Traditional maize ear harvesters mainly rely on manual identification of fallen maize ears, which cannot realize real-time detection of ear falling. The improved You Only Look Once-V4 (YOLO-V4) algorithm was combined with the channel pruning algorithm to detect the dropped ears of maize harvesters. K-means clustering algorithm was used to obtain a prior box matching the size of the dropped ears, which improves the Intersection Over Union (IOU). Compare the effect of different activation functions on the accuracy of the YOLO-V4 model, and use the Mish activation function as the activation function of this model. Improve the calculation of the regression positioning loss function, and use the CEIOU loss function to balance the accuracy of each category. Use improved Adam optimization function and multi-stage learning optimization technology to improve the accuracy of the YOLO-V4 model. The channel pruning algorithm was used to compress the model and distillation technology was used in the fine-tuning of the model. The final model size was only 10.77% before compression, and the test set mean Average Precision (mAP) was 93.14%. The detection speed was 112 fps, which can meet the need for real-time detection of maize harvester ears in the field. This study can provide a technical reference for the detection of the ear loss rate of intelligent maize harvesters.

Applying different types of fertilizers to different depths of soil according to demand is advantageous in that it can optimize the distribution of nutrients in arable soil, adjust the nutrient supply of each growth stage of wheat, and increase grain yield. In the study, a layered fertilization opener that could realize the layered fertilization was developed. The interaction model between the opener, fertilizer and soil was established using EDEM simulation software. A response surface analysis was used to determine the optimal parameters of the opener. Specifically, the horizontal distance between the fertilizer drop openings was 140 mm, the machine speed was 1.05 m/s, and the angle of the opener was 37°. Furthermore, field experiments demonstrated that the average depth of upper layer was 8.39 cm, the average depth of middle layer was 16.465 cm, the average depth of lower layer was 24.025 cm, the average spacing of upper layer was 8.075 cm, and the average spacing of lower layer was 7.6 cm. The corresponding findings demonstrated that the layering effect of the opener met the requirements of the fertilization standard.

Weld feature point detection is a key technology for welding trajectory planning and tracking. Existing two-stage detection methods and conventional convolutional neural network (CNN)-based approaches encounter performance bottlenecks under extreme welding noise conditions. To better obtain accurate weld feature point locations in high-noise environments, we propose a feature point detection network, YOLO-Weld, based on an improved You Only Look Once version 5 (YOLOv5). By introducing the reparameterized convolutional neural network (RepVGG) module, the network structure is optimized, enhancing detection speed. The utilization of a normalization-based attention module (NAM) in the network enhances the network’s perception of feature points. A lightweight decoupled head, RD-Head, is designed to improve classification and regression accuracy. Furthermore, a welding noise generation method is proposed, increasing the model’s robustness in extreme noise environments. Finally, the model is tested on a custom dataset of five weld types, demonstrating better performance than two-stage detection methods and conventional CNN approaches. The proposed model can accurately detect feature points in high-noise environments while meeting real-time welding requirements. In terms of the model’s performance, the average error of detecting feature points in images is 2.100 pixels, while the average error in the world coordinate system is 0.114 mm, sufficiently meeting the accuracy needs of various practical welding tasks.

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Aiming at the low comprehensive utilization rate of corn straw resources, a straw kneading and cutting conveyor suitable for corn harvester was designed to improve the utilization rate of corn straw resources. The workbench module of ANSYS is used to carry out modal analysis of the two blades, and it is determined that the vibration frequency will not cause damage to the blade sweeping bore. By changing the structure of the movable blade shaft, the speed of the blade shaft can be reduced while ensuring the effect of straw crushing and collecting. In order to determine the best working parameters, three-factor and three-level orthogonal test was carried out with blade arrangement, blade shaft speed and length of feed straw as test factors, and the crushing rate of straw as evaluation index. The results show that the main factors influencing the crushing rate of straw are blade shaft speed, blade arrangement and the minor factor is the length of feed straw. Finally, the optimum combination parameters, blade arrangement, blade shaft speed 400 r/min and whole plant feed with straw, were determined. The corresponding straw crushing rate was 96.39%. The research meets the requirements of straw crushing and can provide technical scheme for comprehensive utilization of corn straw.