The Role of CNN in Soil Detection

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Soil is an important part of agricultural production and environmental health, and its physical and chemical properties directly affect crop growth and ecosystem stability. However, traditional soil testing methods such as gravimetric and time-domain reflectance (TDR) techniques, although accurate, have limitations such as being time-consuming, costly and complicated to maintain. To improve the quality of soil, the researchers proposed the method of combining Convolutional Neural Network (CNN) convolutional models with soil detection. In this paper, this will be collated and the measurements will be classified into three categories: soil water content detection based on CNN, soil contamination detection based on CNN, and soil microbial detection based on CNN models. Such classification can be effectively combined with hybrid models such as CNN, Gate Recurrent Unit (GRU) and Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), among others. The results show that the hybrid model has higher accuracy and stability in predicting soil water content, microbial activity and heavy metal contamination, which helps to improve soil health monitoring and management. Future research can further combine multiple models to improve the accuracy and application range of soil testing.