A country's inventive growth is dependent on the agricultural sector. Agriculture, the foundation of all nations, offers food and raw resources. Agriculture is hugely important to humans as a food source. As a result, plant diseases detection has become a major concern. The history of using technology in agriculture dates back several decades, but the application of deep learning in crop disease detection gained prominence in the early 21st century with the advent of powerful computing systems and large datasets. In the traditional system, farmers heavily relied on manual observation and knowledge passed down through generations to identify crop diseases. Agricultural experts would physically inspect the crops, diagnose diseases based on visible symptoms, and suggest remedies. While this method had its merits, it was time-consuming, dependent on the expertise of the observer, and sometimes led to misdiagnosis. Therefore, the need for an advanced approach like deep learning in crop disease detection arises from the growing global population and the subsequent increase in food demand. Timely and accurate identification of crop diseases is crucial to prevent significant yield losses. By automating the detection process, farmers can take swift actions to mitigate the spread of diseases, thereby ensuring higher agricultural productivity. Moreover, providing precise pesticide suggestions reduces the environmental impact of farming by minimizing the unnecessary use of chemicals. Deep learning algorithms, particularly convolutional neural networks (CNNs), have proven to be highly effective in image recognition tasks, making them ideal for identifying patterns in images of diseased crops. The introduction of deep learning in agriculture, specifically in crop disease detection and classification, has revolutionized the way farmers manage their crops. By leveraging advanced technologies, farmers can now detect diseases in crops more accurately and efficiently. This has significant implications for food security, as it enables timely intervention and suggests appropriate measures, such as pesticide usage, to curb the spread of diseases.