The Research on Non-Destructive Testing Method of Rice Nutrients Based on Deep Learning

With the increasing demand for food worldwide, the development of precision agriculture technology has become particularly important. This article aims to achieve efficient management of rice nutrients by using a handheld vertical soil testing instrument to efficiently manage rice soil data. This article uses deep learning to process and analyze key parameters such as soil pH (Pondus Hydrogeni), temperature, and humidity, in order to predict nutrient concentrations. On this basis, this article proposes a real-time feedback mechanism that achieves continuous optimization of prediction results through learning and learning of neural networks. We noticed that the pH value fluctuated between 6.5 and 6.7, which is a relatively stable range, indicating that there was no significant change in the acidity or alkalinity of the water body during this period. The application of this technology can effectively increase the nutrient content of soil, reduce physical interference to soil, and maintain the integrity of soil structure. At the same time, it can also achieve the goals of sustainable agricultural development, reducing environmental pollution, and improving the ecological environment. In summary, this study demonstrates the potential application of deep learning technology in the field of agriculture, particularly providing new ideas and methods in precision agriculture and environmental protection. In the future, the further improvement and promotion of this technology is expected to promote agricultural modernization on a global scale, achieving efficient and sustainable food production.