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

Table 6

Comparison of decision tree and random forest techniques.

Author and year	Methodology	Detected diseases	Remarks	Dataset	Gaps identified
Mehta et al. [36]	Decision tree; Random forest	N/A	For cotton disease prediction,; RF 95.30%; Decision tree 96.73%; Multioutput regressor 89.61%; Sensitivity 82.21%	30 images of size 1504 × 1000	MLP did not do well while classification
Chopda et al. [37]	Decision tree classification	Anthracnose; Grey mildew; Wilt	N/A	N/A	The model needs training