Abstract: Destructive insects and plant leaf diseases present an important threat to the agricultural sector. Faster and more accurate leaf disease prediction in crops could enable the development of an early treatment approach and substantially reduce economic losses. Researchers have been able to substantially improve the performance and accuracy of object identification and recognition systems through advanced developments in deep learning. In contrast, the techniques to detect plant fetal anomalies are labor-intensive and laborious. Several new technologies have been combined with the cultivation system to reduce detection moments and enhance the efficacy of plant disease detection. With the objective prevent these losses and provide a quick solution, this study develops a CNN (Convolution Neural Network) model employing image processing. Upon success, our approach achieves an accuracy of 94.29%. Our research can help the universal farmer enhance crop and fruit output rates while minimizing plant diseases and insect attractiveness.