Research on Intelligent Seedling Environment Monitoring And Early-warning System Based on Machine Learning Algorithm

With the rise of intelligent agriculture, the intelligent transformation of agriculture has been realised, bringing new development to agricultural production. However, in traditional agriculture, the regulation of seedling growth conditions mainly relies on manual management, and it is difficult to accurately predict and monitor complex ecological changes manually, which increases operational uncertainty and leads to economic losses brought about by low production efficiency. Therefore, this study is based on the machine learning-based environmental monitoring and warning system for seedlings, introducing the hardware design, technical framework, machine learning algorithms in the random forest and convolutional neural network applications. Through the experimental data, the speed and accuracy of the system in environmental abnormality detection are verified. The experimental results show that the system can effectively warn of environmental risks, provide scientific basis and reduce unnecessary losses. In the future, with the promotion and use of the system, it can greatly promote the effective combination of artificial intelligence and intelligent agriculture, and promote the sustainable development of agriculture.