After corn mechanical harvest in autumn in the cold region of northeast China, straw mulching leads to high soil topsoil water content and slow ground temperature rise in the spring sowing season, which may result in a no-tillage planter being unable to operate in time, delayed sowing date, poor seedling emergence quality, and low grain yield. Based on the problems of high moisture content and low temperature in soil topsoil in the spring sowing season in conservation tillage with straw mulching, an experimental study on timely sowing in the spring sowing season was carried out from the perspective of combination optimization of attribute parameters of straw mechanical treatment. Taking the four mechanical treatment attributes as experimental factors, including straw length, straw shape, mulching form (surface covering, inter-ridge covering, and inter-ridge mixed soil covering), and stubble height after corn harvest in autumn of 2019 as experimental factors, and soil topsoil moisture content and temperature during spring sowing in 2020 as evaluation indexes, a field plot experiment was carried out by four factors and three levels of orthogonal combination test method. The results showed that all the factors had significant influence on soil topsoil moisture content and soil topsoil temperature (p < 0.05). It is helpful to reduce the moisture content of soil topsoil and increase the temperature of soil topsoil by increasing the length of corn stalk, breaking the stalk moderately, covering the stalk between ridges, and increasing the stubble height of stalk. Design-Expert software was used to optimize the parameters. The results showed that when the length of straw was 150 mm, the shape of straw was half-cut (dividing straw into two parts evenly along the axis), the mulch was between ridges, and the stubble height was 600 mm, the moisture content of soil topsoil was 22~24% and the temperature of soil topsoil was higher than 8 °C during spring sowing. After corn harvest in autumn of 2021, corn stalks were treated in that year according to the results of parameter optimization combination. The soil topsoil moisture content and temperature were measured to be 24.4% and 8.2 °C, respectively, in the spring sowing season of 2022, which proved that the optimization results in this experiment were credible. The experimental results provide a technical reference for the implementation of conservation tillage with straw surface mulching in the cold region of northeast China.

Accurate identification of rice ears is crucial for assessing rice yield. Present research mainly relies on single-scale image data for rice ears detection and counting. However, these approaches are susceptible to misdetection and omission due to the intricate environmental conditions in fields. The combination of multi-source images can better overcome the limitations of single-scale images. In this study, based on the YOLOv5s target detection algorithm, a method for rice ears detection and counting applicable to multi-source images is proposed by integrating image data collected by cell phones and UAVs during the rice heading and maturity periods. The proposed method introduces Attention-based Intrascale Feature Interaction (AIFI) to reconstruct the backbone feature extraction network, optimizing feature expression interaction and enhancing handling of the model of advanced semantic information. Additionally, Simplify Optimal Transport Assignment (SimOTA) is employed to achieve a more refined label assignment strategy, thereby optimizing detection performance of the model and addressing difficulties in detecting multiple targets in high-density rice ears environments. Finally, Channel-wise Knowledge Distillation for Dense Prediction (CWD) is utilized to enhance the performance of the model in dense prediction tasks by transferring knowledge between different channels. The experimental results demonstrated good performance of the model on datasets comprising rice at the heading and maturity stages, achieving Precision, Recall, and mAP values of 93%, 85.3%, and 90.3%, respectively. The coefficients of determination (R $^{2}$ ) for the linear fit between test results and the actual statistical results of the model were 0.91, 0.91, 0.90, and 0.88, respectively. The proposed model performs well in the mixed dataset and can be utilized more effectively for accurate identification and counting of rice ears.

Introduction: Accurate and rapid identification of cabbage posture is crucial for minimizing damage to cabbage heads during mechanical harvesting. However, due to the structural complexity of cabbages, current methods encounter challenges in detecting and segmenting the heads and roots. Therefore, exploring efficient cabbage posture prediction methods is of great significance.Methods: This study introduces YOLOv5-POS, an innovative cabbage posture prediction approach. Building on the YOLOv5s backbone, this method enhances detection and segmentation capabilities for cabbage heads and roots by incorporating C-RepGFPN to replace the traditional Neck layer, optimizing feature extraction and upsampling strategies, and refining the C-Seg segmentation head. Additionally, a cabbage root growth prediction model based on Bézier curves is proposed, using the geometric moment method for key point identification and the anti-gravity stem-seeking principle to determine root-head junctions. It performs precision root growth curve fitting and prediction, effectively overcoming the challenge posed by the outer leaves completely enclosing the cabbage root stem.Results and discussion: YOLOv5-POS was tested on a multi-variety cabbage dataset, achieving an F1 score of 98.8% for head and root detection, with an instance segmentation accuracy of 93.5%. The posture recognition model demonstrated an average absolute error of 1.38° and an average relative error of 2.32%, while the root growth prediction model reached an accuracy of 98%. Cabbage posture recognition was completed within 28 milliseconds, enabling real-time harvesting. The enhanced model effectively addresses the challenges of cabbage segmentation and posture prediction, providing a highly accurate and efficient solution for automated harvesting, minimizing crop damage, and improving operational efficiency.

In order to explore the laws of corn straw lateral moving and throwing, it is necessary to identify the main factors that restrict improvements in the quality of straw clearing and reductions in power consumption and then optimize the knife parameter combinations; in this paper, the kinematic analysis of single-stage lateral moving and throwing of corn straw was carried out, and the mathematical model for the collision process between the knife and straw is established. Key factors affecting the lateral moving and throwing efficiency of straw were determined according to the model analysis. A parameter combination optimization test was conducted with three-factor and five-level quadratic regression orthogonal rotation center combination test methods and discrete element virtual simulation, taking into account the edge angle of cutting, the rotation radius of the knife, and the rotation speed of the knife roller as test factors and the straw clearing rate and power consumption as performance evaluation indexes. The test results showed that at a travel speed of 7.2 km/h when the edge angle of cutting was 65°, the rotation radius of the knife was 420 mm, and the rotation speed of the knife roller was 538~600 rpm, the straw clearing rate was ≥85%, and power consumption was ≤1.5 kW. The field test was carried out to verify the optimized results, and the test results showed that the test values of the performance evaluation indexes were all in the ranges of the optimized interval. These research results lay down the foundation for the design of lateral straw clearing and throwing knives.

To address the problem that current rice fertilization devices rely on experience-based settings of fertilizer application rates and are unable to dynamically adjust according to soil fertility, resulting in low fertilizer use efficiency, a rice side-deep variable-rate fertilization control system based on real-time soil electrical conductivity (EC) detection was designed. First, a three-factor, four-level full factorial experiment was conducted to investigate the effects of soil moisture content, electrode insertion depth, and soil temperature on soil EC, and an EC calibration model was established based on an RBF neural network. Second, a fertilization strategy was developed and a fertilizer application model was constructed based on real-time EC, target yield, and implement forward speed; meanwhile, an incremental PID algorithm was adopted to achieve closed-loop control of variable-rate fertilization with motor speed as the control objective, completing the control system design. Finally, the system was deployed on a pneumatic groove-wheel fertilizer metering device and field experiments were carried out. The performance of the soil EC detection system and the variable-rate fertilization system was validated through EC detection accuracy tests and variable-rate fertilization system response tests. The results showed that the average relative error of EC was 2.70%, the maximum coefficient of variation of the fertilization system response stability was 3.98%, the maximum response time was 1.60 s and the average was 1.28 s, and the average fertilizer reduction rate was 12.39%. These results indicate that the proposed soil EC detection and variable-rate fertilization system can achieve rapid and accurate variable-rate fertilization operations. This study can provide equipment and technical support for rice side-deep variable-rate fertilization.

To address issues such as slow soil temperature recovery and delayed sowing periods caused by straw mulching in the cold regions of northern Heilongjiang Province, this study designed a straw crushing and scattering device compatible with the 2BMFJ series no-till planters, aiming to achieve moderate straw fragmentation and uniform distribution. By establishing mathematical models for the straw pick-up, crushing, and scattering processes, key parameters affecting the device’s performance were determined. Utilizing the discrete model of EDEM 2018 software virtual simulation experiments were conducted based on response surface methodology. The test factors included the blade angle of the crushing long blade, the edge thickness of the crushing long blade, the weight of the crushing long blade, and the rotational speed of the crushing long blade. The performance evaluation indicators were the straw pick-up rate, straw crushing rate, power consumption, and inter-row straw coverage consistency. The optimal parameter combination was identified to be a blade angle of 25°, an edge thickness of 1.25 mm, a weight ranging from 0.35 to 0.41 kg, and a rotational speed between 1400 and 1750 r/min, resulting in a straw pick-up rate of 83%, a straw crushing rate of 84%, power consumption of 6.8 KW, and a straw cleaning consistency between rows of 75%. Field test results indicated that the straw pick-up rate reached 87.2%, the straw crushing rate achieved 81.5%, power consumption was 7.7 kW, and the straw cleaning consistency between rows attained 79.3%. The deviations from simulation results were within acceptable limits. This equipment can effectively complete straw crushing and scattering operations, thereby creating favorable seedbed conditions.