## 2 Deep Learning Models
### Deep Learning Models Constraints  
Typically, these deep learning models are trained and deployed on GPUs because of their computing power requirements. Infield training and/or deployment of such models in low-cost equipment are therefore subject to some main constraints: the memory, the computing power, and the energy.  
Memory: The most obvious bottleneck for DL is the working memory, but also the storage space, especially when working with low resource devices. Models commonly require storage space in the order of hundreds of MB due to their complex representation, far above 32 MB offered by IMote 2.0 or 64 MB proposed by Arduino Yun [19]. From a practical point of view, this inflates sensor apps dramatically and usually dwarfs application logic [20]. Concerning the runtime requirements, the required working memory at peak time will often preclude a particular platform from using a model even if this platform has the necessary storage space to hold the model representation. It is not uncommon for a single model layer to require tens or hundreds of MBs and this might be 90% more than the average layer consumption within the model. In such cases, manual customization of the model is necessary before it runs completely up to the final layer.  
Computing power: Models such as VGGNet, even when carefully implemented on embedded platforms, have proven to take a couple of minutes for a single image due to the paging required to overcome a large memory footprint [20]. Likewise, despite their high computing power, devices such as smartphones present challenges in running deep models with latency tailored to user interaction. This typically leads to the use of models that offload their computation to the cloud, or at least to a GPU. Computational requirements are also strongly dependent on the architecture of the model, with some layers such as convolutional layers being much more related to computation than feed-forward layers (which are more related to memory). As a result, simple architecture changes can have a significant impact on the running time.  
Energy: Related to computation, the power consumption of deep models due to the excessive running time for a single inference can be very expensive for equipment used in continuous monitoring. In addition, the access to a power source in remote areas such as farms is not always guaranteed. That is why equipment are usually battery powered. The limited energy of battery therefore requires the minimization of the power consumption of deep learning models.  
To overcome present challenges and to allow the deployment of DL models on resource limited equipment require the compression of models.  
\begin{table}
\begin{tabular}{c c c} \hline
**Tasks** & **Models Used** & **Reference** \\ \hline Plant recognition & AlexNet & [21; 22] \\ \hline Plant disease detection & AlexNet and Inception & [23] \\ \hline Tomato fruit counting & Inception-ResNet & [6] \\ \hline Plant species classification & VGG16 & [24] \\ \hline Mixed crops semantic segmentation & VGG16 & [25] \\ \hline Obstacles detection in the farm & VGG16 and AlexNet & [26] \\ \hline Fruit detection & Faster R-CNN and VGG16 & [27; 28] \\ \hline Mango fruit detection and yield estimation & Faster R-CNN & [29] \\ \hline Plant disease recognition & CaffeNet & [30] \\ \hline \end{tabular}
\end{table}
Table 1: Some work using state-of-the-art models in agriculture.