Weed detection using canopy reflection

被引：97

作者：

Vrindts E. ^{[1
]}

De Baerdemaeker J. ^{[1
]}

Ramon H. ^{[1
]}

机构：

[1] Dept. AgroEngineering and Economics, Lab. AgroMachinery and Processing, Katholieke Universiteit Leuven, Heverlee, B-3001, Kardinaal Mercierlaan 92, BLOK E

来源：

Precision Agriculture | 2002年 / 3卷 / 1期

关键词：

Canopy reflectance; Precision crop protection; Weed detection;

D O I：

10.1023/A:1013326304427

中图分类号：

学科分类号：

摘要：

For site-specific application of herbicides, automatic detection and evaluation of weeds is desirable. Since reflectance of crop, weeds and soil differs in the visual and near infrared wavelengths, there is potential for using reflection measurements at different wavelengths to distinguish between them. Reflectance spectra of crop and weed canopies were used to evaluate the possibilities of weed detection with reflection measurements in laboratory circumstances. Sugarbeet and maize and 7 weed species were included in the measurements. Classification into crop and weeds was possible in laboratory tests, using a limited number of wavelength band ratios. Crop and weed spectra could be separated with more than 97% correct classification. Field measurements of crop and weed reflection were conducted for testing spectral weed detection. Canopy reflection was measured with a line spectrograph in the wavelength range from 480 to 820 nm (visual to near infrared) with ambient light. The discriminant model uses a limited number of narrow wavelength bands. Over 90% of crop and weed spectra can be identified correctly, when the discriminant model is specific to the prevailing light conditions.

引用

页码：63 / 80

页数：17

共 29 条

[1]

Andersen H., Granum E., Classifying daylight conditions from colour histogram assessment, Presented at AgEng Oslo98, (1998)

[2]

Borregaard T., Nielsen H., Norgaard L., Have H., Crop-weed discrimination by line imaging spectroscopy, Journal of Agricultural Engineering Research, 75, pp. 389-400, (2000)

[3]

Burks T.F., Shearer S.A., Gates R.S., Donohue K.D., Backpropagation neural network design and evaluation for classifying weed species using color image texture, Transactions of the American Society of Agricultural Engineers, 43, 4, pp. 1029-1037, (2000)

[4]

Burks T.F., Shearer S.A., Payne F.A., Classification of weed species using color texture features and discriminant analysis, Transactions of the American Society of Agricultural Engineers, 43, 2, pp. 441-448, (2000)

[5]

Cousens R., Brain P., O'Donovan J.T., O'Sullivan A., The use of biologically realistic equations to describe the effects of weed density and relative time of emergence on crop yield, Weed Science, 35, pp. 720-725, (1987)

[6]

Donald W., Geostatistics for mapping weeds, with a Canada Thistle (Cirsium arvense) patch as a case study, Weed Science, 42, pp. 648-657, (1994)

[7]

El-Faki M.S., Zhang N., Peterson D.E., Weed detection using color machine vision, Transactions of the American Society of Agricultural Engineers, 43, 6, pp. 1969-1978, (2000)

[8]

El-Faki M.S., Zhang N., Peterson D.E., Factors affecting color-based weed detection, Transactions of the American Society of Agricultural Engineers, 43, 4, pp. 1001-1009, (2000)

[9]

Favier J.F., Ross D.W., Tsheko R., Kennedy D.D., Muir A.Y., Fleming J., Discrimination of weeds in brassica crops using optical spectral reflectance and leaf texture analysis, Proceedings of SPIE 3543 - Precision Agriculture and Biological Quality, pp. 311-318, (1999)

[10]

Feyaerts F., Pollet P., Wambacq P., Van Gool L., Sensor for weed detection based on spectral measurements, Proceedings of the 4th International Conference on Precision Agriculture, pp. 1537-1548, (1998)

← 1 2 3 →