Cucumber leaf pathogenic spores identification is an significant stage in achieving quick disease analysis for cultivation. Image processing techniques primarily rely on manually created characteristics that are challenging to handle when it comes to pathogen spore detection. In this paper, a Mobile Net classification is suggested to quickly recognise harmful spores in cucumbers leaf. Firstly, grayscale conversion is applied to the cucumber leaf disease dataset to change the color image in to gray and the unwanted noises are removed using the median filter to get high quality image. Next, the processed image is given to the K Means clustering for the separation of relevant regions. After that cucumber leaf image is featured by Gray Level Co occurrence Matrix (GLCM) textured is designed for further analysis. Finally, a Mobile Net framework is processed to enhance the classification of cucumber leaf pathogenic spores. Using python software proposed framework have accuracy of 94.12% is accomplished when compared to other techniques.