Identification of Diseases in Apple Fruits Using Advanced Image Processing Techniques

Diseases in apple crops are a significant problem in agriculture, causing major economic losses. Early identification of diseases is essential to prevent further spread and severe damage. Image processing techniques have become a promising tool for faster and more accurate disease detection compared to conventional methods. This research aims to identify diseases in apple fruits using advanced image processing techniques, focusing on improving accuracy and efficiency to support timely and effective control measures. The research encompasses four main stages in image processing: Enlarge, Pre-Processing, Enhancement, and Convolution. The Enlarge stage magnifies the image to detect details of the infected area. Pre-Processing reduces noise, removes irrelevant background, and normalizes image intensity. The Enhancement stage improves contrast and clarity of the disease-affected apple image, facilitating easier detection. The Convolution stage employs a convolution filter to highlight patterns or disease signatures difficult to recognize manually. A dataset of images of apples infected with different diseases was used to validate and test the method. The proposed method demonstrated a 15% increase in accuracy and a 20% reduction in detection time compared to conventional methods. This technique has proven effective in enhancing detection accuracy and efficiency, showing great potential for integration into automated plant health monitoring systems.