based on Unmanned Aerial Vehicle RGB Images[J]. Journal of Agricultural Science and Technology, 2022, 24(11): 112-120.

张静, 郭思梦, 韩迎春, 雷亚平, 邢芳芳, 杜文丽, 李亚兵, 冯璐. 基于无人机RGB图像的棉花产量估算[J]. 中国农业科技导报, 2022, 24(11): 112-120.

Figures/Tables 6

References 39

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生育时期 Growth stage	因子Factor	20 m	30 m	40 m
花铃期 Flowering and boll stage	FA₁	-271.4	235.7	-180.7
	FA₂	-139.2	-197.1	-39.1
	FA₃	27.9	-69.2	158.8
吐絮期 Boll-opening stage	FA₁	224.8	204.5	160.7
	FA₂	-112.3	-110.0	63.5
	FA₃	104.3	100.5	165.2

生育时期 Growth stage	因子Factor	20 m	30 m	40 m
花铃期 Flowering and boll stage	FA₁	-271.4	235.7	-180.7
	FA₂	-139.2	-197.1	-39.1
	FA₃	27.9	-69.2	158.8
吐絮期 Boll-opening stage	FA₁	224.8	204.5	160.7
	FA₂	-112.3	-110.0	63.5
	FA₃	104.3	100.5	165.2

高度 Height/m	生育时期 Growth stage	分析方法 Analysis method	拟合模型 Fitting model	决定系数R²	RMSE/（kg·hm^-2）	NRMSE/%
20	花铃期 Flowering and boll stage	逐步回归SWR	Y=-3 598W+0.886 8D-1 250	0.733 3	184.4	5.71
	花铃期 Flowering and boll stage	因子分析FN	Y=-271.4FA₁-139.2FA₂+27.89FA₃+3 228	0.735 8	183.5	5.68
	吐絮期 Boll-opening stage	逐步回归SWR	Y=18 792I+1.952D+2 260	0.606 5	224.0	6.94
	吐絮期 Boll-opening stage	因子分析FN	Y=224.8FA₁-112.3FA₂+104.3FA₃+3 252	0.592 6	230.6	7.14
30	花铃期 Flowering and boll stage	逐步回归SWR	Y=-3 085W-3 102E+15 016	0.784 9	165.6	5.13
	花铃期 Flowering and boll stage	因子分析FN	Y=235.2FA₁-134.1FA₂-152.6FA₃+3 228	0.757 4	175.9	5.45
	吐絮期 Boll-opening stage	逐步回归SWR	Y=-296 8W+1 030	0.415 9	272.9	8.45
	吐絮期 Boll-opening stage	FN	Y=204.5FA₁-110FA₂+100.5FA₃	0.502 0	252.0	7.81
40	花铃期 Flowering and boll stage	逐步回归SWR	Y=-238 87M+11 146	0.435 5	268.3	8.31
	花铃期 Flowering and boll stage	因子分析FN	Y=-180.7FA₁-39.06FA₂+158.8FA₃+3 228	0.465 6	261.0	8.09
	吐絮期 Boll-opening stage	逐步回归SWR	Y=-2 369C+19 032	0.506 0	251.0	7.78
	吐絮期 Boll-opening stage	因子分析FN	Y=160.7FA₁+63.53FA₂+165.2FA₃	0.448 1	265.3	8.22

高度 Height/m	生育时期 Growth stage	分析方法 Analysis method	拟合模型 Fitting model	决定系数R²	RMSE/（kg·hm^-2）	NRMSE/%
20	花铃期 Flowering and boll stage	逐步回归SWR	Y=-3 598W+0.886 8D-1 250	0.733 3	184.4	5.71
	花铃期 Flowering and boll stage	因子分析FN	Y=-271.4FA₁-139.2FA₂+27.89FA₃+3 228	0.735 8	183.5	5.68
	吐絮期 Boll-opening stage	逐步回归SWR	Y=18 792I+1.952D+2 260	0.606 5	224.0	6.94
	吐絮期 Boll-opening stage	因子分析FN	Y=224.8FA₁-112.3FA₂+104.3FA₃+3 252	0.592 6	230.6	7.14
30	花铃期 Flowering and boll stage	逐步回归SWR	Y=-3 085W-3 102E+15 016	0.784 9	165.6	5.13
	花铃期 Flowering and boll stage	因子分析FN	Y=235.2FA₁-134.1FA₂-152.6FA₃+3 228	0.757 4	175.9	5.45
	吐絮期 Boll-opening stage	逐步回归SWR	Y=-296 8W+1 030	0.415 9	272.9	8.45
	吐絮期 Boll-opening stage	FN	Y=204.5FA₁-110FA₂+100.5FA₃	0.502 0	252.0	7.81
40	花铃期 Flowering and boll stage	逐步回归SWR	Y=-238 87M+11 146	0.435 5	268.3	8.31
	花铃期 Flowering and boll stage	因子分析FN	Y=-180.7FA₁-39.06FA₂+158.8FA₃+3 228	0.465 6	261.0	8.09
	吐絮期 Boll-opening stage	逐步回归SWR	Y=-2 369C+19 032	0.506 0	251.0	7.78
	吐絮期 Boll-opening stage	因子分析FN	Y=160.7FA₁+63.53FA₂+165.2FA₃	0.448 1	265.3	8.22

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