Name
Title | ||
|---|---|---|
Transformer Neural Network for Weed and Crop Classification of High Resolution UAV Images |
Abstract | ||
|---|---|---|
Monitoring crops and weeds is a major challenge in agriculture and food production today. Weeds compete directly with crops for moisture, nutrients, and sunlight. They therefore have a significant negative impact on crop yield if not sufficiently controlled. Weed detection and mapping is an essential step in weed control. Many existing research studies recognize the importance of remote sensing systems and machine learning algorithms in weed management. Deep learning approaches have shown good performance in many agriculture-related remote sensing tasks, such as plant classification, disease detection, etc. However, despite the success of these approaches, they still face many challenges such as high computation cost, the need of large labelled datasets, intra-class discrimination (in growing phase weeds and crops share many attributes similarity as color, texture, and shape), etc. This paper aims to show that the attention-based deep network is a promising approach to address the forementioned problems, in the context of weeds and crops recognition with drone system. The specific objective of this study was to investigate visual transformers (ViT) and apply them to plant classification in Unmanned Aerial Vehicles (UAV) images. Data were collected using a high-resolution camera mounted on a UAV, which was deployed in beet, parsley and spinach fields. The acquired data were augmented to build larger dataset, since ViT requires large sample sets for better performance, we also adopted the transfer learning strategy. Experiments were set out to assess the effect of training and validation dataset size, as well as the effect of increasing the test set while reducing the training set. The results show that with a small labeled training dataset, the ViT models outperform state-of-the-art models such as EfficientNet and ResNet. The results of this study are promising and show the potential of ViT to be applied to a wide range of remote sensing image analysis tasks. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/rs14030592 | REMOTE SENSING |
Keywords | DocType | Volume |
computer vision, deep learning, self-attention, vision transformers, remote sensing, drone, image classification, agriculture | Journal | 14 |
Issue | Citations | PageRank |
3 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Reenul Reedha | 1 | 0 | 0.34 |
| Eric Dericquebourg | 2 | 0 | 0.34 |
| Raphaël Canals | 3 | 0 | 0.34 |
| Adel Hafiane | 4 | 0 | 0.34 |