The existing control methods for the slip rate of the driving wheel of a test prototype have limitations that cause low-quality tillage and finishing operations. We propose a slip rate control method based on the dual factor adjustment of wheel speed and tillage depth, taking the power shift tractor New Holland T1404 as an example to verify the algorithm. This method employs the wheel speed control principle based on the power transmission ratio calculation, throttle adjustment, and wheel speed control methods, as well as the slip rate control method, with wheel speed–slip rate control as the main factor and tillage depth–slip rate control as the secondary factor. A tractor test prototype was built to validate the method. The wheel speed control method enabled the tractor to accurately control the wheel speed under three working conditions: no load on a cemented ground, no load in a field, and subsoiling operation. For the subsoiling operation, the slip rate control method gradually reduced the tractor wheel speed when the slip rate of the tractor’s drive wheel was too high until it met the requirements. When the wheel speed was adjusted to the lower limit, suspension control was performed to reduce the tillage depth and improve vehicle trafficability. In the 130 s validation test, it took 14.1 s for the tractor with the slip rate control function to have a wheel slip rate exceeding 20%, which was 25.4% lower than that of the tractor without this function. The proposed method controls the slip rate within the optimal range while ensuring maximum operation quality (tillage depth).

The yield and quality of sweet potatoes are significantly influenced by the transplantation posture of sweet potato seedlings. The performance of the sweet potato seedling transplanting opener directly affects the transplantation posture of sweet potato seedlings. In order to improve the yield and quality of sweet potatoes, this study proposes a joint simulation method based on discrete element and flexible multi body dynamics (DEM-FMBD), which optimizes the structure of a self-covering soil opener. By exploring the influence of self-covering soil trenchers on the planting depth and posture of sweet potato seedlings during horizontal transplantation, it was determined that the influencing factors of the experiment were wing spacing, soil reflux height, and soil reflux length. Based on the DEM-FMBD coupling simulation platform, single factor, and quadratic rotation orthogonal experiments were carried out. According to the results of the simulation test, the effect of the interaction of test factors on planting depth and planting attitude was analyzed by the response surface method. Finally, the optimal structural parameter combination was obtained by a multi-objective optimization method: the spacing of the wings was 58 mm, the height of the soil backflow port was 71 mm, and the length of the soil backflow port was 163 mm; thus, the quality of transplanting is improved effectively. This study provides the method and theory reference for the study of sweet potato transplanting.

The accurate recognition of maize growth stages is crucial for effective farmland management strategies. In order to overcome the difficulty of quickly obtaining precise information about maize growth stage in complex farmland scenarios, this study proposes a Maize Hybrid Vision Transformer (MaizeHT) that combines a convolutional algorithmic structure with self-attention for maize growth stage recognition. The MaizeHT model utilizes a ResNet34 convolutional neural network to extract image features to self-attention, which are then transformed into sequence vectors (tokens) using Patch Embedding. It simultaneously inserts category information and location information as a token. A Transformer architecture with multi-head self-attention is employed to extract token features and predict maize growth stage categories using a linear layer. In addition, the MaizeHT model is standardized and encapsulated, and a prototype platform for intelligent maize growth stage recognition is developed for deployment on a website. Finally, the performance validation test of MaizeHT was carried out. To be specific, MaizeHT has an accuracy of 97.71% when the input image resolution is 224 × 224 and 98.71% when the input image resolution is 512 × 512 on the self-built dataset, the number of parameters is 15.446 M, and the floating-point operations are 4.148 G. The proposed maize growth stage recognition method could provide computational support for maize farm intelligence.

There is an increasing demand for remote monitoring and management of agricultural machinery operation in conservation tillage. Considering the problems of large errors in detecting operation quality parameters, such as tillage depth and corn straw cover rate, in complex farmland environments, this paper proposes a tillage depth measurement method based on the dual attitude compound of a tractor body and three-point hitch mechanism with lower pull rod and an online measurement method based on K-means clustering of the corn straw cover rate on farmland surface. An operation monitoring terminal was developed for the remote collection of quality parameters of conservation tillage field operation. A remote monitoring system of agricultural machinery operation was constructed and applied over a large area. The field tests showed that the static mean error and root-mean-square error of this method were 0.16 and 0.67 cm for uphill and 0.36 and 0.57 cm for downhill, respectively. For the 28 and 33 cm tillage depth tests, the mean dynamic measurement errors of this method were 0.55 and 0.61 cm, and the root means square errors were 0.64 and 0.73 cm, respectively, and the coefficient of variation of tillage depth did not exceed 3%. The correlation coefficient between the corn straw cover rate detection algorithm based on K-means clustering and the manual image marking method reached 0.92, with an average error of 9.69%, and the accuracy filled the demand for straw cover rate detection. The detection accuracy of tillage depth and straw cover rate was high and thus provides an effective means of information technology support for the quality monitoring and production management of conservation tillage farming operations.

The existing methods of measuring the baler feed rate seldom consider the influence of machine vibration on the sensor signal during field operation, which leads to the low detection accuracy and poor stability of feeding quantity detection. We established a feed rate detection model of a baler based on power monitoring of the pickup platform. Through the dynamic analysis of the pickup platform, the functional relationship between the working power of the pickup platform and the feed rate was constructed. A power monitoring system of the pickup platform was developed, and the model construction experiment of the working power and the feed rate was performed. The influence mechanism of different running speeds on the torque noise signal of the power input shaft of the pickup platform was explored. The frequency of the noise signal was mainly concentrated at 0.5–6 Hz and 9–13 Hz employing a fast Fourier transform, and the noise signal was eliminated by the frequency-domain-filtering method. The function model of working power and feed rate of the pickup platform was established based on signal processing, and the determination coefficient R² of the model was 0.9796. The field experiment results show that when the feed rate of the baler is between 1.6 and 4.88 kg/s, the determination coefficient R² and RMSE between the actual and predicted feed rate are 0.989 and 0.2, respectively. The relative error range of feed-rate prediction is −9.37–8.77%, which indicates that the model has high detection accuracy and good stability and meets the requirements of feed-rate monitoring of a baler in field operation.