It is of great significance to obtain soil texture information quickly for the realization of farmland management. Soil with good particle condition can well regulate the needs of plants for water, nutrients, air, and temperature during crop growth, thereby promoting high crop yields. The existing methods of measuring soil texture cannot meet the requirements of time and spatial resolution. For this reason, a vehicle-mounted soil texture detector was designed and developed based on machine vision and soil electrical conductivity devices. The detector does not require pretreatment such as air-drying and screening of the soil, and completely uses the original information of the farmland. The whole process can obtain the soil texture information in real time, omitting the complicated chemical process, and saving manpower and material resources. The vehicle-mounted detector is divided into a mechanical part, a control part, and a display part. The mechanical part provides measurement support for the acquisition of soil texture information; the control part collects and processes signals and images; the measurement results can be intuitively observed and recorded on the display, and can be operated through the mobile phone. The vehicle-mounted detector obtains soil conductivity through 4 disc electrodes, while the vehicle-mounted industrial camera captures the soil surface image, and extracts texture parameters through image processing, takes EC and texture parameters as input, and the embedded SVM model of the instrument was used to perform soil texture prediction. In order to verify the measurement accuracy of the detector, farmland verification experiments were carried out on farmland loam in Tongzhou District and Haidian District of Beijing. The R2 of the correlation between the measured value of soil EC and the actual value was 0.75, and the accuracy of soil texture prediction was 84.86%. It shows that the developed vehicle-mounted soil texture detector can meet the requirements for rapid acquisition of farmland texture information.

Soil texture is one of the most important soil characteristics that affect soil properties. Rapid acquisition of soil texture information is of great significance for accurate farmland management. Traditional soil texture analysis methods are relatively complicated and cannot meet the requirements of temporal and spatial resolution. This research introduced a self-developed vehicle-mounted in-situ soil texture detection system, which can predict the type of soil texture and the particle composition of the texture, and obtain real-time data during the measurement process without preprocessing the soil samples. The detection system is mainly composed of a conductivity measuring device, a camera, an auxiliary mechanical structure, and a control system. The soil electrical conductivity (ECa) and the texture features extracted from the surface image were input into the embedded model to realize real-time texture analysis. In order to find the best model suitable for the detection system, measurements were carried out in three test fields in Northeast and North China to compare the performance of different models applied to the detection system. The results showed that for soil texture classification, ExtraTrees performed best, with Precision, Recall, and F1 all being 0.82. For particle content of soil texture prediction, the R 2 of ExtraTrees was 0.77, and RMSE and MAPE were 74.72 and 39.58. It was observed that ECa, Moment of inertia, and Entropy had larger weights in the drawn model influence weight map, and they are the main contributors to predicting soil texture. These results showed the potential of the vehicle-mounted in-situ soil texture detection system, which can provide a basis for fast, cost-effective, and efficient soil texture analysis.