Plant diseases are adverse elements that significantly reduce crop quality and yield. Observing plants for disease with the naked eye is a common practice among seasoned biologists and farmers, although it can be inaccurate and take a lot of time. The purpose of this project is to create and build an intelligent classification system for plant diseases using feature optimization and artificial intelligence approaches. In this study, two approaches are used, and the results of their simulations are contrasted to assess performance. The Plant disease dataset is subjected to feature optimization utilizing optimization techniques in the first section. These features are classified using a Bayesian-optimized support vector machine classifier in the second half of this work, and the results obtained in terms of precision, sensitivity, f-score, and accuracy. The above-said methodologies will enable farmers all over the world to take early action to prevent their crops from becoming irreversibly damaged, thereby saving the world and themselves from a potential economic crisis.

Plant diseases, optimization, Machine Learning, GWO