Multi-scale data fusion and intelligent identification algorithm of soil fertility status in Zanthoxylum Bungeanum plantation

In recent years, with the growth of image processing technology and the continuous optimization of image acquisition systems, computer vision based image analysis methods have received widespread attention and application in multiple fields, including the important direction of soil science. Soil fertility is a key factor affecting crop yield, but traditional agricultural production has a low level of automation, making it difficult to achieve precise control over the application of fertilizers and pesticides, and lacking real-time online monitoring and evaluation mechanisms for soil fertility. This not only causes resource waste, but also has adverse effects on crop growth and the ecological environment. This paper proposes a multi-scale data fusion and intelligent recognition system based on big data and deep learning (DL) for monitoring the soil fertility status of Zanthoxylum Bungeanum plantations. This system combines Internet of Things (IoT) technology to comprehensively collect multi-dimensional and multi-scale data of soil, and uses DL algorithm to intelligently identify the soil fertility status. Finally, the analysis results are fed back to users in real time. The results indicate that the system exhibits higher accuracy and reliability in identifying soil fertility status, providing strong support for achieving precision agriculture.