# Algorithms for Plant Monitoring Applications: A Comprehensive Review
## 3 Literature Analysis
### Algorithms
#### 3.2.5 Transfer Learning Methodology  
Among the possible methodologies, we decided to emphasize the transfer learning techniques, used to exploit a model already trained for a specific task to solve a different problem. Domain adaptation, often considered a subfield of transfer learning, is applied when using a model trained on one domain in a slightly different target domain.  
The reviewed papers show a trend to use transfer learning in computer vision applications, often by means of predefined pretrained CNNs. The usage of these models is very convenient and it simplifies the adoption of the CNN models. Nevertheless, these pretrained models can be used as they are or in conjunction with transfer learning. A typical workflow in transfer learning consists in taking a pretrained network, excluding the top layers, defining some new fully connected layers that depend on the target application, and performing training with the new dataset and the base model frozen. Optionally, a final fine-tuning of the entire network with a small learning rate can be performed. Figure 13 shows the usage of predefined pretrained architectures across the inspected papers.  
An interesting aspect of transfer learning usage in agriculture is that it helps to overcome situations with a scarcity of data for some specific crop diseases. As an example, in [229], [PERSON] et al. exploit a crafted dataset of generic disease spots of different shapes and sizes on jujubes, to obtain parameters that can be imported inside the target network. In [230], [PERSON] et al. propose VggNet, freezing the weights in the convolutional layers for feature extraction. In general, networks that are pretrained with huge public datasets are used to retain the learned weights. Transfer learning is applied by replacing or adding small parts of the network that are consequently retrained. This methodology can be used to train models used to support disease identification [231; 232; 233; 234; 235; 236; 237] on different kind of crops such as tomato [54; 238; 239], pepper [240], soybean [241], rice [242; 243], groundnut [244], cotton [245], grapes [246], fruits [247; 248], citrus [249], palm [250],  
Figure 13: Histogram showing the usage of predefined CNN architectures across the inspected works; different versions of the same network are grouped under a generic label.  
and cordia [251]. Other applications include rust detection in different crops [252], tea stress detection [253], and grapevine digital ampleography [254]. However, when using these methods, choosing the right network architecture can also be challenging: in [255], a benchmark of 38 different models is provided, reporting the accuracy score for each one. Also, other works report similar benchmarks, each one with its own outcomes, based on the specific domain and dataset used [256; 257; 258; 259; 260; 261; 262]. In [263], comparisons are conducted using both ImageNet and a plant dataset for pretraining, in order to verify the effect on the final accuracy. Instead, [PERSON] et al. in [264] discuss the usability of a network initially trained in a different domain (domain shifting), and [PERSON] et al. in [265] improve the correlation between the mixed domain images. Another application is presented in [266], where transfer learning is used to produce synthetic multi-spectral images from an RGB camera, while [PERSON] et al. in [267] discuss its usage along with pruning, a method that consists in removing unimportant parameters from the CNN, without a significant drop in accuracy.