THE MONITORING AND PREDICTING MODEL FOR HIGHLY RESILIENT AND ACCURATE SMART FARMING USING DEEP LEARNING TECHNIQUE BASED ON BIG DATA ANALYTICS TOGETHER WITH INTERNET OF THINGS
Keywords:
Monitoring , Predicting Model, Smart Agriculture, Deep Learning, Big Data, Internet Of ThingsAbstract
The purposes of this research are 1) To develop the monitoring and predicting model for highly resilient and accurate smart farming using deep learning technique based on big data analytics together with the Internet of Things and 2) To evaluate the performance of the monitoring and predicting model for highly resilient and accurate smart farming. This research is divided into three parts: Part 1 involves the creation of a high-precision smart agriculture monitoring and prediction process. Part 2 focuses on building and evaluating the model's efficiency. Lastly, Part 3 involves the development of an application. The research findings reveal that the high-resilience and highly accurate smart agriculture monitoring and prediction model, using deep learning techniques in conjunction with big data analytics and IoT, consist of two main stages: 1) data collection through IoT and 2) model creation and evaluation. The researchers constructed a Long Short-Term Memory (LSTM) model using environmental factors and plant growth factors to predict powdery mildew occurrences. The evaluation of the model showed a loss value of 0.0047 and an evaluation value of 0.0012877240078523755.
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Copyright (c) 2024 Sumran Chaikhamwang , Surasak Mungsing , Prasong Praneetpolgrang (Author)
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