Table 2.
Reference | Dataset | Method | Evaluation Metrics |
---|---|---|---|
Chavan et al. (2018) [30] | Plant seedlings dataset [20] | AgroAVNET (A hybrid model of AlexNet and VGGNET) | Accuracy: 98.23% |
Trong et al. (2021) [31] | Yielding multi-fold training (YMufT) strategy and DNN; Min-class-max-bound procedure (MCMB); Resnet | Accuracy: 97.18% | |
Xu et al. (2021) [32] | Depthwise separable convolutional neural network, Xception | Accuracy: 99.63% | |
Olsen et al. (2019) [21] | Deepweeds [21] | Dataset was classified with the ResNet-50 and Inception-v3 CNN models to establish a baseline level of performance for comparison. | Accuracy: 95.1% (Inception-v3) Accuracy: 95.7% (ResNet-50) |
Ferreira et al. (2019) [33] | Joint Unsupervised Learning of Deep Representations and Image Clusters (JULE) and Deep Clustering for Unsupervised Learning of Visual Features (DeepCluster) | Precision: 95% | |
Hu et al. (2020) [34] | GWN (Graph Weeds Net) | Accuracy: 98.1% | |
Naresh et al. (2016) [35] | Flavia [28] | MLBP (Modified Local binary patterns) | Accuracy: 97.55% |
Mahajan et al. (2021) [36] | Support vector machine with adaptive boosting | Precision:95.85% | |
Yang C. Z. (2021) [37] | MTD (multiscale triangle descriptor) and LBP-HF (local binary pattern histogram Fourier) | Accuracy: 99.1% |