Use of airborne multispectral imagery to discriminate and map weed infestations in a citrus [Citrus spp.] orchard

• Published in: Weed biology and management Vol. 7; no. 1; pp. 23 - 30
• Main Authors: Ye, X.(Tokyo Univ. of Agriculture and Technology, Fuchu (Japan)), Sakai, K, Asada, S, Sasao, A
• Format: Journal Article
• Language: English
• Published: Melbourne, Australia Melbourne, Australia : Blackwell Publishing Asia 01.03.2007; Blackwell Publishing Asia
• Subjects: ANALISIS DE IMAGENES; ANALYSE D'IMAGE; application rate; CITRUS; CLASIFICACION; CLASSIFICATION; crop production; crop yield; eCognition; herbicides; IMAGE ANALYSIS; MALEZAS; MAUVAISE HERBE; multispectral imagery; orchards; precision agriculture; REMOTE SENSING; spatial distribution; surveys; TELEDETECCION; TELEDETECTION; weed; weed control; WEEDS; Japan
• ISSN: 1444-6162; 1445-6664
• DOI: 10.1111/j.1445-6664.2006.00236.x

Reliable information on weed abundance and distribution within fields is essential for weed management in agricultural systems. Such information is necessary to adopt localized and variable rates of herbicide spraying, thus reducing chemical waste, crop damage, and environmental pollution. This paper examined the potential of airborne multispectral imagery to discriminate and map weed infestations in an experimental citrus orchard in Japan. Using an airborne digital sensor, multispectral imagery was acquired over the study site on 10 April 2003. The obtained reflectance imagery was analyzed using an image object-based approach in eCognition. After creating image objects on the image, the spectral information for weeds and citrus, represented by corresponding selected sample image objects, was extracted. Significant differences in the spectral characteristics between weeds and citrus were observed in each of the red, green, and blue wavebands. The simple average values of these wavebands were used to classify image objects with the nearest neighbor algorithm. Maps were generated with different classes or levels of class groups. A subsequent accuracy assessment demonstrated that the weeds were successfully discriminated from other image objects with a classification accuracy of 99.07%. Therefore, maps generated based on the classification result could provide valuable information for developing a site-specific weed management program for the study orchard.