Research Article
A Comparative Analysis of Machine Learning Algorithms for Detection of Organic and Nonorganic Cotton Diseases
Table 4
Comparison of K-means and KNN techniques.
| | Author and year | Methodology | Detected diseases | Remarks | Dataset | Gaps identified |
| | Revathi and Hemalatha [31] | K-means nearest neighbour | Grey mildew
Bacterial blight
Leaf curl virus disease-gemini virusAlternaria leaf spot | Accuracy of 92% | N/A | The accuracy of KNN is less compared to others | | Parikh et al. [32] | KNN classification | Grey mildew | Accuracy of 82.5% | 150 images
40 images (1024 × 1024 pixels) | Size of the block is chosen depending upon the presence of some disease pattern | | Narmadha and Arulvadivu [33] | K-means technique | Blast
Brown spot | Accuracy of 94.7% | MATLAB image library | Time consuming | | Schuster et al. [34] | K-means clustering
Artificial neural network | N/A | Accuracy of 92.5% | N/A | KNN not specified for a particular metric may be contiguous |
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