Forest fires are caused naturally by lightning, high atmospheric temperatures, and dryness. Forest fires have ramifications for both climatic conditions and anthropogenic ecosystems. According to various research studies, there has been a noticeable increase in the frequency of forest fires in India. Between 1 January and 31 March 2022, the country had 136,604 fire points. They activated an alerting system that indicates the location of a forest fire detected using MODIS sensor data from NASA Aqua and Terra satellite images. However, the satellite passes the country only twice and sends the information to the state forest departments. The early detection of forest fires is crucial, as once they reach a certain level, it is hard to control them. Compared with the satellite monitoring and detection of fire incidents, video-based fire detection on the ground identifies the fire at a faster rate. Hence, an unmanned aerial vehicle equipped with a GPS and a high-resolution camera can acquire quality images referencing the fire location. Further, deep learning frameworks can be applied to efficiently classify forest fires. In this paper, a cheaper UAV with extended MobileNet deep learning capability is proposed to classify forest fires (97.26%) and share the detection of forest fires and the GPS location with the state forest departments for timely action.

Reuse of magnesium alloy is an essential approach to protecting the natural resources, and it eliminates the waste products dumped in landfills, water, and air. Therefore, the first time the scrap magnesium alloy materials are collected and fabricated, the newly formulated magnesium composites use different techniques, such as vacuum, squeeze, and stir casting. AZ91 alloy from the automobile scrap is used as matrix material, and silicon carbide (SiC) is used as reinforcement. Electroless nickel-phosphorous coating is applied to the ceramic particles to avoid unwanted chemical reactions during the casting process. The adhesion between the matrix and reinforcement is improved by masking the surface of the nonmetallic SiC particles. Magnesium composite is most commonly used in automotive and aerospace applications to reduce weight and improve the strength of the component. The magnesium composite is fabricated through four different methods, and the substrates are tested and analyzed for better results. The sample results taken from the composites are compared to the magnesium alloy obtained from the scrap stock. Comparatively, the substrate produced using squeeze casting shows a lower porosity level of 5.5 %, and it is clearly shown in optical images. Significant improvements in the mechanical properties, such as hardness and compression strength, are obtained, and the wear rate in the prepared composites is reduced to 28 % for the sample produced using squeeze casting instead of the vacuum and stir casting processes.