Aim: The main aim of this research work is to compare the accuracy percentage of leaf wetness predicted by the Novel Logistic Regression algorithm to that predicted by the K-Nearest Neighbour algorithm using meteorological data. Materials and methods:The accuracy of leaf wetness prediction was evaluated using Novel Logistic Regression and K-Nearest Neighbour algorithms with a sample size of 20 at different times. Results: Novel Logistic Regression has a significantly better accuracy percentage (91.89%) compared to KNearest Neighbour accuracy (79%). Between Novel Logistic Regression and K-Nearest Neighbour, The statistical significance difference p=0.07 (p<0.05) independent sample T-test value state that the results in the study are insignificant. Conclusion: The K-Nearest Neighbour method fared much worse than Novel Logistic Regression.