Volume 2 - Issue 2, March - April 2026

Plant diseases one of the biggest threats to global agriculture as they cause a reduction in crop yield and act as a threat to the food continuity. The precise identification of plant disease is essential in assisting crop management so that crop losses are minimized. This paper proposes a new framework for predicting the diseases of plants that combines nutritional data with ML models, specifically the Support Vector Machine (SVM) algorithm and enhanced the Bee Colony Optimization (BCO) algorithm for hyperparameter tuning. With the use of BCO, the system optimizes the hyperparameters which increase the prediction accuracy and the speed of calculation of the models. This proposed model shows that combining SVM with BCO result in a significant improvement in the accuracy 83% and enhances the robustness of the plant disease prediction models allowing for an efficient and effective system for early disease diagnosis to be developed.

Mr.D.Praveenkumar, Dr.D.Geethamani,"A Novel Framework for Nutrient-Based Plant Disease Prediction with Machine Learning" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(2): Page(551-555) Mar-Apr 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.