Full Length Research Paper
References
Andújar D, Àngela R, Fernàndez-Quintanilla C, Dorado J (2011). Accuracy and feasibility of optoelectronic sensors for weed mapping in wide row crops. Sensors 11:2304-2318. |
|
Andújar D, Weis M, Gerhards R (2012). An ultrasonic system for weed detection in cereal crops. Sensors 12:17343-17357. |
|
Bayes T (1764). An essay toward solving a problem in the doctrine of chances. Philos. Trans. R Soc. London 53:370-418. |
|
Biller RH (1998). Reduced input of herbicides by use of optoelectronic sensors. J. Agric. Eng. Res. 71:357-362. |
|
Burgos-Artizzu X P, Ribeiro A, Tellaeche A, Pajares G, Fernández-Quintanilla C (2009). Improving weed pressure assessment using digital images from an experience-based reasoning approach. Comput. Electron. Agric. 65:176-185. |
|
Chen S, Li Y, Mao H, Shen B, Zhang Y, Chen B (2009). Research on distinguishing weed from crop using spectrum analysis technology. Spectrosc. Spec. Anal. 29(2):463-466. |
|
Christensen S, Søgaard H, Kudsk P, Nørremark M, Lund I, Nadimi E, Jørgensen R (2009). Site-specific weed control technologies. Weed Res. 49:233-241. |
|
FAO (2009). The lurking menace of weeds. Available at: |
|
Haff RP, Slaughter DC (2009). X-ray based stem detection in an automatic tomato weeding system. In: ASAE Annual Meeting. Paper Number: 096050. |
|
Jurado-Expósito M, López-Granados F, Atenciano S, GarcíA-Torres L (2003). Discrimination of weed seedlings, wheat (Triticum aestivum) stubble and sunflower (Helianthus annuus) by near-infrared reflectance spectroscopy (NIRS). Crop Prot. 22(10):1177-1180. |
|
Karimi Y, Prasher OS, Patel RM, Kim HS (2006). Application of support vector machine technology for weed and nitrogen stress detection in corn. Comput. Electron. Agric. 51(1-2):99-109. |
|
Koger CH, Bruce LM, Shaw DR, Reddy KN (2003). Wavelet analysis of hyperspectral reflectance data for detecting pitted morningglory (Ipomoea lacunosa) in soybean (Glycine max). Remote Sens. Environ. 86(1):108-119. |
|
Li Z, Rao H, Wang Y, Ji C (2007). Status quo and advance on research of variable- rate spraying technology. J. Northeast Agric. Uni. 38(4):563-567. |
|
Li G (2010). Research on discrimination of varieties of invasive weeds based on visible and near-infrared spectroscopy. Dissertation of Zhejiang University, Hang Zhou, China. (In Chinese with English abstract) |
|
Lopez-Granados F, Pena-Barragan JM, Jurado-Exposita M, Francisco-Fernandez M, Cao R, Alosno-Betanzos A (2008). Multi spectral classification of grass weeds and wheat (Triticum durum) using linear and nonparametric functional discriminant analysis and neural networks. Weed Res. 48(1):28-37. |
|
Mao W, Wang Y, Zhang X (2005). Spectrum analysis of crop and weeds at seedling. Spectrosc. Spec. Anal. 25(6):984-987. |
|
Moshou D, Ramon H, De Baerdemaeker J (2002). A weed species spectral detector based on neural networks. Precis. Agric. 3(3):209-223. |
|
Piron A, Leemans V, Kleynen O (2008). Selection of the most efficient wavelength bands for discriminating weeds from crop. Comput. Electron. Agric. 62(2):141-148. |
|
Piron A, van der Heijden F, Destain MF (2011). Weed detection in 3D images. Precis. Agric. 12:607-622. |
|
Rogalski A (2003). Infrared detectors: status and trends. Prog. Quant. Electron. 27(2):59-62. |
|
Slaughter DC, Lanini WT, Giles DK (2004). Discriminating weeds from processing tomato plants using visible and near-infrared spectroscopy. Trans. ASABE 47(6):1907-1911. |
|
Sui R, Thomasson JA, Hanks J, Wooten J (2008). Ground-based sensing system for weed mapping in cotton. Comput. Electron. Agric. 60(1):31-38. |
|
Tang J (2010). Research on Weed Detection and Navigation Parameters Acquisition of Pesticide Spraying Robot. Dissertation of North-west Agriculture and Forestry University, Yang Ling, China. (In Chinese with English abstract) |
|
Thenkabail PS, Enclona EA, Ashton MS, Meer BVD (2004). Accuracy assessments of hyper spectral waveband performance for vegetation analysis applications. Remote Sens. Environ. 91(3):354-376. |
|
Thompson JF, Stafford JV, Miller PCH (1991). Potential for automatic weed detection and selective herbicide application. Crop Prot. 10(4):254-259. |
|
Thorp K, Tian L (2004). A Review on Remote Sensing of Weeds in Agriculture. Precision Agriculture. pp. 5477-508. |
|
Vrindts E, De Baerdemaeker J, Ramon H (2002). Weed detection using canopy reflection. Precis. Agric. 3(1):63-80. |
|
Wang N, Zhang N, Peterson DE, Dowell FE (2001). Design of an optical weed sensor using plant spectral characteristics. Trans. ASAE 44(2):409-419. |
|
Weis M, Sökefeld M (2010). Precision Crop Protection - the Challenge and Use of Heterogeneity; Springer Verlag: Dordrecht/Heidelberg/London/New York. Detect. Ident. Weeds 1:119-134. |
|
Zu Q, Zhao C, Deng W, Wang X (2013a). Research on discrimination of cabbage and weeds based on visible and near-infrared spectrum analysis. Spectrosc. Spec. Anal. 33(5):1202-1205. (In Chinese with English abstract) |
|
Zu Q, Deng W, Wang X, Zhao C (2013b). Research on spectra recognition method for cabbages and weeds based on PCA and SIMCA. Spectrosc. Spec. Anal. 33(10):2745-2750. |
Copyright © 2024 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0