Plant Leaf Disease Detection Using Machine Learning Models

The manual identification of plant diseases through visual examination demands large amounts of labor while showing restricted accuracy and variable scope. The implementation of automatic detection approaches needs fewer human operations to achieve faster results along with better precision. Plants develop brown and yellow spots which cause early and late scorch and transmit different bacterial fungal and viral infections during disease manifestations. Through image processing one can detect plant diseases by both measuring affected areas and analyzing color variations present in infected areas. A comprehensive disease detection process requires image acquisition followed by preprocessing before segmentation and feature extraction and ends with classification. The research explores different methods that use leaf images for detecting plant diseases. Model classifiers used in this research performed with high accuracy rates as deep learning methods produced better outcomes than conventional methods. The study demonstrates that image processing tools offer precision farmers the ability to detect diseases early so they can reduce crop losses and optimize pesticides usage. The findings demonstrated from this research confirm that image-based disease detection systems enhance both agricultural productiveness and sustainability levels.