Multi-Organ Plant Classification Based on Convolutional and Recurrent Neural Networks

• Published in: IEEE transactions on image processing 2018-09, Vol.27 (9), p.4287-4301
• Main Authors: Sue Han Lee, Chee Seng Chan, Remagnino, Paolo
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Biological systems; Classification; Complexity; Computer vision; Databases, Factual; Deep Learning; Feature extraction; Image classification; Image Processing, Computer-Assisted - methods; Machine learning; Neural networks; Organs; Plant classification; Plants - classification; Recurrent neural networks; Shape; Species classification; Task analysis
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2018.2836321

Classification of plants based on a multi-organ approach is very challenging. Although additional data provide more information that might help to disambiguate between species, the variability in shape and appearance in plant organs also raises the degree of complexity of the problem. Despite promising solutions built using deep learning enable representative features to be learned for plant images, the existing approaches focus mainly on generic features for species classification, disregarding the features representing plant organs. In fact, plants are complex living organisms sustained by a number of organ systems. In our approach, we introduce a hybrid generic-organ convolutional neural network (HGO-CNN), which takes into account both organ and generic information, combining them using a new feature fusion scheme for species classification. Next, instead of using a CNN-based method to operate on one image with a single organ, we extend our approach. We propose a new framework for plant structural learning using the recurrent neural network-based method. This novel approach supports classification based on a varying number of plant views, capturing one or more organs of a plant, by optimizing the contextual dependencies between them. We also present the qualitative results of our proposed models based on feature visualization techniques and show that the outcomes of visualizations depict our hypothesis and expectation. Finally, we show that by leveraging and combining the aforementioned techniques, our best network outperforms the state of the art on the PlantClef2015 benchmark. The source code and models are available at https://github.com/cs-chan/Deep-Plant.