Real-time grain breakage sensing for rice combine harvesters using machine vision technology

Recently, the existing unmanned systems of combine harvesters mostly adopts satellite navigation scheme, lacking real-time observation of harvesting adjustment. To improve the operational efficiency of combine harvester assisted navigation operation, this paper designs a combine harvester navigation control system based on vision simultaneous localization and mapping (SLAM)-inertial guidance fusion. The system acquires field image information and extracts the crop boundary line as the navigation datum by binocular camera. First, the system acquires field image information through binocular camera and extracts the crop boundary line as the navigation datum. Second, fusing camera and inertial guidance information to obtain the real-time relative position of a combine harvester. Third, constrained optimization of image and inertial guidance information is achieved through a sliding window optimization method based on tightly coupled nonlinear optimization. Finally, obtain the position of the combine harvester relative to the navigation datum line, and output a signal to the steering mechanism to realize the combine harvester in the field intelligent positioning navigation control. The system consists of binocular camera, inertial measurement unit, motorized steering wheel, monitor display, angle sensor and microcontroller. During field testing, the system underwent repetitive harvesting trials over a distance of 25 m.. The testing machine performs field operations at a speed of 0.9-1.5 m/s, with an average lateral deviation range of 2.21-8.62 cm, a standard deviation range of 0.13-4.21 cm and an average cutting rate range of 92.2%-96.0%, achieving the expected harvesting effect.

To enhance the automation and efficiency of combine harvesters, this paper proposes a predictive control method based on Long Short-Term Memory (LSTM) neural networks. The method integrates multi-sensor data fusion using an Extended Kalman Filter (EKF) to improve speed measurement accuracy. By considering feeding volume, operational performance indicators, and critical component speeds, an LSTM-based model predicts the optimal operation speed. The predicted speed is then regulated through an incremental proportional-integral-derivative (PID) control control system. Simulation and field experiments validate the effectiveness of the proposed approach, demonstrating improved speed stability and work efficiency. The results indicate that the system enhances operational performance and reduces manual intervention, contributing to the advancement of intelligent agricultural machinery.

The stable and uniformly distributed airflow field can effectively improve the seed suction effect and seed-carrying stability of the pneumatic seeder. With this end in view, this paper was optimized the airflow basin structure of vacuum-vibration tray precision seeder based on the computational fluid dynamics (CFD) simulation technology. The results show that the airflow field is relatively stable and well-distributed when the chamber height is 50 mm and the outlet tube diameter is 65 mm. In addition, the thickness of the base plate with suction holes should be less than 5 mm, and the needle suction nozzle guide should be greater than 15 mm according to the numerical analysis results. On the basis of the above study, the seeding characteristics of the needle type suction nozzle and the plate type suction nozzle were further explored to determine the type of nozzle more suitable for rectangular sucker. Through various experimental designs, the significant influencing factors of the two suction nozzles and their appropriate working ranges as well as the optimal combination of working parameters were determined in turn. The needle suction nozzle requires lower suction height and less grains dispersion according to the experimental results, while the plate suction nozzle is just the opposite, it allows a certain suction height to be maintained with the seeds, and requires high dispersion of grain. In general, the plate suction nozzle can obtain better seeding performance, is a more favorable nozzle for the vacuum-vibration tray precision seeder.

The combine harvester is a widely used piece of agricultural equipment in modern agriculture, and the seed loss rate is one of the important indexes used to measure its operational performance, so the monitoring of the seed loss rate is crucial for adjusting the operational parameters of combine harvesters and improving the quality of grain harvesting. Aiming at the problems of the slow response speed and low monitoring accuracy of the existing domestic seed loss rate monitoring models, this paper proposed a rice seed loss rate monitoring method based on the whale optimization algorithm-back propagation neural network (WOA-BP). The loss rate monitoring device consisted of a piezoelectric ceramic sensor module, charge amplification circuit, band-pass filter circuit, analog-to-digital (AD) converter, main control unit, etc. The WOA-BP algorithm, which has a high accuracy and fast response speed, was used to classify and count the signals to realise seed loss rate monitoring. The indoor test results showed that the relative errors of the monitoring results are less than 8.5% under the condition of a conveying speed of 1.3-2.1m/s, and the relative errors showed an increasing trend as the proportion of straw increased.

To improve the seeding qualification rate and stability of the air-suction vibrating disc type seed meter on the rice seedling raising line, in this paper, an improved wireless sensor network node layout optimization algorithm was proposed, and the operation monitoring system of the seed meter was designed using the Internet of Things and configuration software. In the system, the upper computer software adopted the Kingview software, the lower computer took the STM32F429IGT and CC2530 as the core controllers, and ZigBee was selected for data transmission to build the wireless sensor network. The acquisition of field status information and the sending of control instructions were realized through the sensor nodes constructed by the CC2530 core controller. The data was sent to the coordinator node in real-time through the wireless sensor network. The coordinator node realized the bidirectional transmission of data with Kingview and the upper computer control instructions forwarding using the ASCII protocol The host computer monitoring and management software was developed based on configuration software to realize real-time data monitoring, access database storage, fault alarm, control command sending and other functions. The experimental results showed that the detection accuracy of the system for the seeding amount and missed seeding amount was 94.3% and 95.6%, respectively, which could realize the monitoring of the primary working status of the seed meter. The system realized effective data transmission and data remote wireless transmission function, which provided sufficient theoretical and data support for the performance optimization of the seed meter, and laid a good foundation for the visualization and intelligence of information data.