Plant Recognition System Using Convolutional Neural Network

Abstract

There are many plant species available to man, even some that have not been discovered. There are species of plants are reported to be on the verge of extinction. Due to the inhumane nature of humans, forest is cleared to make space for industrial purposes. These purposes will lead to the destruction of nature, including plants. Plants are important to be preserved for the future. Plants should be made aware to the public to avoid such disaster. Ensuring they know the plants species are the reasons for the development of this system. The system will ensure people can identify plants without having flipping through research papers or books. Those plants that are similar to one another can be recognized easily. Using convolutional neural network to recognize plant can help the public gain more awareness towards plants species. Using the traditional convolutional neural network for the training of model to recognize plants. The system was tested to get the best accuracy to recognize plants. The images were increased to ensure a better accuracy. This system requires a good model in order to recognize the correct plants. Convolutional neural network is commonly used for image classification due to its high accuracy. The accuracy for the system created in this project is 78.85%. This project can be improved in the future by increasing the accuracy. There are a lot of ways to increase the accuracy such as trying out other CNN architectures or by feeding the system more images. This project involves 7 phases which is preliminary study, knowledge gathering and acquisition, knowledge representation, system design, system development, system testing and evaluation, and documentation.

Deep Convolutional Neural Network Architecture for Plant Seedling Classification

“…A deep encoder-decoder CNN was updated with a 14channel image containing vegetation index values for segmentation to solve crop-weed classification consisting of sugar beet plants and weeds in [11]. In [12], a CNN network was able to determine unsupervised feature characterizations from 44 different plant species with high accuracy. In [13], instance segmentation was utilized to identify crops and weeds.…”