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.