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Machine Learning Approaches for Agro-IoT Systems
Published in Saravanan Krishnan, J Bruce Ralphin Rose, N R Rajalakshmi, Narayanan Prasanth, Cloud IoT Systems for Smart Agricultural Engineering, 2022
Once the crop starts to give a good yield the next danger the farmers have to face is various types of attacks. It is a very common practice to stay awake day and night to look after crops till they are harvested. Famers have to protect their crops from various animals and birds; furthermore, humans are also harmful as they can destroy the crops by burring out the land out of any personal rivalry or by stealing the crops. Traditionally farmers used electric fencing for preventing animal attach but it was made illegal as it had a high risk of animal or human death. Image sensor along with proximity sensor is the best to answer for this, were the proximity sensor can detect the presence of any object within marked area and image sensor will help to identify that object, thus making farmer attentive and ready to take quick action. A flame detector, a type of fire sensor, is moreover accurate and responds faster, making it more reliable to identify a fire in the field.
IR Sensors for Indoor Monitoring
Published in Moeness G. Amin, Radar for Indoor Monitoring, 2017
The flame detection system, unlike conventional smoke detectors, is capable of detecting flame in open spaces or large rooms in a few seconds after the flame is visible. Response time of the conventional detectors depends on the fire area, the distance to the fire, and where they are used, that is, indoor or outdoor and are much higher in comparison to that of the PIR sensor-based system. The detection range of the PIR sensor is 5 m, which enables covering a 10 m × 10 m room using a single sensor.
Design of a small wheel-foot hybrid firefighting robot for infrared visual fire recognition
Published in Mechanics Based Design of Structures and Machines, 2023
Anfu Guo, Tao Jiang, Junjie Li, Yajun Cui, Jin Li, Zhipeng Chen
The main purpose of flame detection is to accurately locate the source point of the fire without being affected by interfering sources such as smoke and dust. Therefore, many scholars have focused on recognition algorithms. For example, Kim et al. (Kim, Keller, and Lattimer 2013; Kim, Jo, and Lattimer 2016; Kim and Lattimer 2015; Kim, Starr, and Lattimer 2015) developed a real-time probabilistic classification algorithm for identifying fire, smoke, their thermal reflections, and other objects in infrared images. The algorithm uses a Bayesian classifier to probabilistically classify multiple classes and multiple-goal genetic-algorithm optimization to investigate the appropriate combination of features that have the lowest errors and the highest performance. Tsai et al. Tsai, Huang, and Lin Tsai, Huang, and Lin (2011) proposed an efficient coarse-grain parallel deoxyribonucleic acid (PDNA) to search for the global optimum of the redundant inverse kinematics problem with minimal movement. Compared with traditional genetic algorithms, it showed merit and superiority in terms of firefighting. Freire et al. (Freire et al. 2013) proposed a local data fusion algorithm based on luminosity, temperature, and flame. Experiments showed that the algorithm could effectively detect the occurrence of fires. Marbach et al. Marbach, Loepfe, and Brupbacher Marbach, Loepfe, and Brupbacher (2006) also presented an image processing technique for automatic real-time fire detection from video images to detect fires effectively.
Pre-ignition detection and early fire detection in mining vehicles
Published in Mining Technology, 2021
The heat release rates of the three specimens can be found in Figure 9. The rapid increase to higher levels of heat release rate of the electrical cable and the hydraulic hose further emphasizes the need for early detection. Even though the cab interior specimen displays a lower peak heat release rate, the specimen displays a more rapid fire growth rate compared with the cable and the hose (continuing from the pre-ignition phase). This very rapid fire growth rate could be taken advantage of, applying heat sensors or possibly flame detectors in the cab compartment. As mentioned earlier, rate-of-rise type heat detectors could fit the rapid fire growth rate very well. A flame detector would have the advantage of instantaneous detection – not depending on any transport time – which could lead to a faster detection than a CO sensor in the pre-ignition phase. The suitability of a flame detector in the case of cab fires is in line with the cab fire experiments conducted by De Rosa and Litton (2010). A flame detector in the experiments was found to efficiently and rapidly small pool fires – simulating spray fires – in the cab compartment. Despite the somewhat slower fire growth rates of the cable and the hose, the distinctly increasing heat release rates of the two specimens to higher levels could also be used for heat detection in a post-ignition scenario.