| Asparagus is rich in nutrients and has anti-tumor and antioxidant effects,and is known as the "king of vegetables".China’s asparagus planting area is 1.5 million mu,accounting for 40%of the world’s planting area,and asparagus production accounts for about 50% of the world’s total output.China is the largest producer and exporter of asparagus,and asparagus is also the largest single vegetable species in China’s export trade.White asparagus is planted once and harvested for many years and mature continuously during the harvesting season.The asparagus need to be harvested timely when they emerge from the ridge,otherwise they will become purple by sunlight irradiation and decrease in quality.At present,the harvesting of white asparagus internationally is still mainly artificial,with heavy workload and low efficiency,and has been faced with the severe situation of unmanned harvesting,mechanized harvesting is crucial for the sustainable development of asparagus industry.Artificial harvesting first identifies the mature spears on the ridge surface,and then complete the process of harvesting mechanism entering the soil,cutting,and collecting.The selective harvesting of white asparagus imitating artificial harvesting process is currently recognized as the best harvesting method.Moreover,asparagus is tender and juicy,easy to break and damage,and it is extremely difficult to realize mechanized intelligent harvesting.In this paper,with the goal of efficient and low-loss selective harvesting,a rapid identification model for two types of asparagus spears under the complex ridge environment was constructed.A pull-cutting end-effector was designed,and the DEM-MBD bi-directional coupling simulation method was adopted to analyze the operation process of end-effector-soilasparagus in high-speed harvesting.The intelligent control methods of “walking-lookingharvesting” and dual-arm coordination for selective harvesting of white asparagus were studied.A selective harvesting robot platform was built and field experiments were carried out.The main research contents are as follows.(1)The YOLO5-Spear method for the recognition of unearthed spear tips was proposed.Aiming at the problems of small target of spear tips,the similarity in color,texture and size between spear tips and disturbances such as soil clods and dead leaves,and insufficient robustness in the recognition of spear tips on multi-scenario complex ridge surfaces in the field,an image augmentation algorithm based on resampling was proposed.Extract the spear tip patches from the collected images of the ridge surfaces to form a multiscale combined image,and perform image augmentation transformations by simulating the field harvesting scenarios.LC3,DWConv,and SE modules were used to improve the network structure of the original model,and the YOLO5-Spear spear tip recognition model was constructed.A platform for spear tips detection in the field was built,and field experiments for the recognition of emergent spear tips were carried out.The results showed that the recognition accuracy F1 and AP of YOLO5-Spear were 97.5% and 97.8%,respectively,which were 0.93% and 2.41% higher than that of YOLOv5.The model parameters,computation amount and weight size were reduced by51.3%,33.7%,and 50.3%,respectively,and the detection time was only 9 ms.The model improved the detection accuracy and reduced the identification speed,which met the requirement of identification accuracy and real-time.Under the complex field test environment,the success rate of identifying the emergent spears reached 85.5%.(2)The HGCA-YOLO method for the recognition of invisible spears was proposed.In order to solve the problem of purple spear tips and quality degradation due to untimely harvesting,a method of recognizing invisible spears(leak morphology)was proposed for the soil leaks that appeared as spear tips grew upward against the soil under the ridge surface.The invisible spear image data set in the field environment was established,and data augmentation was carried out based on the characteristics of the targets.A lightweight invisible spear recognition model HGCA-YOLO was constructed,and the baseline network was determined by hyperparameter evolution method to obtain the best hyperparameters for the spear recognition model.Ghost module and coordinate attention mechanism were introduced into the baseline network to reduce the complexity of the model and improve the sensitivity to the target position.The test time augmentation method was introduced into the network inference to enhance the robustness of target recognition in changing environments.Field test experiment was carried out for the identification of invisible spears.The results showed that the recognition accuracy of m AP and m F1 of HGCA-YOLO model were 95.2% and 92.4% respectively,and the model size was 7.58 M,which met the requirements of spear recognition accuracy and lightweight.The success rate of invisible spears recognition reached 87.0% under the complex field test environment,providing support for the harvesting robot of white asparagus.(3)A pull-cutting end effector was designed and the interaction behavior of soil-asparagus-end-effector was studied.Aiming at the problem of high-speed and low-loss harvesting of asparagus in soil,an ex-situ net harvesting mode was proposed,and a rigid-flexible coupled pull-cutting end-effector was designed.Aiming at the complex interactions between soilasparagus-end-effector,the theoretical force analysis of the key sub-processes during harvesting was carried out,and the models of asparagus-soil complex and end-effector were established.The multi-body dynamics and discrete element bidirectional coupling method approach was used to describe the sub-processes of net harvesting in soil(move-penetrating,pull-cutting,lifting-out,and eject-throwing).The dynamics characteristics of entering the soil,cutting,pocketing the soil and asparagus,exiting from the soil and separating of soil and asparagus during the high-speed harvesting process were analyzed microscopically,and the configuration change of the end-effector as well as the interaction coupling mechanism of soilasparagus-end-effector were clarified.A test bench was built for asparagus pull-cutting and harvesting in the soil tank.The results showed that the maximum horizontal and vertical resistances of the end-effector were 445 N and 530 N during move-penetrating,respectively,and the pull-cutting resistance was 624 N.The relative errors between the test and the simulation were 5.3%,11.7%,and 15.2%,respectively.In the soil tank test,the harvest success rate was 90%,and the average single harvesting time was 3.2 s,of which,0.8 s for movepenetrating the soil,1.4s for pull-cutting,0.6 s for lifting-out the soil,and 0.4 s for ejectthrowing the asparagus.The disturbance of the ridge surface after harvesting was 15 cm in diameter and 12 cm in depth,and the disturbance was small,which verified the effectiveness of the end-effector for white asparagus harvesting.(4)The “walking-looking-harvesting” and dual-arm cooperative control methods for selective harvesting of white asparagus were studied.In order to realize efficient and stable harvesting of white asparagus under the “non-stop” mode,for multiple spears randomly distributed in the harvesting area,a dual-arm efficient harvesting collaborative algorithm with the objective of load balance was proposed.The “walking-looking-harvesting” cooperative control strategy of platform movement,asparagus spear identification and end-effector harvesting was studied.The dual robotic arms selective harvesting intelligent control system based on ROS was developed.The experimental prototype of a selective harvesting robot for white asparagus was built by integrating the above software and hardware systems,and field tests and performance evaluation were carried out.The simulation analysis of the harvesting cooperative algorithm showed that the shortest distance based first-see-first-pick(ST-FSP)scheme was optimal among the three proposed path planning schemes.Under the premise of balancing the working load,the time of double-arm harvesting was saved by 45.17% and the success rate of harvesting was increased by 3.19% compared with single-arm harvesting.Field harvesting experiment showed that the success rate of actual spear recognition was 82.6% and the average detection time was 0.033 s.The average harvesting success rate of successfully recognized spears was 92.3%,the average movement time of robotic arm was 1.7 s,and the harvesting time of end-effector was 5.7 s.The damage rate of the asparagus was 7.2%,the ridge surface was less destructive,and the mechanisms operated smoothly during harvesting,which initially verified the feasibility of the selective harvesting robot for white asparagus with high efficiency and low loss. |
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