## 5 Discussion  
The CNN based deep learning model is developed considering the fact that it will be trained in an embedded system in real-time, rather than in a high-performance graphics processing unit (GPU) based computer. So, the model capacity is kept relatively low, so that the memory and speed of RPi are sufficient to train the model. From Table 1, we see that the training time increases with the increase of classes as the total number of training and validation samples increases. This training happens in a separate background process, thus the normal operation can continue while training is going on.  
It is also possible to send image data to a cloud using the Internet and train the model in a cloud [46], where training can be done quickly by using GPU based computers. We plan to explore this possibility in the future.  
Microwave food cover is sometimes used to eliminate the splattering of food inside a microwave oven to keep it clean. As the proposed microwave captures images to classy the food, opaque food cover cannot be used in this proposed system. Rather, transparent food covering [47] needs to be used.  
The microwave oven has an inherent property of non-uniform heating [7]. The thermal image shows the areas where the food is hot and where it is relatively cold, thus the user can have a good idea about the distribution of the heat on the food, and start eating from the desired portion of the food. Moreover, the realtime display of thermal images adds artistic value which will attract customers.  
Note that the two cameras are not placed inside the oven cavity. As shown in Figures 1 and 8, they are placed at the outside of the oven cavity on the microwave oven's roof where the cameras can sense the light and the heat through two small 4-mm holes. Thus, their bodies are not directly exposed to the microwaves, except for the lenses. The microwave has a wavelength of around 120 mm [29], visible light has a wavelength of about 380 nm to 740 nm, and infrared has a wavelength in the range of 1-750 nm [30]. As the microwaves are long waves, they do not pass the 4-mm hole and do not damage the camera. The visible light and IR waves can pass through the hole and thus images can be captured, and the food temperature can be sensed. If the food explodes, then the food particles that only penetrate the two 4 mm holes may put dirt on the lenses. Transparent microwave food cover [47] can be used to eliminate the splattering of food inside a microwave oven to keep it clean.  
The RPi 3 uses a Broadcom BCM2837 SoC that has a 1.2 GHz 64-bit ARM Cortex-A53 core, while the Raspberry Pi Zero W (RPZW) uses a Broadcom BCM2835 SoC that has a 1 GHz 32-bit ARM1176 JZF-S core. The speed of the RP3 is 20% faster than the RPZW. The RPZW only has a single-core, meaning that it contains only one core to process different operations. With RP3's quad-core, it has four cores to process different operations; thus, it can multitask. Their memory (SDRAM) differs, as the RP3 has a 1 GB memory and the RPZW has a 512 MB memory [48]. In this application, we need a faster processor to train the model in a short time, need multitasking ability to train the model at the background  
Figure 11: Training and validation history plots when the total class is 10: (a) Loss vs. epochs; (b) Accuracy vs. epochsprocess while the normal operation of the microwave can go on, and need more memory to increase the batch size while training the model. This makes the RP3 a better option compared to RPZW, which is faster, multitasking enabled quad-core processor and has more memory to perform the proposed deep learning task more efficiently.  
The microwave oven contains a cooling fan as shown in Figure 5, and it is activated by the RPi whenever the microwave heating is turned on. Thus, the cooling fan runs as long as the heating is going.