Name
Title | ||
|---|---|---|
Multi-label learning for crop leaf diseases recognition and severity estimation based on convolutional neural networks |
Abstract | ||
|---|---|---|
Crop diseases have always been a dilemma as it can cause significant diminution in both quality and quantity of agricultural yields. Thus, automatic recognition and severity estimation of crop diseases on leaves plays a crucial role in agricultural sector. In this paper, we propose a series of automatic image-based crop leaf diseases recognition and severity estimation networks, i.e., BR-CNNs, which can simultaneously recognize crop species, classify crop diseases and estimate crop diseases severity based on deep learning. BR-CNNs based on binary relevance (BR) multi-label learning algorithm and deep convolutional neural network (CNN) approaches succeed in identifying 7 crop species, 10 crop diseases types (including Healthy) and 3 crop diseases severity kinds (normal, general and serious). Compared with LP-CNNs and MLP-CNNs, the overall performance of BR-CNNs is superior. The BR-CNN based on ResNet50 achieves the best test accuracy of 86.70%, which demonstrates the feasibility and effectiveness of our network. The BR-CNN based on the light-weight NasNet also achieves excellent test accuracy of 85.28%, which can provide more possibilities for the development of mobile systems and devices. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s00500-020-04866-z | SOFT COMPUTING |
Keywords | DocType | Volume |
Multi-label,Crop diseases recognition,Crop diseases severity estimation,Convolutional neural network,Computer vision | Journal | 24.0 |
Issue | ISSN | Citations |
20.0 | 1432-7643 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Miaomiao Ji | 1 | 1 | 1.37 |
| Keke Zhang | 2 | 2 | 1.39 |
| Qiufeng Wu | 3 | 0 | 1.01 |
| deng zhao | 4 | 16 | 3.66 |