Plant Classification Using Firefly Algorithm and Support Vector Machine

Plant identification plays an important role in crop cultivation and agriculture. Plants are traditionally distinguished based on their fruit, flowers, and leaves. However, relying on human experience quickly becomes tedious and unmanageable, so a need for an automated approach that can assist farmers in crop management presents itself. This research proposes an approach based on swarm intelligence (SI) algorithms and support vector machine (SVM) to extract features and classify plant images. The nature-inspired firefly algorithm (FA) models behavior patterns of fireflies and adapts them to optimization problems for which it excels at resolving. Combined with the SVM model, which is often used for solving classification problems with great accuracy gives a novel approach used to handle plant identification. The resulting FA-SVM algorithm has been thoroughly tested on an extensive plant image dataset, and obtained results indicate that it outperforms competing algorithms in terms of accuracy, as well as other performance metrics, when applied to the same dataset.