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An Instance Segmentation Model for Strawberry Diseases Based on Mask R-CNN
| Content Provider | MDPI |
|---|---|
| Author | Afzaal, Usman Bhattarai, Bhuwan Pandeya, Yagya Raj Lee, Joonwhoan |
| Copyright Year | 2021 |
| Description | Plant diseases must be identified at the earliest stage for pursuing appropriate treatment procedures and reducing economic and quality losses. There is an indispensable need for low-cost and highly accurate approaches for diagnosing plant diseases. Deep neural networks have achieved state-of-the-art performance in numerous aspects of human life including the agriculture sector. The current state of the literature indicates that there are a limited number of datasets available for autonomous strawberry disease and pest detection that allow fine-grained instance segmentation. To this end, we introduce a novel dataset comprised of 2500 images of seven kinds of strawberry diseases, which allows developing deep learning-based autonomous detection systems to segment strawberry diseases under complex background conditions. As a baseline for future works, we propose a model based on the Mask R-CNN architecture that effectively performs instance segmentation for these seven diseases. We use a ResNet backbone along with following a systematic approach to data augmentation that allows for segmentation of the target diseases under complex environmental conditions, achieving a final mean average precision of 82.43%. |
| Starting Page | 6565 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s21196565 |
| Journal | Sensors |
| Issue Number | 19 |
| Volume Number | 21 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-09-30 |
| Access Restriction | Open |
| Subject Keyword | Sensors Instance Segmentation Smart Farming Convolutional Neural Network Strawberry Disease Detection Mask R-cnn |
| Content Type | Text |
| Resource Type | Article |
