The purpose of this study is to explore how corn varieties and moisture content affect the mechanical properties of corn with high moisture content (26.0~36.4%). The mechanical properties of corn that affect corn threshing quality are determined by establishing the contact mechanical model of the threshing elements and corn kernels. These mechanical properties of corn include the size characteristics of corn (corn ear weight and corn kernel triaxial size) in addition to the mechanical characteristics of corn (corn elastic modulus, corn kernel breaking force, and corn stalk breaking force). The effects of corn varieties and moisture content on the mechanical properties of corn were tested. The results showed that the corn varieties and moisture content had significant effects on the mechanical properties of corn. Among them, the different levels of moisture content levels have significant differences in the mechanical properties of corn, while corn varieties have different significant differences in the mechanical properties of corn. The ear weight, corn kernel triaxial size, and the axial and radial breaking force of corn pedicels all increased significantly as the moisture content increased. Corn’s elastic modulus, kernel breaking force, and the normal breaking force of corn pedicels all decreased significantly as moisture content increased. The breaking force of corn kernels at different positions is in the order of ventral, lateral, and top surface, and the breaking force of corn pedicels in different directions is in the order of normal, axial, and radial. When the moisture content is between 26.0% and 36.4%, the maximum breaking force of a corn kernel is 401.62 N, and the minimum is 35.47 N. The maximum breaking force of corn pedicels is 32.72 N, and the minimum is 5.37 N. Understanding the influences of these relationships is very important for applications related to the threshing process, such as selecting the threshing mode, designing the threshing mechanism, and setting the threshing parameters.

Experimental study on the key factors of low-loss threshing of high-moisture maize

Agricultural environment mapping is the premise of the autonomous navigation of agricultural robots. Due to the undulating terrain and chaotic environment, it is challenging to accurately map the environmental maize field using existing LOAM (LiDAR odometry and mapping) methods. This paper proposes a LOAM method based on maize stalk semantic features for 6-DOF (degrees of freedom) pose estimation and field mapping with agricultural robots operating in a dynamic environment. The piecewise plane fitting method filters the ground points for the complex farmland terrain. To eliminate the unstable factors in the environment, we introduce the semantic information of maize plants into the feature extraction. The regional growth method segments the maize stalk instances, the instances are parameterized to a line model, and the optimization method calculates the pose transformation. Finally, the mapping method corrects the drift error of the odometry and outputs the maize field map. This paper compares our method with the GICP and LOAM methods. The trajectory relative errors of our method are 0.88%, 0.96%, and 2.12%, respectively, better than other methods. At the same time, the map drawn by our method has less ghosting and clearer plant edges. The results show that our method is more robust and accurate than other methods due to the introduction of semantic information in the environment. The mapping of corn fields can be further used in precision agriculture.

Image moments-based visual servoing control of bagged agricultural materials handling robot