Abstract: As the cornerstone of agricultural production, soil provides various nutrients and moisture essential for crop growth, directly influencing crop yield and quality. Field soil information management constitutes a pivotal component in the development of modern agriculture. Currently, manual soil sampling coupled with offline parameter testing remains the primary approach in field management, which is plagued by issues such as high labor costs, intensive workload, and inefficiencies in collection and measurement. To mitigate these challenges and enhance the cost-effectiveness and efficiency of field management, a soil automatic sampling and real-timesoil parameter detection device was developed in this study. Furthermore, a prediction method based on the BPNN (Backpropagation Neural Network) is proposed for estimating soil firmness and mass moisture content. Firstly, based on the requirements for automatic soil sample collection and parameter measurement, a direct-pressure, two-stage, and step-by-step soil sampling and inspection mechanism, an unloading mechanism, and an indexing soil sample collection mechanism were designed. These mechanisms were analyzed and verified, resulting in the determination of a 400mm movement stroke and a maximum soil penetration thrust of 800 N. A dual-layer control system architecture was also established, utilizing the Jetson TX2 embedded computer and STM32F3 series microcontroller, integrated with GNSS positioning. This system enabled functions such as automatic soil sampling, autonomous navigation, information recording and transmission, soil sampling self-protection, and dynamic prediction of soil firmness and mass moisture content. Secondly, a three-layer BPNN neural network prediction model was constructed to establish a regression relationship between easily measurable parameters including volumetric water content, soil sampling current, sampling speed, and sampling depth, with soil firmness and mass water content. The model was trained and tested using 275 experimental samples, resulting in an optimal number of hidden layer nodes determined as 10. The average percentage errors for the predictions of soil firmness and mass moisture content were 7.744% and 1.531%, respectively. Finally, to validate the machine's overall performance, field tests were conducted at 10 sampling points along an inspection path in an orange grove. The evaluation criteria included machine sampling time, soil penetration depth of the temperature and humidity sensor probe, absolute error of soil weight measurement, and relative errors of predicted soil firmness and mass moisture content. The results indicated that the average soil sampling time per operation was 60.5 s, the average soil penetration depth of the sensor probe was 64.7 mm, the average absolute error of soil sample weight measurement is 1.53 g. and the average relative errors of predicted soil firmness and mass moisture content at the 10 sampling points were 6.37% and 5.00%, respectively. These results satisfied the requirements for soil sampling and parameter detection. Additionally, by combining geographical location information, a field distribution map of soil firmness and mass water content was provided. This study provides a reference for intelligent soil collection, real-timeparameter detection, and visual management of field soil information distribution.