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In recent years, the Internet of Things has flourished, whether it is to bring convenience to people's lives, improve the quality of life, or in industry, let people manage and maximize benefits more effectively. With the influence of global warming, the climate has become unstable, which makes it impossible for us to use common sense to infer the weather, especially the change of the weather has a great impact on crops, so we also pay more attention to the professional technology of greenhouse cultivation in agriculture. . This research uses NB-IoT communication module with a variety of sensors, including temperature and humidity, light and soil humidity, to monitor the small smart greenhouse. The sensed data is visualized and recorded on the network platform, and then the sensed environmental data will be analyzed by Matlab for fuzzy fuzzy control, so that the load equipment can make corresponding actions with the data changes in the greenhouse. It can be controlled through the conditions set on the network platform to achieve a two-way control smart greenhouse. In the aspect of image recognition in this study, the system uses the YOLOv4 neural network model for training to identify custom pests, and this study takes the litchi stink bug, a common agricultural pest in Taiwan, as the identification object, and then makes the best treatment method to solve this problem. class problem.
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