Safflower is one of the most important oil crops worldwide. To improve the efficiency and quality of harvesting soft safflower filaments, minimize filament damage by the blade, and ensure the integrity of the filaments, an end-effector with a rotary push-cut-type negative-pressure airflow was designed. The design of the rotary blade edge curve was based on Archimedean and logarithmic spirals. The effect of the Archimedean spiral on the pushing of the scattered filaments was investigated. The rotary-cutting force and speed of the logarithmic helix on the soft filaments were analyzed. The sliding cut angle and feed speed of the blade are the key factors governing filament removal and damage. A simulation model of the cutting chamber flow field was established for the change in airflow with the position and speed rotation of the blade, allowing analysis of the parameters of the negative-pressure airflow. Single-factor and Box-Behnken tests were performed with the sliding cut angle, blade feed speed, and negative-pressure airflow speed as influencing factors and filament removal, damage, and drop rate as indices. The response surface clarified the effects of various factors on these indices. Optimal parameters were a sliding cut angle of 32.20°, a blade feeding speed of 0.031 m/s, and a negative-pressure airflow speed of 4.57 m/s. The corresponding filament removal, damage, and drop rates were 93.47%, 6.94%, and 4.33%, respectively. Optimization results showed that filament removal, damage, and drop rates were 93.50%, 7.02%, and 4.43%, respectively. The harvesting process met the requirements of high efficiency and low damage.

A suitable picking method for the end-effector is the key to achieving low-damage picking. Due to the high moisture content of fresh safflower and its fragile filaments, achieving low-damage picking is a challenge for most picking robots. In this paper, a clamping and root-cutting end-effector for harvesting fresh safflower is developed. It utilizes dual rollers to pre-clamp the filaments and a blade to root-cut the safflower necking to realize the safflower harvesting operation. A mechanical model of dual rollers-blade-filaments cutting was constructed to theoretically analyze the harvesting process. The key factors affecting the performance of the end-effector were identified as blade feed speed, rotation speed, and roller clearance. Using the filament removal rate and damage rate as evaluation indices, the central composite design was carried out. The mathematical model between the test factors and evaluation indices was obtained, and the regression model was optimized with multiple objectives. Finally, the optimal combination of parameters was determined to be a blade feed speed of 40 mm/s, a rotation speed of 318 r/min, and a roller clearance of 0.60 mm. The field test was conducted under the optimal parameter combination, and the filament removal rate and damage rate were 91.25% and 5.57%, respectively. The optimized results had an error of less than 5%, indicating that the designed end-effector can harvest filaments with high removal and low damage rates. This study provides a reference for the low-damage harvesting of fresh safflower.

Understanding the biomechanical properties of safflowers is essential for appropriately designing harvesting machinery and optimizing the harvesting process. Safflower is a flexible crop that lacks a basis for relevant simulation parameters, which causes difficulties in designing harvesting machinery. In this study, a calibration method for safflowers was proposed. First, a discrete element model was established by measuring the intrinsic parameters of a safflower, such as its geometric parameters, density, Poisson’s ratio, and modulus of elasticity. Second, the contact and bonding parameters were calibrated using a combination of physical and simulation tests. In the contact parameter tests, the Hertz-Mindlin (no-slip) model was implemented for the stacking angle tests conducted regarding the safflower filament. A regular two-level factorial design was used to determine the important factors and perform the steepest climb test. Moreover, the Box-Behnken design was adopted to obtain the optimal contact parameters. In the bonding parameter tests, the Hertz-Mindlin model with bonding contact was applied for the safflower shear simulation tests; moreover, the optimum bonding parameters were obtained through the central composite design test. The results demonstrated that the relative errors between the simulated and measured stacking angles and maximum shear were 3.19% and 5.29%, respectively. As a result, the safflower simulation parameters were accurately calibrated, providing a reference for appropriately setting the simulation parameters and designing key mechanical components.

Harvesting robots had difficulty extracting filament phenotypes for small, numerous filaments, heavy cross-obscuration, and similar phenotypic characteristics with organs. Robots experience difficulty in localizing under near-colored backgrounds and fuzzy contour features. It cannot accurately harvest filaments for robots. Therefore, a method for detecting and locating filament picking points based on an improved DeepLabv3+ algorithm is proposed in this study. A lightweight network structure, ShuffletNetV2, was used to replace the backbone network Xception of the traditional DeepLabv3+. Convolutional branches for 3 different sampling rates were added to extract information on the safflower features under the receptive field. Convolutional block attention was incorporated into feature extraction at the coding and decoding layers to solve the interference problem of the near-color background in the feature-fusion process. Then, using the region of interest of the safflower branch obtained by the improved DeepLabv3+, an algorithm for filament picking-point localization was designed based on barycenter projection. The tests demonstrated that this method was capable of accurately localizing the filament. The mean pixel accuracy and mean intersection over union of the improved DeepLabv3+ were 95.84% and 96.87%, respectively. The detection rate and weights file size required were superior to those of other algorithms. In the localization test, the depth-measurement distance between the depth camera and target safflower filament was 450 to 510 mm, which minimized the visual-localization error. The average localization and picking success rates were 92.50% and 90.83%, respectively. The results show that the proposed localization method offers a viable approach for accurate harvesting localization.