The world population is increasing rapidly. In order to cater the daily needs of an individual, grains and vegetable production are imperative. This paper is focused to establish a technology support to formers and to minimize the deceases in plant. Tomato and pepper bell leaves are considered to detect the deceases. Contrast limited adaptive histogram equalization (CLAHE) is applied to improve the contrast of the leaf image before processing with machine learning algorithm. The contrast limiting is considered with clip limit 40. Bi-cubic interpolation is applied to minimize the false edge of the leaf with neighbouring tails of the leaf. The qualitative parameters like absolute mean brightness error (AMBE), mean square error (MSE), peak signal to noise ratio, mean average error (MAE) and maximum deviation (MD) are analysed. MSE values achieved less than ‘1’ indicates contrast adjustment is good. CNN Classification is applied. The decease detection accuracy with CNN is increased to 95.6 percent with increasing epochs. The accuracy Vs epoch and Loss Vs Epoch analysis is done. Optimum Tunning of hyperparameters (β1), and (β2) is done in this study. The results achieved with this approach are best fit for plant decease finding to improve the yielding rate the crop.