One of the primary factors determining a nation’s growth is its agricultural sector. In India, agriculture employs around 65% of the country’s workforce. A variety of diseases can infect crops as a result of different seasonal circumstances. These illnesses initially damage the plant’s leaves before spreading to the entire plant, which has an impact on the type and quantity of crop that is grown. Because there are so many plants on the farm, it is quite challenging for the human eye to identify and categorize each plant’s condition. And since these diseases have the potential to spread, it is critical to identify every plant. Our findings indicate that when utilizing Edge Impulse for adaptive learning, plant disease detection accuracy and efficiency may be increased in comparison to starting from scratch. This has the potential for the development of plant illnesses. This work advances learning and edge computing by offering insights and recommendations for creating accurate and efficient learning models for identifying plant diseases on edge devices.