Limitation in the use of spectral analysis to detect Weligama coconut leaf wilt disease affected palms in Southern Sri Lanka
Weligama Coconut Leaf Wilt Disease (WCLWD) was first reported in late 2006 in the Weligama Divisional Secretariat Division (DSD) in Matara District in the Southern Province of Sri Lanka. Surveys conducted in 2012 revealed that 65,838, 251,980 and 14,344 palms affected by the disease in Galle, Malara and Hambanlota districts respectively. It has been confirmed by the Coconut Research Institute that the disease is caused by a phytoplasma which has some resemblance to the phytoplasma of Coconut Root (Wilt) disease in India. A 3 km wide boundary was demarcated covering the area affected by the disease and all suspicious palms were removed in the boundary zone. In the core area, affected palms were gradual removed. The disease prevailing area spanned 680 km 2 and the detection of affected coconut palms across such a large area became practically difficult. Therefore, the objective of the present study was to find alternative methods to detect affected coconut palms with yellowing leaves prominent at the middle stage of the disease, using multispectral satellite images of 0.5 m resolution. It was envisaged to use this technique as a supportive management tool at field level, if successful. Normalized Differential Vegetation Index (NDVI) which is the standard and widely used algorithm for detecting vegetation under stress conditions did not reveal an appreciable isolation of canopies of affected coconut palms. Of the different customized algorithms tested, the best outcome was obtained by the algorithm (Red Green) 2 x (Blue) When this algorithm was applied to the multispectral satellite images of 0.5 m resolution in the affected area, adult coconut palms in advanced stage of the disease could be distinguished with an accuracy of above 80% However, a detection of palms in the early stages of the disease where leaf flaccidity is the major morphological symptom could not be made with an acceptable level of accuracy. Moreover, the detection of affected coconut seedlings was much more difficult. Thus, only an overall accuracy of 60 70% could be achieved by this approach with multispectral satellite images of 0.5 m resolution. Although the level of accuracy is not sufficient for a comprehensive field level application, there is scope for further improvement using images with higher resolution.