Leaf Disease Prediction in Farms Using Image Processing

Abstract

Agriculture plays a vital role in sustaining the global economy, and the productivity of crops greatly depends on their health. One of the major challenges faced by farmers is the timely detection of crop diseases, especially those that first appear on plant leaves. Traditional disease identification relies heavily on manual inspection by experts, which is time-consuming, labor-intensive, and often prone to human error. With recent advancements in computer vision and artificial intelligence, image processing has emerged as a highly effective method for automating leaf disease prediction in farms. Image processing refers to a set of techniques used to analyze and extract meaningful information from images. In the context of agriculture, these techniques can help detect disease symptoms such as spots, discoloration, and texture changes on leaves. The prediction process typically begins with image acquisition — farmers capture pictures of leaves using cameras or smartphones. These images undergo preprocessing steps such as noise reduction, contrast adjustment, and background removal to enhance the clarity of disease-related features. Feature extraction is a crucial stage in which important characteristics of the leaf are identified. Techniques such as color analysis, edge detection, and texture analysis help differentiate healthy leaves from diseased ones. Modern approaches often integrate machine learning or deep learning methods, especially Convolutional Neural Networks (CNNs), which can automatically learn complex patterns from large datasets of leaf images. These models can classify diseases like bacterial blight, powdery mildew, leaf spot, and rust with high accuracy.