Automatic counting of maize plants based on unmanned aerial vehicle (UAV) 3D point cloud

doi:10.3969/j.issn.1004-1524.2022.09.22

Acta Agriculturae Zhejiangensis ›› 2022, Vol. 34 ›› Issue (9): 2032-2042.DOI: 10.3969/j.issn.1004-1524.2022.09.22

• Biosystems Engineening • Previous Articles Next Articles

Automatic counting of maize plants based on unmanned aerial vehicle (UAV) 3D point cloud

JIANG Youyi¹(), ZHANG Chengjian¹^,²^,³, HAN Shaoyu²^,³, YANG Xiaodong²^,³, YANG Guijun²^,³, YANG Hao²^,³^,^*()

1. College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China
2. Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
3. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China

Received:2021-03-24 Online:2022-09-25 Published:2022-09-30
Contact: YANG Hao

Abstract

Abstract:

Plant counting is one of the most commonly used methods for farmers and breeding experts to evaluate crop growth status and management practices throughout the crop growing season, which can be used for reasonable field planning and management. In view of the lack of high-throughput methods to obtain the number of maize plants in the high-density planting experimental area, this study used the unmanned aerial vehicle (UAV) remote sensing platform to obtain the digital images and light detection and ranging (LiDAR) point cloud data of 314 maize high-density breeding plots with different genotypes in the field, and developed a fixed window local maximum algorithm combined with maize three-dimensional spatial information. The automatic detection of the number of plants in high density maize breeding plot was realized, and the detection accuracy based on the two different data sources was compared. Based on the plant height information in the crop height model (CHM), the method detected the seed points of each maize plant by using local maximum filter with a fixed window size, and then we spatially matched the detected seed points with the position of maize interpreted visually to evaluate the accuracy. The results showed that the comprehensive detection accuracy of three spatial resolutions CHM based on UAV digital images was 81.30%, 83.11% and 78.93% respectively, and the comprehensive accuracy of UAV-LiDAR was 82.33%, 88.66% and 81.46% respectively. The CHM, based on the two different data sources had the best detection accuracy when the spatial resolution was 0.05 m. In addition, when the spatial resolution was the same, LiDAR performsed better than UAV digital images. But when the demand for detection accuracy was not high, the digital sensor showed greater potential in field management because of its cheap price and easy to operate. This study realized the automatic counting of maize plant number in dense planting maize breeding experimental area, which provided a basis for phenotypic screening, field management and accurate yield estimation.

Key words: automatic counting, unmanned aerial vehicle, high-throughput, local maximum, canopy height model, field breeding

CLC Number:

S252

JIANG Youyi, ZHANG Chengjian, HAN Shaoyu, YANG Xiaodong, YANG Guijun, YANG Hao. Automatic counting of maize plants based on unmanned aerial vehicle (UAV) 3D point cloud[J]. Acta Agriculturae Zhejiangensis, 2022, 34(9): 2032-2042.

Figures/Tables 11

References 27

[1]	PANG Y, SHI Y Y, GAO S C, et al. Improved crop row detection with deep neural network for early-season maize stand count in UAV imagery[J]. Computers and Electronics in Agriculture, 2020, 178: 105766. DOI URL
[2]	CHEN R Z, CHU T X, LANDIVAR J A, et al. Monitoring cotton (Gossypium hirsutum L.) germination using ultrahigh-resolution UAS images[J]. Precision Agriculture, 2018, 19(1): 161-177. DOI URL
[3]	GNÄDINGER F, SCHMIDHALTER U. Digital counts of maize plants by unmanned aerial vehicles (UAVs)[J]. Remote Sensing, 2017, 9(6): 544. DOI URL
[4]	VARELA S, DHODDA P, HSU W, et al. Early-season stand count determination in corn via integration of imagery from unmanned aerial systems (UAS) and supervised learning techniques[J]. Remote Sensing, 2018, 10(3): 343. DOI URL
[5]	SHIRZADIFAR A, MAHARLOOEI M, BAJWA S G, et al. Mapping crop stand count and planting uniformity using high resolution imagery in a maize crop[J]. Biosystems Engineering, 2020, 200: 377-390. DOI URL
[6]	SHUAI G Y, MARTINEZ-FERIA R A, ZHANG J S, et al. Capturing maize stand heterogeneity across yield-stability zones using unmanned aerial vehicles (UAV)[J]. Sensors, 2019, 19(20): 4446. DOI URL
[7]	POSTMA J A, HECHT V L, HIKOSAKA K, et al. Dividing the pie: a quantitative review on plant density responses[J]. Plant, Cell & Environment, 2021, 44(4): 1072-1094.
[8]	明博, 谢瑞芝, 侯鹏, 等. 2005—2016年中国玉米种植密度变化分析[J]. 中国农业科学, 2017, 50(11): 1960-1972.
	MING B, XIE R Z, HOU P, et al. Changes of maize planting density in China[J]. Scientia Agricultura Sinica, 2017, 50(11): 1960-1972. (in Chinese with English abstract)
[9]	ZOU H W, LU H, LI Y N, et al. Maize tassels detection: a benchmark of the state of the art[J]. Plant Methods, 2020, 16: 108. DOI PMID
[10]	ZHAO B Q, ZHANG J, YANG C H, et al. Rapeseed seedling stand counting and seeding performance evaluation at two early growth stages based on unmanned aerial vehicle imagery[J]. Frontiers in Plant Science, 2018, 9: 1362. DOI PMID
[11]	ZHAO H X, WANG Y X, SUN Z B, et al. Failure detection in Eucalyptus plantation based on UAV images[J]. Forests, 2021, 12(9): 1250. DOI URL
[12]	MATIAS F I, CARAZA-HARTER M V, ENDELMAN J B. FIELDimageR: an R package to analyze orthomosaic images from agricultural field trials[J]. The Plant Phenome Journal, 2020, 3(1): e20005.
[13]	JIN X L, LIU S Y, BARET F, et al. Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery[J]. Remote Sensing of Environment, 2017, 198: 105-114. DOI URL
[14]	ZHANG J, ZHAO B Q, YANG C H, et al. Rapeseed stand count estimation at leaf development stages with UAV imagery and convolutional neural networks[J]. Frontiers in Plant Science, 2020, 11: 617. DOI PMID
[15]	郑晓岚, 张显峰, 程俊毅, 等. 利用无人机多光谱影像数据构建棉苗株数估算模型[J]. 中国图象图形学报, 2020, 25(3): 520-534.
	ZHENG X L, ZHANG X F, CHENG J Y, et al. Using the multispectral image data acquired by unmanned aerial vehicle to build an estimation model of the number of seedling stage cotton plants[J]. Journal of Image and Graphics, 2020, 25(3): 520-534. (in Chinese with English abstract)
[16]	赵必权, 丁幼春, 蔡晓斌, 等. 基于低空无人机遥感技术的油菜机械直播苗期株数识别[J]. 农业工程学报, 2017, 33(19): 115-123.
	ZHAO B Q, DING Y C, CAI X B, et al. Seedlings number identification of rape planter based on low altitude unmanned aerial vehicles remote sensing technology[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(19): 115-123. (in Chinese with English abstract)
[17]	PICOS J, BASTOS G, MÍGUEZ D, et al. Individual tree detection in a Eucalyptus plantation using unmanned aerial vehicle (UAV)-LiDAR[J]. Remote Sensing, 2020, 12(5): 885. DOI URL
[18]	YIN D M, WANG L. Individual mangrove tree measurement using UAV-based LiDAR data: possibilities and challenges[J]. Remote Sensing of Environment, 2019, 223: 34-49. DOI URL
[19]	李丹, 张俊杰, 赵梦溪. 基于FCM和分水岭算法的无人机影像中林分因子提取[J]. 林业科学, 2019, 55(5): 180-187.
	LI D, ZHANG J J, ZHAO M X. Extraction of stand factors in UAV image based on FCM and watershed algorithm[J]. Scientia Silvae Sinicae, 2019, 55(5): 180-187. (in Chinese with English abstract)
[20]	胡馨月, 倪海明, 戚大伟. 基于无人机影像的树木株数提取[J]. 森林工程, 2021, 37(1): 6-12.
	HU X Y, NI H M, QI D W. Tree counts extraction based on UAV imagery[J]. Forest Engineering, 2021, 37(1): 6-12. (in Chinese with English abstract)
[21]	YIN T, ZENG J, ZHANG X L, et al. Individual tree parameters estimation for Chinese fir (Cunninghamia lanceolate(Lamb.) Hook) plantations of South China using UAV oblique photography: possibilities and challenges[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 14: 827-842. DOI URL
[22]	李平昊, 申鑫, 代劲松, 等. 机载激光雷达人工林单木分割方法比较和精度分析[J]. 林业科学, 2018, 54(12): 127-136.
	LI P H, SHEN X, DAI J S, et al. Comparisons and accuracy assessments of LiDAR-based tree segmentation approaches in planted forests[J]. Scientia Silvae Sinicae, 2018, 54(12): 127-136. (in Chinese with English abstract)
[23]	ZHANG W M, QI J B, WAN P, et al. An easy-to-use airborne LiDAR data filtering method based on cloth simulation[J]. Remote Sensing, 2016, 8(6): 501. DOI URL
[24]	刘晓双, 黄建文, 鞠洪波. 高空间分辨率遥感的单木树冠自动提取方法与应用[J]. 浙江林学院学报, 2010, 27(1): 126-133.
	LIU X S, HUANG J W, JU H B. Research progress in the methods and applications of individual tree crown’s automatic extraction by high spatial resolution remote sensing[J]. Journal of Zhejiang Forestry College, 2010, 27(1): 126-133. (in Chinese with English abstract)
[25]	POULIOT D A, KING D J, BELL F W, et al. Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration[J]. Remote Sensing of Environment, 2002, 82(2/3): 322-334. DOI URL
[26]	WALSWORTH N A, KING D J. Image modelling of forest changes associated with acid mine drainage[J]. Computers & Geosciences, 1999, 25(5): 567-580. DOI URL
[27]	CULVENOR D S. TIDA: an algorithm for the delineation of tree crowns in high spatial resolution remotely sensed imagery[J]. Computers & Geosciences, 2002, 28(1): 33-44. DOI URL

项目Item	无人机数码UAV digital	无人机LiDAR UAV-LiDAR
无人机型号UAV model	DJI精灵4 Pro DJI Phantom 4 Pro	DJI M600
传感器及产地 Sensor and origin	1英寸CMOS/中国 1 inch CMOS/China	Riegl VUX-1/奥地利 Riegl VUX-1/Austria
像素Pixel	2×10⁷	-
测量频率/激光发射频率 Measurement frequency/laser emission frequency	前/后/下视:10/10/20 Hz Front/back/bottom view: 10/10/20 Hz	550 kHz
拍摄模式/扫描方式 Shooting mode/scanning mode	单张拍摄/多张连拍 Single shot/multiple consecutive shots	摆锤式 Pendulum type
快门速度/扫描速度Shutter speed/scanning speed	8-1/2000 s	220 Scan/s
照片尺寸/激光光斑直径Photo size/laser spot diameter	3∶2/4∶3/16∶9	0.007 5 m
是否可拆卸Detachable or not	否Not	是Yes
价格Price	9 999 yuan	1×10⁶ yuan

项目Item	无人机数码UAV digital	无人机LiDAR UAV-LiDAR
无人机型号UAV model	DJI精灵4 Pro DJI Phantom 4 Pro	DJI M600
传感器及产地 Sensor and origin	1英寸CMOS/中国 1 inch CMOS/China	Riegl VUX-1/奥地利 Riegl VUX-1/Austria
像素Pixel	2×10⁷	-
测量频率/激光发射频率 Measurement frequency/laser emission frequency	前/后/下视:10/10/20 Hz Front/back/bottom view: 10/10/20 Hz	550 kHz
拍摄模式/扫描方式 Shooting mode/scanning mode	单张拍摄/多张连拍 Single shot/multiple consecutive shots	摆锤式 Pendulum type
快门速度/扫描速度Shutter speed/scanning speed	8-1/2000 s	220 Scan/s
照片尺寸/激光光斑直径Photo size/laser spot diameter	3∶2/4∶3/16∶9	0.007 5 m
是否可拆卸Detachable or not	否Not	是Yes
价格Price	9 999 yuan	1×10⁶ yuan

指标 Measurement index	指标具体含义 Specific meaning of index
TP	被正确检测的玉米 Maize that has been correctly detected
FP	非玉米或部分玉米植株被视做整株检测出 Non-maize or part of maize plants are considered to be detected as whole plants
FN	未被检测到的玉米Undetected maize

指标 Measurement index	指标具体含义 Specific meaning of index
TP	被正确检测的玉米 Maize that has been correctly detected
FP	非玉米或部分玉米植株被视做整株检测出 Non-maize or part of maize plants are considered to be detected as whole plants
FN	未被检测到的玉米Undetected maize

样点 Sampling point	0.03 m			0.05 m			0.10 m
样点 Sampling point	正检 Correct detection	漏检 Missed detection	错检 Error detection	正检 Correct detection	漏检 Missed detection	错检 Error detection	正检 Correct detection	漏检 Missed detection	错检 Error detection
A	117	30	10	111	33	13	114	30	13
B	121	37	17	136	25	14	105	65	5
C	118	53	6	127	41	9	111	57	9
D	129	63	2	132	59	3	127	59	8
E	117	52	7	119	52	5	116	52	8
总计Total	602	235	42	625	210	44	573	263	43

Automatic counting of maize plants based on unmanned aerial vehicle (UAV) 3D point cloud

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分辨率 Resolution/m	R_RGB	P_RGB	F_RGB
0.03	71.92	93.48	81.30
0.05	74.85	93.49	83.11
0.10	68.54	93.02	78.93

分辨率 Resolution/m	R_RGB	P_RGB	F_RGB
0.03	71.92	93.48	81.30
0.05	74.85	93.49	83.11
0.10	68.54	93.02	78.93

分辨率 Resolution/m	R_LiDAR	P_LiDAR	F_LiDAR
0.03	77.46	87.86	82.33
0.05	82.94	95.24	88.66
0.10	73.48	91.38	81.46

分辨率 Resolution/m	R_LiDAR	P_LiDAR	F_LiDAR
0.03	77.46	87.86	82.33
0.05	82.94	95.24	88.66
0.10	73.48	91.38	81.46