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- W3178292669 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." @default.
- W3178292669 created "2021-07-19" @default.
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- W3178292669 date "2021-07-10" @default.
- W3178292669 modified "2023-09-27" @default.
- W3178292669 title "Detection of Fungal Disease in Cabbage Images Using Adaptive Thresholding Technique Compared with Threshold Technique" @default.
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- W3178292669 doi "https://doi.org/10.47059/revistageintec.v11i4.2172" @default.
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