Classification of Plants Using Convolutional Neural Network

Abstract

Plant leaf classification plays a foremost role in botanical research, Ayurveda, agriculture practices, medicine and drug, weed detection, and many more areas. It is a technique by which a plant leaf is categorized based on its different morphological structures. The aim of this paper is to offer a deep learning technique for plant leaf classification with the help of deep Convolutional Neural Network as a substitute of conventional classification methods like k-nearest neighbor, probabilistic neural network, support vector machine, genetic algorithm, and principal component analysis, which all need feature extraction and are time-consuming. CNN has been used since it has outperformed in various image recognition challenges and feature extraction is performed automatically, which takes less time. This work uses a 5000 leaf images of two plant species. 4000 images are used for training and 1000 for testing purpose. CNN is trained in such a way that it can classify the species and predict the class for a new leaf image. The proposed model is run for different epochs and results were recorded. It was observed that the CNN model performed effectively in distinguishing the plant leaf images and achieved 99.96% training accuracy and 99.90% testing accuracy.