Artificial intelligence and machine learning for the green development of agriculture in the emerging manufacturing industry in the IoT platform
Yuanyuan Zhou | Qing Xia | Zichen Zhang | Mengqi Quan | Haoran Li
In recent years, greenhouse development has been innovative in agriculture based on information systems guidance with accelerated growth. The IoT provides an intelligent system and remote access technologies such as green infrastructure. The usability of information systems for effective training and producing intelligent systems and predictive models in organizational real-time based on machine learning and artificial intelligence (AI). Therefore, a Remote Sensing Assisted Control System (RSCS) has been proposed for improving greenhouse agriculture requirements. This proposed method utilizes artificial intelligence and machine learning technology for the green development potential industry's ability to manage economic resources and increase innovative agriculture product development patterns. Thus, the key preconditions for increasing healthy food choices and promoting local and global organic farmers' potential development are straightforward suggestions for developing an effective marketing strategy. The experimental results RSCS the highest precision ratio of 95.1%, the performance ratio of 96.35%, a data transmission rate of 92.3%, agriculture production ratio of 94.2%, irrigation control ratio of 94.7%, the lowest moisture content ratio of 18.7%, and CO2 emission ratio of 21.5%, compared to other methods.
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