Abstract: The Indian economy heavily relies on agriculture, which involves the cultivation of various plants for food and other products, as well as the raising of domesticated animals. Nutrients are crucial for agriculture and crop production, and nutrient deficiency is one of the many factors that can cause a decrease in crop yield. Detecting nutrient deficiency and determining theappropriate fertilizer to address it is a major challenge faced by farmers. To enhance productivity, agricultural automation has made significant advancements in recent years. One such automated technique is the use of Convolutional Neural Networks (CNN) to process captured images of crops. By comparing these images to an existing dataset, the CNN can identify nutrient deficiencies in crops and indicate the percentage of deficiency. The LCD display will show the name of the disease associated with the nutrient deficiency and the appropriate amount of fertilizer required. This technology can alleviate the burden of manual nutrient deficiency detection faced by farmers and can help optimize fertilizer use to enhance crop yield