Table 3.
Deep learning application to GS in plants.
| Crop | Topology | Traits | Ref | ||
|---|---|---|---|---|---|
| 1 | Wheat | MLP | Grain yield, days to heading | (Perez-Rodriguez et al., 2012) | |
| 2 | Maize | DBN | Grain yield, female flowering or days to silking, male flowering time or days to anthesis, and anthesis-silking interval | (Gonzalez-Camacho et al., 2012) | |
| 3 | Maize and wheat | MLP | Grain yield | (Gonzalez-Camacho et al., 2016) | |
| 4 | Maize and wheat | MLP | Grain yield | (Montesinos-Lopez et al., 2018) | |
| 5 | wheat | MLP | Grain yield, days to heading, plant height | (Montesinos-Lopez et al., 2019) | |
| 6 | Maize | MLP | Grain yield, check yield, yield difference | (Wang, 2019) | |
| 7 | Soybean | CNN | Grain yield, protein, oil, moisture, plant height | (Liu et al., 2019) | |
| 8 | Arabidopsis | MLP, CNN | Arabidopsis traits | (Pook et al., 2020) | |
| 9 | Maize and wheat | MLP | Leaf spot diseases, Gray Leaf Spot | (Perez-Rodriguez et al., 2020) | |
MLP, Multi-Layer Perceptron; CNN, Convolutional Neural Networks; DBN, Deep Belief Network.