An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first, the two key steps of guided filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then, the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image-processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of $0.094\mathrm{s}$ in comparison with HSV, HIS, and 2R-G-B color spaces. The guided filtering method can effectively distinguish the boundary between the tillage soil compared to other competing vanilla methods such as Tarel, multi-scale retinex, wavelet-based retinex, and homomorphic filtering in spite of having the fastest processing speed of $0.113\mathrm{s}$ . The extracted soil boundary line of the improved anti-noise morphology algorithm has the best precision and speed compared to other operators such as Sobel, Roberts, Prewitt, and Log. After comparing different sizes of image templates, the optimal template with the size of $140\times 260$ pixels could achieve high-precision vision navigation while the course deviation angle was not more than $7.5°$ . The maximum tractor speed of the optimal template and global template were $51.41\mathrm{km}/\mathrm{h}$ and $27.47\mathrm{km}/\mathrm{h}$ , respectively, which can meet the real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the tillage soil boundary line extracted by the proposed improved anti-noise morphology algorithm, which has broad application prospect.

To satisfy the increasing demand for soybeans, identifying and sorting high-vigor seeds before sowing is an effective way to improve the yield. Polarized hyperspectral imaging (PHI) technology is here proposed as a rapid, non-destructive method for detecting the vigor of naturally aged soybean seeds. First, the spectrum of 396.1–1044.1 nm was collected to automatically extract the region of interest (ROI). Then, first derivative (FD), Savitzky–Golay (SG), multiplicative scatter correction (MSC), and standard normal variate (SNV) preprocessed hyperspectral and polarized hyperspectral data (0°, 45°, 90°, and 135°) for the soybean seeds was obtained. Finally, the seed vigor prediction model based on polarized hyperspectral components such as I, Q, and U was constructed, and partial least squares regression (PLSR), back-propagation neural network (BPNN), generalized regression neural network (GRNN), support vector regression (SVR), random forest (RF), and blending ensemble learning were applied for modeling analysis. The results showed that the prediction accuracy when using PHI was improved to 93.36%, higher than that for the hyperspectral technique, with a prediction accuracy up to 97.17%, 98.25%, and 97.55% when using the polarization component of I, Q, and U, respectively.