Integrating artificial intelligence and machine learning for plant disease diagnosis and control

Detection and treatment of plant diseases are pivotal in ensuring environmental preservation, sustainable food production, and combating global challenges such as climate change and desertification. While traditional methods rely on visual observation by expert farmers, this research introduces a novel approach by developing a Graphical User Interface (GUI) to identify plant diseases efficiently. The system integrates the Support Vector Machine (SVM) Multi-V algorithm within the MATLAB environment, offering a user-friendly interface for inputting images of diseased plants. These images are then processed and analyzed, achieving an exceptional recognition rate of up to 100 percent through rigorous experimentation and validation. By empowering farmers and agricultural professionals with a practical tool for swift and accurate diagnosis, this research contributes significantly to agricultural technology, enabling timely interventions and enhancing crop yield and sustainability.