The operational performance of cereal seeding machinery influences the yield and quality of cereals. In this article, we review the existing literature on intelligent technologies for cereal seeding machinery, encompassing active controllable seeding actuators, intelligent seeding rate control, and intelligent seed position control systems. In this manuscript, (1) the characteristics and innovative structures of existing motor-driven seed-metering devices and ground surface profiling mechanisms are expounded; (2) state-of-the-art detection principles and applications for soil property sensors are described based on different soil properties; (3) optimal seeding rate decision approaches based on soil properties are summarized; (4) the research state of seeding rate measuring and control technologies is expounded in detail; (5) trajectory control methods for seeding machinery and seeding depth control systems are described based on measurement and control principles; and (6) the present state, limitations, and future development directions of intelligent cereal seeding machinery are described. In the future, more advanced multi-algorithm and multi-sensor fusion technologies for soil property detection, optimal seeding rate decisions, seeding rates, and seed position control are likely to evolve. This review not only expounds the latest studies on intelligent actuating, sensing, and control technologies for intelligent cereal seeding machinery, but also discusses the shortcomings of existing intelligent seeding technologies and future developing trends in detail. This review, therefore, offers a reference for future research in the domain of intelligent seeding machinery for cereals.

This study is situated against the background of shortages in the agricultural labor force and shortages of cultivated land. In order to improve the intelligence level and operational efficiency of agricultural machinery and solve the problems of difficulties in recognizing navigation lines and a lack of real-time performance of transplanters in the crop-free ridge environment, we propose a crop-free-ridge navigation line recognition method based on an improved YOLOv8 segmentation algorithm. First, this method reduces the parameters and computational complexity of the model by replacing the YOLOv8 backbone network with MobileNetV4 and the feature extraction module C2f with ShuffleNetV2, thereby improving the real-time segmentation of crop-free ridges. Second, we use the least-squares method to fit the obtained point set to accurately obtain navigation lines. Finally, the method is applied to testing and analyzing the field experimental ridges. The results showed that the average precision of the improved neural network model using this method was 90.4%, with a Params of 1.8 M, a FLOPs of 8.8 G, and an FPS of 49.5. The results indicate that the model maintains high accuracy while significantly outperforming Mask-RCNN, YOLACT++, YOLOv8, and YOLO11 in terms of computational speed. The detection frame rate increased significantly, improving the real-time performance of detection. This method uses the least-squares method to fit the 55% ridge contour feature points under the picture, and the fitting navigation line shows no large deviation compared with the image ridge centerline; the result is better than that of the RANSAC fitting method. The research results indicate that this method significantly reduces the size of the model parameters and improves the recognition speed, providing a more efficient solution for the autonomous navigation of intelligent carrier aircraft.

There are various types of fruits and vegetables that need to be planted on ridges. In order to allow for seedlings with a certain row space and seedling space, the ridge transplanter should be able to track along the ridge. Therefore, an ultrasonic ridge-tracking method and system were developed to let the ridge transplanter track the ridge accurately. The ultrasonic ridge-tracking method mainly contains a limiter sliding window filtering algorithm and a fuzzy look-ahead distance decision model. The limiter sliding window filtering algorithm was proposed to filter the abnormal measuring results to avoid disoperation of the steering mechanism. Moreover, the fuzzy look-ahead distance decision model was proposed to determine the optimal look-ahead distance in order to obtain a desirable tracking performance. Additionally, a comparison experiment of the proposed ultrasonic ridge-tracking method and the universal pure pursuit method was conducted. The experimental results show that the greatest mean absolute errors of the lateral deviations of the ultrasonic ridge-tracking method and universal pure pursuit were 10.56 mm and 13.11 mm. The greatest maximum absolute errors of the lateral deviations of the ultrasonic ridge-tracking method and universal pure pursuit were 18.87 mm and 23.23 mm. In addition, the greatest root mean square error of the lateral deviation of the ultrasonic ridge-tracking method and the universal pure pursuit method were 13.52 mm and 15.66 mm. According to the ridge-tracking performance of the proposed ultrasonic ridge-tracking method, it can be used in practical transplanting conditions. Moreover, in other fields, robots or intelligent machinery can also apply the proposed ultrasonic ridge-tracking method to track objects similar to ridges.

A rotary tiller is a common tillage tool for straw incorporation in an intensive tillage system. However, rotary tillage for seedbed preparation in dense-straw mulching conditions experiences high torque and poor performance of straw incorporation. Nowadays, a great deal of studies have been focused on mimicking the morphological features of low-resistance animals to improve the performance of soil-engaging tools. Accordingly, the present study investigated the performance of three C-type rotary blades (i.e., conventional, serrated, and biomimetic) under three straw lengths (50, 100, and 150 mm) for incorporation of straw into the field using an in situ field tillage testing bench. Compared to the conventional and serrated blades, the biomimetic blade had lower straw displacement (decreased by an average of 50 mm and 7 mm, respectively), higher straw burying rate (increased by an average of 5.2% and 7.8%, respectively), better straw distribution (decreased by an average of 9.1% and 10.4% on the coefficient of variation, respectively), as well as lower torque and power (decreased by an average of 3.3 N·m and 4.4 N·m, respectively) under all straw lengths. The improved performance of the biomimetic blade could be attributed to the fact that its typical teeth configuration was designed by mimicking the smooth arc of the mole-rat’s claw. These results demonstrated that the biomimetic-designed blade could be a better option for incorporating dense straw into the field conditions.

The uniformity of the cultivation environment in a micro-plant factory plays a critical role in ensuring the consistent growth of seedlings, and an optimal airflow pattern is the key to maintaining environmental uniformity. This study applied computational fluid dynamics (CFD) modeling to compare the effects of six different ventilation modes on the microclimate within the cultivation space. In cases 1 and 2, the inlet was positioned at the top, while the outlets were located at both the bottom and the top of the side walls. For cases 3 to 6, a side-inlet and side-outlet ventilation system was employed across the three cultivation layers. Case 4 maintained consistent inlet and outlet airflow speeds, whereas cases 3, 5, and 6 featured airflow settings that either increased or decreased progressively from the top layer to the bottom. Notably, case 6 was characterized by a more compact arrangement of cultivation racks within the space, which were positioned closer to the outlet than in the other cases. In case 1, the air inlets were positioned at the top, while the outlets were located on both side walls at the lower layer of cultivation. In contrast, case 6 used a side-inlet and side-outlet ventilation strategy, in which the airflow speed of the inlets decreases progressively from the top to the bottom of the cultivation layers. Additionally, the cultivation racks in case 6 were arranged more compactly and positioned closer to the outlet of the cultivation space. The relative standard deviation (RSD) was used to evaluate the uniformity of the airflow velocity (m/s), temperature (K), and relative humidity (%) within the crop-growing area. The results indicated that, among all the scenarios, case 6 demonstrated the lowest RSD values for the airflow velocity, temperature, and relative humidity, with reductions of 18.34%, 0.12%, and 2.05%, respectively, compared to the control group (case 1). Based on the ventilation design of case 6, a micro-plant factory was developed featuring a bidirectional flow fan, air conditioning, and PWM fans for the coordinated control of air circulation within the seedling cultivation space, along with adjustable cultivation layer heights and shelf spacing. The accuracy of the CFD model for the micro-plant factory was validated with normalized root mean square error (NMSE) for cultivation layer heights of 250 mm, 300 mm, and 350 mm. The NMSE values comparing the simulated and measured results for the airflow velocity, temperature, and relative humidity were found to be 0.032, 0.0020, and 0.0022; 0.031, 0.0021, and 0.0018; and 0.046, 0.0021, and 0.0021, respectively. These findings indicate that the established CFD model can reliably predict the microenvironment within the micro-plant factory.

An important aspect of below-ground crop physiology is its root foraging performance, which is inherently related to root system architecture (RSA). A 2-yr field experiment was conducted and the field-state wheat RSA was phenotyped for root foraging trait (RFT). Four RSA-derived traits, i.e. Root horizontal angle (RHA), axial root expansion volume (AREV), RSA convex hull volume (CHV) and effective volume per unit root length (EVURL), were analyzed for RFTs in response to environment × management interactions. Results showed a dynamical RHA process but without statistical difference both within crop seasons and tillage treatments. AREV increased with root developmental stages, revealing an overall better root performance in the first year. However, tillage treatments did not induce observed difference within both crop seasons. CHV varied drastically from year to year and between tillage treatments, correlating well to the root length, but not with RHA. EVURL was both sensitive to tillage treatments and crop seasons, being a potential indicator for RFT. Above all, tillage effect on RFT was statistically far less than that induced by crop seasons. Pro/E assisted modeling can be used as an effective means for phenotyping integrated, RSA-derived, RFTs for root foraging response to induced environment × management interactions.