Volume 11 - Volume 11
Detection of Fungal Disease in Cabbage Images Using Adaptive Thresholding Technique Compared with Threshold Technique
Abstract
Aim: To improve the detection rate of fungal disease in cabbage leaf images using the adaptive
thresholding algorithm in terms of accuracy and sensitivity. Materials and methods: The accuracy
and sensitivity of adaptive thresholding algorithm (n=272) was compared with thresholding
algorithm (n=272) with p-value 0.8 and appears to be improved the detection rate of fungal disease
in cabbage in terms of accuracy and sensitivity in the MATLAB simulation tool. Result: The adaptive
thresholding algorithm has appeared to be accuracy (80.5%) and sensitivity (93.5%) than the
thresholding algorithm accuracy(62.7%) and sensitivity(43.1%). Adaptive thresholding algorithm has
appeared to be accuracy (p=0.26) and sensitivity (p=0.614) compared with the thresholding
algorithm. Conclusion: It appears to be that the detection rate is better using adaptive thresholding
algorithm compared with thresholding algorithm in terms of accuracy and sensitivity.
Paper Details
PaperID: 2172
Author's Name: S. Alex and S. Premkumar
Volume: Volume 11
Issues: Volume 11
Keywords: Fungal Disease, Thresholding, Novel Adaptive Thresholding Algorithm, Image Processing.
Year: 2021
Month: June
Pages: 1112-1125