Intelligent monitoring of soil moisture in agricultural fields based on polymer fibre Bragg grating sensors

As artificial intelligence and machine learning technology rapidly develop, intelligent monitoring of agriculture has gradually become a research hotspot in agriculture. As one of the key factors affecting the growth of crops, the accurate monitoring of soil moisture is of pivotal importance for agricultural production. Therefore, the study designed a new polymer fiber Bragg grating sensor and combined the sensor with deep neural network algorithm to propose a novel intelligent monitoring method of soil moisture in farmland. The experiment findings denote that the performance of the research method is raised by about 10% to 25% compared with the remaining state-of-the-art methods. It is shown that the combination of polymer fiber Bragg grating sensor and deep neural network algorithm can effectively enhance the model's adaptability and prediction ability for complex soil moisture distribution. In addition, the maximum soil moisture measurement time of the research method in the training set and test set is only 10.2 s and 9.7 s. The research method not only has the advantage in the accuracy of soil moisture measurement in farmland, but also performs well in the processing efficiency, which can quickly and accurately monitor soil moisture and has a wide range of application prospects.