## 2 Related Works
Our proposed work does not involve an external PC, but controls are done using an embedded low-cost system. The research in [15] uses histograms to classify pictures and is less accurate in classification. Our proposed work classifies a food item by a deep learning model based on CNN and determines the food's target temperature based on its previous experience. The approach suggested also has the ability to learn new food items and to store their target temperature in the database. As a result, the microwave slowly becomes more knowledgeable in suggesting the target temperature of food items that are used in that house. The proposed microwave oven shows a real-time high-resolution thermal image of the food and shuts down the heating automatically when the food hits the target temperature. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Work** & **Contactless Temperature** & **Automatic Heating** & \begin{tabular}{c} **Automatic Target** \\ **Temperature** \\ **Recommendation** \\ \end{tabular} & \begin{tabular}{c} **Ability to Learn** \\ **Target Temperature** \\ **of New Food’s** \\ \end{tabular} & \begin{tabular}{c} **Real-Time** \\ **Thermal** \\ **Imaging** \\ \end{tabular} & \
\begin{tabular}{c} **Notification in** \\ **Smarphone** \\ \end{tabular} \\ \hline [PERSON] et al. [7] & Yes, InfraRed (IR) & No & No & No & No & No \\  & fiberoptic radiometer & & & & & \\ [PERSON] et al. [8] & Yes, Forward-Looking &
\begin{tabular}{c} A Personal Computer \\ (PC) Controls power \\ level \\ \end{tabular} & No & No, apple slices only & Yes & No \\  & & & & & & \\ [PERSON] et al. [9] & Yes, FLIR camera & No & No & No & Yes & No \\ [PERSON] et al. [10] & Yes, Charge-Coupled & No & No & No & No & No \\  & Device (CCD) camera & No, Resistance & & Yes, for a set of & No & No & Yes \\ June [PERSON] [14] & Temperature Detector & & & & & & \\  & (RTD) temperature sensor & & & & & & \\ [PERSON] [15] & Yes, the IR temperature sensor & & & & & & \\  & & & & & & \\  & & & & & & & \\ [PERSON] [16] & Yes, \(8\times 8\) IR temperature & & & & & & \\  & sensor grid & & & & & & \\  & & & & & & \\  & & & & & & \\ Proposed & Yes, using a FLIR camera & Yes, an embedded & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \end{tabular}
\end{table} 
Table 1: Comparison with other works. The research in [14] uses a \"Food ID\" feature, which automatically recognizes a collection of predefined foods using image processing. Nevertheless, new food products and their target temperatures cannot be learned. Whenever the food is ready it also uses a mobile notification system. It should be noticed that the research in [14] is an oven for food cooking, and it is not a microwave oven for food heating. It can take hours to cook food in the oven and mobile notification is well justified. Nonetheless, usually, it does not take more than a few minutes to heat up food using a microwave, so the user can stay close to the microwave until the food is ready. So a notification feature for a smartphone could be an overdesign. The food temperature is calculated in this proposed research using a FLIR thermal camera mounted on the top of the microwave roof outside the cavity of the oven using a 4 mm hole. It is microwave insensitive and does not require physical contact with the food. In this research microcontroller-based embedded system also implements the automatic shutdown of microwave power when the food temperature reaches the target temperature. The microwave includes an LCD with a touchscreen and displays the colored thermal image of the food while the food is being heated in real-time. The temperature of the food is computed by contouring the thermal image. In addition, the proposed microwave captures a food image using a Complementary Metal-Oxide-Semiconductor (CMOS) camera (Raspberry Pi, UK) and automatically classifies the food. The proposed CNN-based image classification method gradually learns the food items through real-time retraining of a deep learning model and recommends their target temperatures.