In the process of rice straw nutrient bowl tray drying, real-time detection of changes in moisture content to achieve automatic adjustment of drying factors is one of the important means to ensure its drying quality. At present, the main method for measuring the moisture content of rice straw nutrient bowl trays is the drying and weighing method. This method is not only time consuming, labor intensive, and complex to operate, but also has poor real-time performance, which cannot meet the demand for real-time detection of the moisture content in the production process of rice straw nutrient bowl trays. In this regard, a real-time moisture content detection method for rice straw nutrient bowl trays based on hyperspectral imaging technology was studied. In this study we took the rice straw nutrient bowl tray during the drying process as the research object, adopted a single factor experiment, took microwave power, hot air temperature, and hot air speed as the drying factors, and took the moisture content of the rice straw nutrient bowl tray as the drying index. The rice straw nutrient bowl tray was dried according to the designed drying conditions. When drying, we removed the rice straw nutrient bowl tray every 5 min for weighing and collected hyperspectral image data within the wavelength range of 400~1000 nm until its quality remained unchanged. A total of 204 samples were collected. Using the average spectrum of the region of interest as the sample for effective spectral information, spectral preprocessing was performed using multivariate scattering correction (MSC), standardization normal variables (SNV), and Savitzky–Golay convolution smoothing (SG) methods. Principal component analysis (PCA) and competitive adaptive reweighting (CARS) methods were adopted for the dimensionality reduction of the spectral data. Three prediction models of rice straw nutrient bowl tray moisture content, namely random forest regression (RF), particle swarm optimization support vector regression (PSO-SVR), and XGBoost model were constructed using the reduced dimension spectral data. Finally, the performance of the model was compared using the coefficient of determination (R²) and mean square error (RMSE) as evaluation indicators. The research results indicate that the PCA-PSO-SVR model established based on SG method preprocessing has the best predictive performance, with a training set decision coefficient R²C of 0.984, a training set mean square error RMSE-C of 2.775, a testing set decision coefficient R²P of 0.971, and a testing set mean square error RMSE-P of 3.448. The model therefore has a high accuracy. This study achieved rapid detection of water content in rice straw nutrition trays. This method provides a reliable theoretical basis and technical support for the rapid detection of rice straw nutrient bowl tray moisture content, and is of great significance for improving the quality of rice straw nutrient bowl trays; promoting the popularization and application of raising rice straw nutrient bowl tray seedlings and whole process mechanized planting technology system; improving soil structure; and protecting the ecological environment.

The foundation of robot autonomous movement is to quickly grasp the position and surroundings of the robot, which SLAM technology provides important support for. Due to the complex and dynamic environments, single-sensor SLAM methods often have the problem of degeneracy. In this paper, a multi-sensor fusion SLAM method based on the LVI-SAM framework was proposed. First of all, the state-of-the-art feature detection algorithm SuperPoint is used to extract the feature points from a visual-inertial system, enhancing the detection ability of feature points in complex scenarios. In addition, to improve the performance of loop-closure detection in complex scenarios, scan context is used to optimize the loop-closure detection. Ultimately, the experiment results show that the RMSE of the trajectory under the 05 sequence from the KITTI dataset and the Street07 sequence from the M2DGR dataset are reduced by 12% and 11%, respectively, compared to LVI-SAM. In simulated complex environments of animal farms, the error of this method at the starting and ending points of the trajectory is less than that of LVI-SAM, as well. All these experimental comparison results prove that the method proposed in this paper can achieve higher precision and robustness performance in localization and mapping within complex environments of animal farms.