Leaf Disease Detection Using OpenCV And Deep Learning

Plants play a role in maintaining the food source, yet plant diseases can lead to a significant decrease in production, As the world’s population continues to grow, increasing crop production is vital to ensure food safety and security for all. Unfortunately, numerous plant diseases can undermine the supply of essential crops. Therefore, it is essential to identify and monitor these diseases on a large scale. However manual monitoring by agriculture specialists and botanists is difficult time consuming and prone to mistakes. To tackle this issue this research suggests a system for identifying leaf diseases using OpenCV, a computer vision library known for its effectiveness, in detecting plant ailments. The proposed system offers numerous benefits, including high accuracy and ease of implementation, making it a cost-effective means of detecting and managing plant diseases. By implementing this system, it is possible to reduce economic losses for farmers and improve crop yields. Tomato is a popular fruit(vegetable) belonging to the nightshade family. It comes in different shapes, sizes, and colors such as red, yellow, and green. In agriculture, tomato plants are prone to various diseases, including Target Spot, Late Blight, and Yellow Leaf Curl Virus. To tackle this, we classified ten common tomato diseases and trained a model using a dataset of 15,600 images. Our model achieved an accuracy ranging from 91.25% to 93.9%. which can help farmers detect and treat diseases at an early stage, improving crop yields and reducing losses.