An efficient system for leaf disease detection using SVM and KNN based classification for improved accuracy

The primary goal of this study is to perform leaf disease diagnosis using K-Nearest Neighbors classification (KNN) and compare its performance against that of Support Vector Machine (SVM) technique to increase accuracy. Materials and Techniques SVM was used in this study to detect leaf illness by sampling 10 samples (N=10), and K-Nearest Neighbors was used to classify the samples (N=10). With a sample size of 20 for each group and a pretest power of 80%, two groups are analyzed statistically. Both strategies' accuracy is evaluated. The Support Vector Machine algorithm outperforms the KNN approach in terms of accuracy (96.25% vs. 83.3%), and independent samples T-tests show a statistically significant difference between the two algorithms' accuracy at p=0.37 (p0.05). Conclusion: According to the results, the Support Vector Machine technique for identifying leaf diseases appears to be noticeably superior to K-Nearest Neighbors categorization.