Autonomous Vision-Based Fire Detection in Rice Farms: A Solar-powered IoT System Utilizing Hybrid CNN LSTM Networks
Keywords:
CNN-LSTM, Edge computing, Fire detection, GSM notification, IoT, Rice farm monitoringAbstract
Agricultural fires in rice paddies that burn out of control are a major hazard to agricultural production and rural lives globally. Standard fire detection systems, based on point sensors, smoke and heat, ineffectively detect fire in open field conditions where wind currents easily carry signals away. This work suggests a solar energy self-governing Vision-Based Fire Detection (VBFD) system designed for an environment of remote monitoring of a rice farm. This system has a hybrid spatial-temporal architecture. To eliminate the effect of the environment, a CNN is used to extract the spatial features from the images by performing the convolution task, and to distinguish image details, an LSTM algorithm is used to perform the temporal (temporal “flicker”) feature extraction task. The system runs on a “Raspberry Pi 4B” edge computing platform, which uses on-device inference to eliminate latency and reliance on the cloud. The study evaluated a custom-trained CNN using a validation set of 199 images and compared it to a pretrained CNN with a MobileNetV2 backbone. Experimental results show that the custom CNN outperforms the MobileNetV2 model with a better overall correctness of 97% and a fire detection recall of 0.98 in comparison to the MobileNetV2 with 93% accuracy and 0.92 recall. The custom model’s performance in identifying a fire with a high probability when it results in a false negative only 2% of the time is crucial to minimizing the effects of catastrophic losses. It also has an integrated SIM800L GSM module that allows immediate SMS notification sending and an integrated solar-powered UPS system for 24/7 off-grid operation. The study delivers a scalable, high-recall, early warning solution that can play a substantial role in current Climate Smart Agriculture. The present study can be perceived as an effective, cost-effective tool for ensuring safe food production in high-risk areas.
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