Deep convolutional neural network based plant species recognition through features of leaf

In present scenario, the research under image processing has been rapidly transformed from machine learning to deep learning. The deep learning algorithms are usually applied in the various areas like images to be classified or identified more accurately. One of the application areas of deep learning is the plant identification through its leaf which helps to recognize plant species. Botanists consume most of time in identifying plant species by manually scrutinizing and finding its features. This paper proposes an automated plant identification system, for identifying the plants species through their leaf. This task is accomplished using deep convolutional neural network to achieve higher accuracy. Image pre-processing, feature extraction and recognition are three main identification steps which are taken under consideration. Proposed CNN classifier learns the features of plants such as classification of leafs by using hidden layers like convolutional layer, max pooling layer, dropout layers and fully connected layers. The model acquires a knowledge related to features of Swedish leaf dataset in which 15 tree classes are available, that helps to predict the correct category of unknown plant with accuracy of 97% and minimum losses. Result is slightly better than the previous work that analyzes 93.75% of accuracy.

Automated summary

In the current scenario, research in image processing has rapidly shifted from machine learning to deep learning, with the use of deep convolutional neural networks for more accurate plant species identification through leaf recognition. The proposed automated plant identification system successfully achieves the task of identifying plant species through their leaves and achieves an accuracy of 97%.