Machine learning, particularly with convolutional neural networks (CNNs), has demonstrated significant success in classifying various plant diseases. This proposal offers a summary and analysis of current methodologies for detecting capsicum leaf diseases. The investigation employed several simulation techniques for neurons and layers, utilizing a CNN trained on a dataset comprising images of capsicum plants. The decline in both quantity and quality of agricultural products can be attributed to foliar diseases, resulting in substantial financial losses. Robust plant health and dependable disease detection data can facilitate effective disease control, thereby boosting agricultural production. Given the importance of managing insect populations, particularly concerning capsicum plants, swift action is necessary to address issues such as viruses or wilt, which can devastate gardens. Prompt removal of diseased plants is advisable to prevent further spread among neighboring plants. Convolutional networks have the capability to learn features from larger datasets, mitigating concerns about image quality. Real-time data from 3,100 leaf images can achieve an average accuracy of 96.83% in identifying capsicum diseases.