ABSTRACT

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.

摘要

近年来，温室大棚开发在农业领域以信息系统引导为基础，加速发展。物联网提供智能系统和远程访问技术，例如绿色基础设施。基于机器学习和人工智能 (AI) 的组织实时有效训练和生成智能系统和预测模型的信息系统的可用性。因此，已经提出了一种遥感辅助控制系统（RSCS）来改善温室农业的要求。该方法利用人工智能和机器学习技术提高绿色发展潜力产业管理经济资源和增加创新农产品开发模式的能力。因此，增加健康食品选择和促进当地和全球有机农民潜在发展的关键先决条件是制定有效营销战略的直接建议。实验结果 RSCS 最高精确率为 95.1%，性能比为 96.35%，数据传输率为 92.3%，农业生产率为 94.2%，灌溉控制率为 94.7%，最低含水率为 18.7%，和一氧化碳₂排放比为21.5%，与其他方法相比。