Pig weight and body size are important indicators for producers. Due to the increasing scale of pig farms, it is increasingly difficult for farmers to quickly and automatically obtain pig weight and body size. Due to this problem, we focused on a multiple output regression convolutional neural network (CNN) to estimate pig weight and body size. DenseNet201, ResNet152 V2, Xception and MobileNet V2 were modified into multiple output regression CNNs and trained on modeling data. By comparing the estimated performance of each model on test data, modified Xception was selected as the optimal estimation model. Based on pig height, body shape, and contour, the mean absolute error (MAE) of the model to estimate body weight (BW), shoulder width (SW), shoulder height (SH), hip width (HW), hip width (HH), and body length (BL) were 1.16 kg, 0.33 cm, 1.23 cm, 0.38 cm, 0.66 cm, and 0.75 cm, respectively. The coefficient of determination (R²) value between the estimated and measured results was in the range of 0.9879–0.9973. Combined with the LabVIEW software development platform, this method can estimate pig weight and body size accurately, quickly, and automatically. This work contributes to the automatic management of pig farms.

During the operation of combine harvesters, the cutting platform height is typically controlled using manual valve hydraulic systems, which can result in issues such as delays in adjustment and high labor intensity, affecting both the quality and efficiency of the operation. There is an urgent need to enhance the automation level. Conventional methods frequently employ single-point measurements and lack extensive area coverage, which means their results do not fully represent the terrain’s variations in the area and are prone to local anomalies. Given the inherently undulating terrain of farmland during harvesting, a control strategy that does not adjust for minor undulations but only for significant ones proves to be more rational. To this end, a sine wave superposition model was established to simulate three-dimensional ground elevation changes, and an area array LiDAR was used to collect 8 × 8 data for the header height. The effects of mounds and stubble on the measurement results were analyzed, and a dynamic process simulation model for the solenoid valve core was developed to analyze the on/off delay characteristics of a three-position four-way electromagnetic directional valve. Moreover, a physical model of the hydraulic system was constructed based on the Simscape module in Simulink, and the Bang Bang switch predictive control system based on position threshold was introduced to achieve early switching of the electromagnetic directional valve circuit. In addition, an automatic control system for cutting platform height was designed based on an STM32 microcontroller. The control system was tested on the hydraulic automatic control test rig developed by Shanxi Agricultural University. The simulation and experimental results demonstrated that the control system and strategy were robust to output disturbances, effectively enhancing the intelligence and environmental adaptability of agricultural machinery operations.

Controlling the backfat thickness of sows within an appropriate range during different production stages helps to increase the number of pigs weaned per sow per year and ultimately enhances the economic benefit to the pig farm. To obtain the backfat thickness of sows automatically, a backfat thickness estimation method based on machine vision is proposed. First, the backfat thickness values and 3D images of the buttocks of 154 Landrace–Yorkshire crossbred sows were obtained using a veterinary ultrasound backfat meter and Azure Kinect DK camera. After preprocessing the 3D images utilizing Python 3.9.16 software, 10 external morphological parameters reflecting the area, width, height, and contour radius of the sow’s buttocks were extracted. The relationships between backfat thickness and external morphological parameters were analyzed in a randomly selected group of 100 sows. A significant positive correlation was observed between backfat thickness and buttock morphological parameters, with the Pearson coefficient for the fitted ellipse area achieving values up to 0.90. A backfat thickness estimation model was developed based on selected buttock feature parameters. The model’s generalization performance was evaluated using 54 additional sows that were not involved in the model development. The coefficient of determination (R²) between the estimated and actual backfat thicknesses was 0.8923, with a mean absolute error (MAE) of 1.23 mm and a mean absolute percentage error (MAPE) of 5.73%. These metrics indicate that the model can meet production requirements, and the proposed technique offers improved estimation accuracy compared to existing methods. Ultimately, a backfat thickness automatic estimation system was developed using LabVIEW 2023 Q1 (64-bit) software. This research helps to address the cumbersome process of measuring sow backfat thickness and promotes the automation of sow farms.

Buckwheat plant height is an important indicator for producers. Due to the decline in agricultural labor, the automatic and real-time acquisition of crop growth information will become a prominent issue for farms in the future. To address this problem, we focused on stereo vision and a regression convolutional neural network (CNN) in order to estimate buckwheat plant height. MobileNet V3 Small, NasNet Mobile, RegNet Y002, EfficientNet V2 B0, MobileNet V3 Large, NasNet Large, RegNet Y008, and EfficientNet V2 L were modified into regression CNNs. Through a five-fold cross-validation of the modeling data, the modified RegNet Y008 was selected as the optimal estimation model. Based on the depth and contour information of buckwheat depth image, the mean absolute error (MAE), root mean square error (RMSE), mean square error (MSE), and mean relative error (MRE) when estimating plant height were 0.56 cm, 0.73 cm, 0.54 cm, and 1.7%, respectively. The coefficient of determination (R2) value between the estimated and measured results was 0.9994. Combined with the LabVIEW software development platform, this method can estimate buckwheat accurately, quickly, and automatically. This work contributes to the automatic management of farms.

To reduce grain loss during pickup and prevent stalk entanglement in buckwheat harvesters, thereby improving the quality of mechanized harvesting, a three-stage pickup conveyor roll with a ground-level rotary knife-type pickup header was designed and tested. This paper, based on the growth characteristics of buckwheat, determined the three-stage pickup conveying process and the overall structure of the ground-level rotary knife-type pickup header. Kinematic analysis and parameter design of the rotary knife-type pickup roll were conducted. Finally, a physical prototype was fabricated, and field performance tests were carried out, using machine forward speed and pickup roll rotational speed as influencing factors and pickup loss rate as an evaluation metric. Results showed that the interaction between pickup roll rotational speed and forward speed had a significant effect on the pickup loss rate, with forward speed having a greater impact than pickup roll rotational speed. Under consideration of their interaction, when the pickup roll rotational speed was within the range of 396–457 r/min, and the forward speed was between 0.9–1.0 m/s, the pickup loss rate was minimized. Based on the regression equation model, the predicted optimal conditions were a forward speed of 1.0 m/s and a roll pickup speed of 396 rpm. Under these conditions, the test results showed a pickup loss rate of 5.235%, indicating good pickup performance. This research provides a reference for the design of pickup devices in grain combine harvesters.