Aiming at the problems of low intelligence level of peanut seeder, unstable quality of sowing and fertilization, and poor coordination ability of one-time multi-work, this paper proposes a cooperative control method for simultaneous sowing and fertilization in electric-driven peanut planters. In this method, an improved cross-coupling control structure is proposed to realize the cooperative control of sowing and fertilization, and a fuzzy PID controller is designed. In addition, in order to solve the problem of high overshoot and poor system follow-up when the target speed of the control motor changes greatly during the operation process, an improved particle swarm optimization algorithm is introduced to reduce overshoot, improve response speed, and improve the control accuracy and stability of the seed and fertilizer simultaneous sowing control system. The method was simulated and analyzed on the Matlab/Simulink simulation platform, and the simulation results indicate that the dynamic performance and anti-interference capability of the improved controller have been significantly enhanced. To verify the effectiveness of this control method, an experiment on simultaneous sowing and fertilization of peanuts was designed. The experimental data showed that under stable operation, the average sowing qualification rate was 98.67% and the average fertilization qualification rate was 98.34%; under sudden load conditions, the average sowing qualification rate was 97.33% and the average fertilization qualification rate was 98.18%. The method maintained a low fluctuation range under different working conditions, effectively achieving precise simultaneous sowing and fertilization of peanuts. This research can provide an effective technical reference for efficient peanut cultivation.

The chassis frame of the self-propelled peanut seeder in hilly and mountainous areas is the main supporting structure of the entire machine, and its weight directly affects the operational performance of the seeder. Therefore, in response to the issues of structural heaviness, strength redundancy, and short endurance of the self-propelled peanut seeder in hilly and mountainous areas, this study aims to reduce the overall weight of the machine, conserve resources, and extend the seeder’s endurance time. The research focuses on the chassis frame of the self-propelled peanut seeder, utilizing SolidWorks for 3D modeling. A finite element model of the chassis frame is established using ANSYS Workbench, followed by modal analysis and static analysis under four different working conditions. Based on sensitivity analysis, design variables for the chassis frame are selected, and the response relationships between these design variables are simulated using the Latin Hypercube Design method combined with the Kriging approximation model. Finally, a multi-objective lightweight design is conducted based on the MOGA algorithm. The results indicate that the optimized chassis frame mass is reduced by 28.9%, while meeting the strength requirements. Field tests indicate that the plant spacing qualification rate is ≥98%; the seeding depth operational performance is stable, with an average qualification rate of seeding depth ≥90%. After lightweight design, the prototype structure is stable and the performance is reliable. The research results can provide reference and theoretical basis for the structural optimization and design of the walking chassis frame of self-propelled peanut planters in hilly and mountainous areas.

Aiming at the problem that the consistency and stability of seeding depth are difficult to control during the complex field operation of peanut seeders, this study proposes a control method combining the Kalman Filter algorithm based on Maximum Likelihood Estimation (MLE-KF) and the Fuzzy PID algorithm. A system was designed to automatically adjust the furrow opener during the operation of the peanut seeder, thereby realizing the automatic control of seeding depth. This method maintains the peanut seeding depth within a reasonable error range suitable for growth, and effectively ensures the consistency of peanut seeding depth. In this study, displacement sensors and ultrasonic sensors were used to directly or indirectly detect the furrow depth. Combined with electric push rods and actuators, the furrow depth was adjusted in real time during the field operation of the peanut seeder. For the filtering fusion algorithm, a noise parameter adaptive algorithm based on sliding window and approximate MLE was adopted, while a Fuzzy PID control strategy was employed for the controller. Simulations were conducted in MATLAB/Simulink, and the simulation results showed that the improved algorithm achieved a significant improvement in seeding depth accuracy compared with the traditional Kalman Filter and PID control. Field tests were carried out with the qualification rate as the index under different operating speeds and seeding depth conditions. For this seeding depth control method, when the operation speed was 3 km/h and the furrow depth was set to 50.00 mm, the qualification rate of seeding depth reached 94.80%, with a standard deviation of 3.12 mm and a coefficient of variation of 6.08%. Compared with the traditional mechanical adjustment and passive adjustment methods, this method increased the qualification rate by 4.60% and 3.30%, respectively, and reduced the coefficient of variation by 2.46% and 3.01%, respectively. The research on the seeding depth control method in this study provides a new detection and control approach for the seeding depth control of peanut seeders.

Aiming at the problems in the traditional peanut sowing operation process, such as single monitoring mode, lack of online monitoring function, inability to deeply utilize data, and difficulty in tracing the sowing quality, an online monitoring system for peanut sowing parameters based on the Internet of Things (IoT) was designed. The online monitoring system for peanut sowing parameters consists of a peanut seed-metering monitoring device, an on-board monitoring terminal, and an online monitoring cloud platform. The system can monitor parameters such as seed spacing, seeding rate, missed seeding, vehicle speed, temperature, and humidity. It transmits data through RS485 and IoT communication, and supports local interaction and cloud data storage and analysis. The seed-metering monitoring device uses laser opposite reflection and window fiber optic sensors to monitor the missed seeding and seed-metering status in real time. The on-board terminal uses an optoelectronic rotary encoder to collect the rotational speed and calculate the parameters. The operation status is displayed through the human-machine interaction module, and the data is packaged and sent to the cloud server via a wireless network. The online monitoring cloud platform selects the Alibaba Cloud IoT platform, connects with it through the MQTT protocol, and conducts visual development using IoT-studio to achieve data display, analysis, and statistics. Through the test on the simulated test bench, the accuracy rates of the seed spacing, seeding rate, and missed seeding monitoring of the seed-metering monitoring module of the system exceed 98.02%, 98.03%, and 99%, respectively. The field test based on the actual seeder shows that the monitoring effect of the seed spacing of 16 cm and 20 cm is good. The accuracy rate of the 27 cm seed spacing decreases slightly with the increase of the speed but still exceeds 97%. The accuracy rates of the missed seeding and seeding rate monitoring exceed 98%, and the online monitoring module transmits data normally. After testing, each part of the system has good performance, meeting the functional and accuracy requirements of the online monitoring of peanut sowing operation parameters.

Single seed metering devices for Chinese flowering cabbage planting machines have suffered such deficiencies as low efficiency, poor accuracy, and instability. To overcome these limitations, a pneumatic double disc precision seed metering device was designed. This innovative device can simultaneously plant four rows, considering the specific agricultural requirements and the geometric characteristics for Chinese flowering cabbage seeds. The precise parameters of the key component seed disc were derived through theoretical calculation. In addition, a detailed account was given for the working principle and workflow of the seed metering device. The discrete element method and EDEM software were employed to optimize the seed disc by exploring the effects of seed disc rotational speed and interleaving seed slots on seed viability. Orthogonal rotation experiments were conducted to evaluate the impact of seed disc rotational speed and negative pressure. While rates of qualified seeding, double seeding and missing seeding were adopted as test indicators. Testing results show that, at a seed disc rotational speed of 41.5 r/min and a negative pressure of 3.80 kPa, the average qualified seeding rate is 90.13%, the average missing seeding rate is 3.30%, and the average double seeding rate is 6.02%. These values satisfy the agricultural requirements for planting Chinese flowering cabbage. The findings can also provide valuable insights for the structural optimization and design of precision seed metering devices for Chinese flowering cabbage.