Today’s agricultural world demands high-quality, high-yield agricultural produce. The economy is also impacted by crop production. Crop loss from disease poses a serious risk to food security and production. It is critical to identify plant or crop diseases early on. Farmers can employ preventive methods to halt the spread of plant diseases when they are diagnosed accurately and early. Plant disease classification and detection are crucial for timely management and intervention in agriculture to safeguard crop output and health. Using efficient procedures becomes essential due to the increasing problems given by different infections and environmental factors. This introduction will look at the various methods that are employed to identify and classify plant diseases. This chapter discusses the many approaches to crop disease diagnostics that rely on fuzzy logic and soft computing. A system based on computer vision is created that can automatically identify and categorize leaf diseases and evaluate the health of plants with 92.5% disease identification accuracy. Also discussed another approach to detect and classify plant leaf disease using deep CNN along with soft computing optimization approach. A deep CNN is trained to extract specific features from input image and optimal set of features are extracted using soft computing optimization algorithm thereby giving the accuracy of about 99.8%.