A Comparative Analysis of Machine Learning Algorithms for Detection of Organic and Nonorganic Cotton Diseases

Table 3

Comparison of image processing techniques.

Author and year	Methodology	Detected diseases	Remarks	Gaps identified
Rothe and Kshirsagar [27]	Image enhancement; Image segmentation; Feature extraction	Bacterial blightAlternaria; Myrothecium	Sharpness of bacterial blight 490.11292Alternaria 194.20520Myrothecium 502.62412	The detection of diseases is limited
Revathi and Hemalatha [28]	Edge detection	Fusarium wiltVerticillium wilt; Leaf blight	Accuracy of 98.1%	If the size of the image is enlarged, quality would be reduced
Kirthi Pilli et al. [29]	Image acquisitionPreprocessingSegmentationFeature extraction	Bacterium blight; Magnesium deficiency	Accuracy 90%	Wider images result in the incorrect classification