At present, there are excessive fertilizer use and poor uniformity of fertilizer discharge in corn fertilizer planter. The key difficulty is that accurate perception and control of fertilizer amount has not been solved. Aiming at the above problems, a set of accurate perception and control system applied to corn fertilization planter was studied. According to the difference in dielectric properties between fertilizer and air, a sensor for online detection of fertilizer amount based on capacitance method was designed. And the relationship model of mass flow rate for N, P, K fertilizer and capacitance output was established. In order to reduce the influence of pulsation on fertilization flow, a high-precision fertilizer flow control system for fertilization planter based on the fertilizer flow feedback and PID control method was designed. The validated results showed that the maximum measurement error between the relationship model and capacitance output was 3.75%. As the temperature rises from room temperature to 55°C, the differential capacitance change rate of the sensor was less than 3%. The steady-state error of fertilizer discharge was less than 2%. The field experiment of the accurate perception and control system for corn fertilization amount show that the electric drive fertilization system has good consistency, the maximum and average variation coefficient of fertilization were 3.74%, 1.6%, respectively, and the variable control accuracy was greater than 97%. The control accuracy of the grain spacing control by electric drive seed metering was 98%. Therefore, the precision fertilization control system in this study can realize high-precision and on-demand fertilization. It is of great significance to realize the intelligence and precision for corn fertilization planter.

The non-destructive harvesting of fresh tomatoes with agricultural robots requires the robotic arm to approach the fruit with the correct posture to ensure successful harvesting. However, this process faces significant challenges due to the small size of fruit pedicels, cluttered environments, and varied poses of the tomatoes and pedicels. Accurately identifying, localizing, and estimating the 6D spatial pose of the cutting points is critical for efficient and non-destructive harvesting. To address these challenges, we propose a keypoint-based pose estimation model, TomatoPoseNet, tailored to meet the agronomic requirements of tomato harvesting. The model integrates an efficient fusion block (EFBlock) based on the CSPLayer, referred to as the CSEFLayer, as the backbone network, designed to fuse multiscale features while maintaining efficient computational resource usage. Next, a parallel deep fusion network (PDFN) is utilized as the neck network to integrate features from multiple parallel branches. Furthermore, simple coordinate classification (SimCC) is employed as the head network for keypoint detection, and a StripPooling block is introduced to enhance the model’s ability to capture features of different scales and shapes by applying strip pooling in horizontal and vertical directions. Finally, a geometric model is constructed based on the information about the predicted 3D keypoints to estimate the 6D pose of the cutting points. The results show the following: (1) The average precision for keypoint detection (PCK@0.05) reached 82.51%, surpassing those of ViTPose, HRNet, Lite-HRNet, Hourglass, and RTMPose by 3.78%, 9.46%, 11%, 9.14%, and 10.07%, respectively. (2) The mean absolute errors (MAEs) of the yaw and pitch angles for 6D pose estimation of the cutting points were 2.98° and 3.54°, respectively, with maximum errors within 6.5°, meeting the requirements for harvesting. The experimental results demonstrate that the proposed method can accurately locate the 6D pose of cutting points in an unstructured tomato harvesting environment, enabling non-destructive harvesting.

Design and parameters optimization of the curved sieve for an air suction jujube harvester

Full-condition monitoring and intelligent yield prediction and decision-making technology for wheat combine harvesters

While the root architecture of potted crop seedlings directly determines subsequent crop productivity and adaptability, these root systems remain challenging to quantify using conventional methods due to their structural complexity. To investigate the microscopic characteristics of the root systems of pepper seedlings within pots, Micro-CT was employed to scan the seedling pots. After three-dimensional (3D) reconstruction was conducted on the data acquired from the pot scans, the 3D model of the root system was segmented and extracted using the watershed algorithm. Vertically, the three-dimensional root model was divided from top to bottom into four equally spaced regions (a, b, c, and d), showing the volumetric distribution characteristics of pepper seedling roots within the pots. The results showed that region a had the largest average root volume proportion (29.72%), primarily due to the substantial volume contribution of the taproot. Region d followed with an average proportion of 27.26%, resulting from root coiling and entanglement at the pot bottom caused by the spatial constraints of the seedling tray. The middle regions of the pot, b and c, showed average root volume proportions of 23.14% and 19.89%, respectively. To further investigate the influence of root system characteristics on root injury during seedling gripping, the seedlings were categorized into three types based on their taproot growth positions. A gripping experiment was conducted on these three seedling types using spatula-equipped needles. The results showed that the greatest root injury (12.67%) was observed in Type 1 seedlings, which had taproots located closest to the needle insertion point. In contrast, the least injury (4.09%) was found in Type 3 seedlings, characterized by centrally positioned taproots. Type 2 seedlings, with their taproots growing on the side (laterally away from the insertion point), sustained intermediate injury (5.45%). This was because their lateral positioning led to an uneven distribution of mechanical stress during gripping compared with Type 3 seedlings. A validation experiment conducted on an automated seedling retrieval platform confirmed the root injury analysis. The experimental results showed maximum root injury in Type 1 seedlings (14.16%), followed by Type 2 (6.03%) and Type 3 (4.82%) seedlings, with a successful retrieval rate of 95.29%. These findings were consistent with the Micro-CT analysis. This study could provide a theoretical foundation for low-injury seedling gripping in fully automated seedling transplanters.

To improve the working efficiency of cottonseed delinting machine, an automated measurement and control system for the machine is designed. The system hardware is comprised of three torque sensor, master controller, data acquisition and regulation module, displacement transducer, linear actuator and variable frequency device. The operating parameters such as the rotation torque and speed of every delinting cylinder and outlet opening are obtained on line by the apparatus and then sent to the master controller. With the help of LabWindow/CVI, the master controller program is developed to get and deal with the information. Since the feed rate and discharge rate directly affect the working load of the cylinders, the speed of feeding motor and outlet opening can be manually regulated by means of the program to facilitate the working load test experiment. In order to achieve the optimization load allocation of the delinting cylinder, the optimal decision is made on the basis of the third cylinder’s working load to regulate the outlet opening. The experiment results show that the outlet opening regulation accuracy reaches ±1mm.This system is also easy to use and provide a good graphical user interface and has higher accuracy, higher efficiency for the real-time control.