Aim: The proposed research aims to improve the classification accuracy of the plant disease detection using ResNet over Novel Support Vector Machine. Materials and Methods: The detection of plant disease is performed using ResNet (N=10) and Novel Support Vector Machine (N=10) Novel Algorithms. The sample size for each sample is considered as 10 which is performed with a G power calculator. Results: The ResNet 95% algorithm exhibited better results with classification accuracy compared to that of Novel Support Vector Machine 77% accuracy. The significant accuracy value of p=0.001 (p<0.05) is attained through SPSS Statistical Analysis. Conclusion: The classification of plant disease using ResNet with accuracy efficiency of 95% is better than the Novel Support Vector Machine with accuracy efficiency of 77%.