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Here are some papers we can look at for inspiration. This is a random sample to start with, not exhaustive, please add more:
Deep-Plant: Plant Identification with convolutional neural networks
Sue Han Lee, Chee Seng Chan, Paul Wilkin, Paolo Remagnino
This paper studies convolutional neural networks (CNN) to learn unsupervised feature representations for 44 different plant species, collected at the Royal Botanic Gardens, Kew, England. To gain intuition on the chosen features from the CNN model (opposed to a 'black box' solution), a visualisation technique based on the deconvolutional networks (DN) is utilized. It is found that venations of different order have been chosen to uniquely represent each of the plant species. Experimental results using these CNN features with different classifiers show consistency and superiority compared to the state-of-the art solutions which rely on hand-crafted features.
Flower species identification using deep convolutional neural networks
Conference: Regional Conference on Computer and Information Engineering (RCCIE 2016) October 2016
Nhan Nguyen, Van Tuan Le, Thi Lan Le, Yasushi Yagi
This paper demonstrates robustness of deep convolutional neural networks (CNN) for automatically identifying plant species from flower images. Among organs of plant, flower image plays an important role because its appearances are highly distinguishing. Moreover, flower's observations are stable and less invariant with weather conditions, age of trees, or other artifacts. A number of traditional features have been proposed for basic-level category of the recognition task. However, these approaches may eliminate many useful natural cues during the feature extraction. They also require domain-related expert knowledge. In this paper, robustness of a deep Convolutional Neural Network (CNN) is presented. To select the appropriate network, we firstly implement a comparative study on evaluating performances of well-known CNNs such as AlexNet,CaffeNet,GoogLeNet. We have concluded that GoogLeNet archives the highest performance. By tuning network parameters, the highest performance is archived with the accuracy rate of 67.45% at rank 1 and 90.82% at rank 10 for a flower dataset of 967 species extracted from PlantCLEF 2015. These results are higher six times comparing with conventional Kernel Descriptor (KDES) techniques [1]. Consequently, the proposed technique can be one of the most promising solutions for helps developing the image-based searching applications in fields of the botanical classification, ecological monitoring systems, or in the multimedia community. Discover the world's research
Plant identification using deep neural networks via optimization of transfer learning parameters
Mostafa Mehdipour Ghazia, BerrinYanikoglua, ErchanAptoulab
We use deep convolutional neural networks to identify the plant species captured in a photograph and evaluate different factors affecting the performance of these networks. Three powerful and popular deep learning architectures, namely GoogLeNet, AlexNet, and VGGNet, are used for this purpose. Transfer learning is used to fine-tune the pre-trained models, using the plant task datasets of LifeCLEF 2015. To decrease the chance of overfitting, data augmentation techniques are applied based on image transforms such as rotation, translation, reflection, and scaling. Furthermore, the networks' parameters are adjusted and different classifiers are fused to improve overall performance. Our best combined system has achieved an overall accuracy of 80% on the validation set and an overall inverse rank score of 0.752 on the official test set. A comparison of our results against the results of the LifeCLEF 2015 plant identification campaign shows that we have improved the overall validation accuracy of the top system by 15% points and its overall inverse rank score on the test set by 0.1 while outperforming the top three competition participants in all categories. The system recently obtained a very close second place in the PlantCLEF 2016.
Deep Learning for Plant Identification in Natural Environment
Yu Sun, Yuan Liu, Guan Wang, and Haiyan Zhang
Plant image identification has become an interdisciplinary focus in both botanical taxonomy and computer vision. The first plant image dataset collected by mobile phone in natural scene is presented, which contains 10,000 images of 100 ornamental plant species in Beijing Forestry University campus. A 26-layer deep learning model consisting of 8 residual building blocks is designed for large-scale plant classification in natural environment. The proposed model achieves a recognition rate of 91.78% on the BJFU100 dataset, demonstrating that deep learning is a promising technology for smart forestry.