# Deep-Plant: Plant Identification with convolutional neural networks

arXiv: https://arxiv.org/abs/1506.08425 · HTML: https://mlsanity.com/paper/1506.08425/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2015.

## Built on by (most cited first, 11 of 11)

- [Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications](https://mlsanity.com/paper/2301.01200/) (2022)
- [In-field high throughput grapevine phenotyping with a consumer-grade depth camera](https://mlsanity.com/paper/2104.06945/) (2021)
- [Deep Learning for Plant Identification and Disease Classification from Leaf Images: Multi-prediction Approaches](https://mlsanity.com/paper/2310.16273/) (2023)
- [Deep-CNN based Robotic Multi-Class Under-Canopy Weed Control in Precision Farming](https://mlsanity.com/paper/2112.13986/) (2021)
- [Automatic Plant Cover Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/2106.11154/) (2021)
- [Plant Species Classification Using Transfer Learning by Pretrained Classifier VGG-19](https://mlsanity.com/paper/2209.03076/) (2022)
- [Semantic Image Segmentation with Deep Learning for Vine Leaf Phenotyping](https://mlsanity.com/paper/2210.13296/) (2022)
- [Multispectral Image Segmentation in Agriculture: A Comprehensive Study on Fusion Approaches](https://mlsanity.com/paper/2308.00159/) (2023)
- [On the Image-Based Detection of Tomato and Corn leaves Diseases : An in-depth comparative experiments](https://mlsanity.com/paper/2312.08659/) (2023)
- [Floralens: a Deep Learning Model for the Portuguese Native Flora](https://mlsanity.com/paper/2403.12072/) (2024)
- [Importance of realism in procedurally-generated synthetic images for deep learning: case studies in maize and canola](https://mlsanity.com/paper/2404.05128/) (2024)

## Abstract (arXiv)

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.