The development of unmanned aerial vehicles (UAVs) and image processing algorithms for field-based phenotyping offers a non-invasive and effective technology to obtain plant growth traits such as canopy cover and plant height in fields. Crop seedling stand count in early growth stages is important not only for determining plant emergence, but also for planning other related agronomic practices. The main objective of this research was to develop practical and rapid remote sensing methods for early growth stage stand counting to evaluate mechanically seeded rapeseed (Brassica napus L.) seedlings. Rapeseed was seeded in a field by three different seeding devices. A digital single-lens reflex camera was installed on an UAV platform to capture ultrahigh resolution RGB images at two growth stages when most rapeseed plants had at least two leaves. Rapeseed plant objects were segmented from images of vegetation indices using typical Otsu thresholding method. After segmentation, shape features such as area, length-width ratio and elliptic fit were extracted from the segmented rapeseed plant objects to establish regression models of seedling stand count. Three row characteristics (the coefficient of variation of row spacing uniformity, the error rate of the row spacing and the coefficient of variation of seedling uniformity) were further calculated for seeding performance evaluation after crop row detection. Results demonstrated that shape features had strong correlations with ground-measured seedling stand count. The regression models achieved R-squared values of 0.845 and 0.867, respectively, for the two growth stages. The mean absolute errors of total stand count were 9.79 and 5.11% for the two respective stages. A single model over these two stages had an R-squared value of 0.846, and the total number of rapeseed plants was also accurately estimated with an average relative error of 6.83%. Moreover, the calculated row characteristics were demonstrated to be useful in recognizing areas of failed germination possibly resulted from skipped or ineffective planting. In summary, this study developed practical UAV-based remote sensing methods and demonstrated the feasibility of using the methods for rapeseed seedling stand counting and mechanical seeding performance evaluation at early growth stages.

Soil shear strength is an important indicator of soil erosion sensitivity and the tillage performance of the cultivated layer. Measuring soil shear strength at a field scale is difficult, time-consuming, and costly. This study proposes a new method to predict soil shear strength parameters (cohesion and internal friction angle) by combining cone penetration test (CPT) data and soil properties. A portable CPT measuring device with two pressure sensors was designed to collect two CPT data in farmland, namely cone tip resistance, and cone side pressure. Direct shear tests were performed in the laboratory to determine the soil shear strength parameters for 83 CPT data collection points. Two easily available soil properties (water content and bulk density) were determined via the oven-drying method. Using the two CPT data and the two soil properties as predictors, three machine learning (ML) models were built for predicting soil cohesion and the internal friction angle, including backpropagation neural network (BPNN), partial least squares regression (PLSR), and support vector regression (SVR). The prediction performance of each model was evaluated using the coefficient of determination (R²), the root-mean-square error (RMSE), and the relative error (RE). The results suggested that among all the evaluated models, the BPNN model was the most suitable prediction model for soil cohesion, and the SVR model performed best in predicting soil internal friction angle. Thus, our findings provide a foundation for the convenient and low-cost measurement of soil shear strength parameters.

Design and experiment of multi-row pneumatic precision metering device for rapeseed

Sesame seeds are flat and oval, with poor mobility, easily blocking a seed tube and reducing seeding quality. An air-assisted seed-guiding device was designed for a hill-seeding centralized seed-metering device for sesame. The core of the seed-guiding device is a distribution manifold that could restrict the trajectory of seeds and make seeds move in the same direction as airflow. Six-factor three-level orthogonal tests were carried out using CFD–DEM coupling simulation to study the influence of the structure and operation parameters of the seed-guiding device on airflow field, seed transport, and seeding performance. The simulation results derived optimal parameters: the depth of the circular section of the seed slide was 2.62 mm, the length of the expansion and contraction section was 188 mm and the length of the contraction section was 20 mm, the seed tube diameter was 19 mm, the airflow velocity was 6.3 m/s, and the rotation speed of the roller was 25 r/min. Under the optimal parameters, the positive pressure required for the seed-guiding device was 256.77 Pa, the time of seeds passing through the seed-guiding device was 0.77 ± 0.02 s, and the velocity of seeds when they came out of the seed tubes was 2.24 ± 0.30 m/s. The qualified rate was 88.33% (2 ± 1 seeds/hill), and the miss-seeding rate was 5.00% (0 seeds/hill). Bench test showed that the qualified rate was 86.80%, and the miss-seeding rate was 6.00%. The seeding performance of the bench test was consistent with the simulation results. Field tests showed that the average number of seedlings per hill was 1.32. The seed-guiding device could meet the requirements of precision hill-seeding for sesame. This study provides a reference for design of a seed-guiding device of a centralized seed-metering device for sesame.

Biosensing and tactile sensing are considered to be essential functions for intelligent diagnostic medical robot. In this paper, biosensing and tactile sensing had been demonstrated with a single photonic crystal structure. The flexible and stretchable photonic crystal structure consists of PDMS as the flexible substrate and TiO₂ as the guided layer, and the nanograting structure was realized by nanoreplica molding. For biosensing experiment, a sensitivity of 93 nm/RIU is verified with ambient environment RI variance simulation results. For tactile sensing experiment, the highest resolution for strain sensing is 0.1%, and the minimum detected scale of the grating period variation is 0.1 nm. The TiO2/PDMS structure based flexible and stretchable photonic crystal sensor demonstrates highly sensitivity and potentially cost effective for biosensing and tactile sensing, and it is promising in the area of intelligent diagnostic medical robot.

Based on the related characteristics of optical waveguide and flexible optical materials, a flexible and stretchable optical waveguide structure oriented to tactile perception is proposed. The sensing principle of optical waveguide is based on mechanical deformation caused by output light loss. It overcomes the shortcomings of traditional optical waveguide devices, which are unable to conform to irregular surface. The flexible and stretchable optical waveguide is fabricated with nanoreplica molding method, and it has been applied to the measurement of pressure and strain in the field of tactile sensing. The flexible and stretchable optical waveguide had a strain detection range of 0 to 12.5%, and the external force detection range is from 0 to 23 × 10^–3 N.