To improve the seeding motor control performance of electric-driven seeding (EDS), a genetic particle swarm optimization (GAPSO)-optimized fuzzy PID control strategy for electric-driven seeding was designed. Since the parameters of the fuzzy controller were difficult to determine, two quantization factors were applied to the input of the fuzzy controller, and three scaling factors were introduced into the output of fuzzy controller. Genetic algorithm (GA) and particle swarm optimization (PSO) were combined into GAPSO by a genetic screening method. GAPSO was introduced to optimize the initial values of the two quantization factors, three scaling factors, and three characteristic functions before updating. The simulation results showed that the maximum overshoot of the GAPSO-based fuzzy PID controller system was 0.071%, settling time was 0.408 s, and steady-state error was 3.0693 × 10⁻⁵, which indicated the excellent control performance of the proposed strategy. Results of the field experiment showed that the EDS had better performance than the ground wheel chain sprocket seeding (GCSS). With a seeder operating speed of 6km/h, the average qualified index (I_q) was 95.83%, the average multiple index (I_mult) was 1.11%, the average missing index (I_miss) was 3.23%, and the average precision index (I_p) was 14.64%. The research results provide a reference for the parameter tuning mode of the fuzzy PID controller for EDS.

In order to real-time monitor the health status of pigs in the process of breeding and to achieve the purpose of early warning of swine respiratory diseases, the SE-DenseNet-121 recognition model was established to recognize pig cough sounds. The 13-dimensional MFCC, ΔMFCC and Δ²MFCC were transverse spliced to obtain six groups of parameters that could reflect the static, dynamic and mixed characteristics of pig sound signals respectively, and the DenseNet-121 recognition model was used to compare the performance of the six sets of parameters to obtain the optimal set of parameters. The DenseNet-121 recognition model was improved by using the SENets attention module to enhance the recognition model’s ability to extract effective features from the pig sound signals. The results showed that the optimal set of parameters was the 26-dimensional MFCC + ΔMFCC, and the rate of recognition accuracy, recall, precision and F1 score of the SE-DenseNet-121 recognition model for pig cough sounds were 93.8%, 98.6%, 97% and 97.8%, respectively. The above results can be used to develop a pig cough sound recognition system for early warning of pig respiratory diseases.

Soybean seedling root morphology is important to genetic breeding. Root segmentation is a key technique for identifying root morphological characteristics. This paper proposed a semantic segmentation model of soybean seedling root images based on an improved U-Net network to address the problems of the over-segmentation phenomenon, unsmooth root edges and root disconnection, which are easily caused by background interference such as water stains and noise, as well as inconspicuous contrast in soybean seedling images. Soybean seedling root images in the hydroponic environment were collected for annotation and augmentation. A double attention mechanism was introduced in the downsampling process, and an Attention Gate mechanism was added in the skip connection part to enhance the weight of the root region and suppress the interference of background and noise. Then, the model prediction process was visually interpreted using feature maps and class activation mapping maps. The remaining background noise was removed by connected component analysis. The experimental results showed that the Accuracy, Precision, Recall, F1-Score and Intersection over Union of the model were 0.9962, 0.9883, 0.9794, 0.9837 and 0.9683, respectively. The processing time of an individual image was 0.153 s. A segmentation experiment on soybean root images was performed in the soil-culturing environment. The results showed that this proposed model could extract more complete detail information and had strong generalization ability. It can achieve accurate root segmentation in soybean seedlings and provide a theoretical basis and technical support for the quantitative evaluation of the root morphological characteristics in soybean seedlings.

The current real-time detection of sowing depth has the problems of low detection accuracy and poor reliability. After analyzing the movement mechanism of the sowing monomer parallel four-bar linkage, an improved adaptive Kalman filtering method for sowing depth detection is proposed. The method uses two MPU6050 attitude sensors. The difference between the rotation angle of the parallel four-bar linkage and the attitude angle of the sowing monomer is used as the relative rotation angle of the parallel four-bar linkage relative to the sowing monomer. The sowing monomer parallel four-bar linkage has the characteristics of up–down translation, and sowing depth changes can be obtained by converting the angle data. By using moving average filter, particle filter, Kalman filter, and improved adaptive Kalman filter to fuse the data of MPU6050 in two positions and perform MATLAB simulation, the average mean squared errors of the above four filtering algorithms are 0.12645, 0.05545, 0.03785, and 0.0189, respectively. In the end, the experiment of sowing depth detection was carried out, and it was found that the improved adaptive Kalman filter algorithm detects the smallest sowing depth mean squared error, which is 0.0636, and the algorithm can track data changes better. At the same time, it is more adaptable to noise and obtains an ideal filtering effect.