An Improved Temporal Pattern Analysis to Detect the Cotton Leaf Disease using Mobile Net And CNN

The study's major goal is to offer a novel cotton plant leaves disease discovery method by associating the accuracy of mobilenet and CNN algorithms in noticing illness. To forecast cotton leaves illness, the mobile net (N=10) and CNN (N=10) were reiterated 20 times. When compared to CNN, Mobile Net has much superior accuracy (96.97%) and (82.54%). The value of the statistical implication difference 0.01 (p0.05 independent sample test) indicates that the study's findings are significant. Conclusion: Within the scope of this investigation, mobile net discovery of cotton plant leaves illness is more accurate.