| 蔬菜穴盘育苗播种漏播检测及智能补种装置设计与试验 |
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| 引用本文： | 马旭,杨传艺,谭穗妍,曹秀龙,秦亦娟,陈嘉盈,余杰,王曦成.蔬菜穴盘育苗播种漏播检测及智能补种装置设计与试验[J].农业工程学报,2024,40(6):168-180. |
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| 作者姓名： | 马旭 杨传艺 谭穗妍 曹秀龙 秦亦娟 陈嘉盈 余杰 王曦成 |
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| 作者单位： | 华南农业大学工程学院，广州 510642;华南农业大学电子工程学院，广州 510642;河北北方学院农林科技学院，张家口 075132 |
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| 基金项目： | 国家重点研发计划项目（2021YFD2000702-3）；河北省高等学校科学技术研究项目（QN2023237）；河北北方学院自然科学研究计划项目（XJ2023025） |
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| 摘 要： | 为保证植物工厂蔬菜穴盘育苗高质量作业要求，该研究在气吸滚筒式蔬菜穴盘育苗精密播种器的基础上，优化设计了在线漏播检测与智能补种装置，以可编程逻辑控制器（programmable logic controller, PLC）为控制核心，实时进行播种器吸孔漏吸检测及穴盘穴孔漏播位置预报，并完成漏播穴孔的定点定穴补种。采用光电检测技术检测播种器吸孔漏吸位置，构建漏吸吸孔与育苗穴盘穴孔的对应动态补种矩阵，实现穴盘穴孔漏播位置精准预报；优化设计了智能补种装置，根据预报的穴盘穴孔漏播位置实现定点定穴精准补种。以中双11号菜心种子为对象，开展播种器吸孔漏吸检测与穴孔漏播位置预报试验，得到吸孔漏吸平均检测准确率为98.82%，穴孔漏播位置预报准确率为100%。采用Box-Behnken试验设计方法，对智能补种装置开展作业性能试验，构建主要性能指标（单粒合格指数、重播指数和漏播指数）与主要影响因素（吸针负压、吸针孔径和种室振动压力）的关系，并进行多目标优化，确定智能补种装置最优工作参数组合为吸针负压10.19 kPa、吸针孔径0.67 mm、种室振动压力0.07 MPa，此时补种装置播种的平均单粒合格指数为94.80%、重播指数为2.94%、漏播指数为2.26%。开展整机性能试验，在生产率为100盘/h条件下，整机的单粒合格指数由补种前的93.96%提高到98.18%；在生产率为300盘/h条件下，单粒合格指数由补种前的93.18%提高到97.89%。试验结果满足植物工厂和大田蔬菜穴盘育苗播种装置高精密播种作业要求，可提高蔬菜穴盘育苗的播种性能。
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| 关 键 词： | 农业机械 自动化 蔬菜穴盘育苗 漏播检测 补种 |
| 收稿时间： | 2023/10/19 0:00:00 |
| 修稿时间： | 2024/1/15 0:00:00 |
Design and experiments of the miss-seeding detection and intelligent reseeding device for vegetable pot tray seedling |
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| MA Xu,YANG Chuanyi,TAN Suiyan,CAO Xiulong,QIN Yijuan,CHEN Jiaying,YU Jie,WANG Xicheng.Design and experiments of the miss-seeding detection and intelligent reseeding device for vegetable pot tray seedling[J].Transactions of the Chinese Society of Agricultural Engineering,2024,40(6):168-180. |
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| Authors: | MA Xu YANG Chuanyi TAN Suiyan CAO Xiulong QIN Yijuan CHEN Jiaying YU Jie WANG Xicheng |
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| Institution: | College of Engineering, South China Agricultural University, Guangzhou 510642, China;College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China;College of Agriculture, Forestry and Science, Hebei North University, Zhangjiakou 075132, China |
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| Abstract: | This study aims to ensure the high-quality operation requirements of vegetable seedlings in a plant factory. A series of tests were performed on the precision seeding device of the vegetable pot tray with high accuracy. A device was designed and optimized for online miss-seeding detection and intelligent reseeding, according to the pneumatic roller-type device of precision seeding. Furthermore, a programmable logic controller (PLC) was used as the control core, in order to ensure the stability of the control system in the production line. The real-time miss-seeding detection of suction holes was realized to predict the miss-seeding location of the pots. The intelligent fixed-point was timely completed for the precise reseeding of the missed-seeding location pots. The performance of miss-seeding detection was then optimized in the production process, according to the structural characteristics of the pneumatic roller-type seed-metering device. A specific arrangement of photoelectric sensors was used to realize the real-time detection of miss-seeding suction holes. Subsequently, a dynamic reseeding matrix was constructed corresponding to the suction holes and pots of the pot tray, according to the pot''s position and number of the pot tray. The miss-seeding location of the pot tray was also predicted. Furthermore, the intelligent reseeding device was designed and optimized to realize intelligent and accurate reseeding at the miss-seeding holes. The miss-seeding location of the pots was extracted from the data from the dynamic reseeding matrix. Taking vegetable seeds named Zhongshuang No.11 as the test materials, the suction holes miss-seeding detection and pots miss-seeding location prediction were carried out on the reliability of miss-seeding detection. The results that the average accuracies of miss-seeding suction holes detection and pots miss-seeding location prediction were 98.82% and 100%, respectively. Furthermore, the Box-Behnken method was used to evaluate the operational performance of the intelligent reseeding device. The relationship between the performance indexes (single seed qualified index, multiple seeding index, and miss-seeding index) and the influencing factors (negative pressure of suction needle, diameter of suction needle, and position pressure of vibrator) was constructed using multi-objective optimization. The optimal working parameters of the intelligent reseeding device were determined to be the negative suction pressure of 10.19 kPa, the diameter of 0.67 mm, and the vibration pressure of 0.07 MPa after a large number of tests and analyses. In this case, the values of working parameters were rounded to facilitate the test. It was found that the average single-seed qualified index was 94.80%, the multiple seeding index was 2.94%, and the miss-seeding index was 2.26%. The intelligent reseeding device under this condition fully met the reseeding requirements of the miss-seeding detection and reseeding device. The performance test of the miss-seeding detection and reseeding device was carried out to verify the model. Once the productivity of the miss-seeding detection and reseeding device was 100 plates/h, the single-seed qualified index of the miss-seeding detection and reseeding device increased to 98.18%, compared with 93.96% before reseeding. When the productivity of the miss-seeding detection and reseeding device was 300 plates/h, the single-seed qualified index of the miss-seeding detection and reseeding device increased to 97.89%, compared with 93.18% before reseeding. The test results fully met the requirements of high precision seeding in vegetable pot seeding devices in plant factory and field conditions. The practical application value was offered to improve the seeding performance of vegetable pot seeding devices. The finding can provide technical support for the production of high-quality vegetable pot seedlings. |
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| Keywords: | agricultural machinery automation vegetable pot seedling miss-seeding detection reseeding |
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