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大田甘藍作物行識別與對行噴霧控制系統(tǒng)設(shè)計與試驗
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江蘇省重點研發(fā)計劃項目(BE2021302),、江蘇省農(nóng)業(yè)科技自主創(chuàng)新資金項目(CX(21)2006)、北京農(nóng)業(yè)智能裝備技術(shù)研究中心開放項目(KFZN2021W001)和天山創(chuàng)新團隊項目(2021D14010)


Design and Experiment of Row Identification and Row-oriented Spray Control System for Field Cabbage Crops
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    摘要:

    對行噴霧技術(shù)可提高農(nóng)藥的利用率,,有利于保護環(huán)境和減少農(nóng)藥殘留,。本文搭建基于機器視覺的大田甘藍對行噴霧控制系統(tǒng)。通過改進的ExG算法提取顏色信息,,采用最大類間方差法和形態(tài)學(xué)的開閉運算分割作物與背景,。提出甘藍作物行定位與多作物行自適應(yīng)ROI提取方法,在條帶分割的ROI內(nèi)基于限定閾值垂直投影對特征點集進行采集,,通過最小二乘法對特征點集進行線性擬合得到作物行中心線,。利用中心線幾何關(guān)系得到作物行偏移信息,根據(jù)對行機構(gòu)的運動特性建立對行偏移補償模型,,并設(shè)計基于PID軌跡追蹤算法的對行噴霧控制系統(tǒng),。試驗結(jié)果表明,實驗室作物行識別準確率為95.75%,,算法平均耗時為77ms,。在田間試驗中,,識別算法在時間段09:00—11:00,、14:00—16:00內(nèi)測試效果最佳,識別偏差均值保持在2.32cm以下,。針對不同范圍的雜草測試中,,算法平均識別成功率為95.56%,說明算法具有較強的魯棒性,。在與其他識別算法對比測試中,,本文算法平均耗時最短,識別成功率最高,,能夠為實時作業(yè)提供視覺引導(dǎo),。在對行噴霧控制系統(tǒng)田間試驗中,對行準確率達到93.33%,,對行控制算法可將對行偏差控制在1.54cm,,滿足田間實際應(yīng)用要求。

    Abstract:

    Row-oriented spraying technology can improve the utilization rate of pesticides, protect the environment and reduce pesticide residues. A vision based row-oriented spray control system for field cabbage was established. The improved ExG algorithm was used to extract color information, and the method of OTSU and morphological opening and closing operation were used to segment crops and background. A method of cabbage crop row localization and multi row adaptive ROI extraction was proposed. In the ROI of strip segmentation, the feature point set was collected based on the limited threshold vertical projection, and the crop row centerline was obtained by linear fitting of the feature point set by the least square method. The offset information of crop rows was obtained based on the geometric relationship of the centerline. A row offset compensation model was established based on the kinematic characteristics of the row mechanism, and row-oriented spray control system based on PID trajectory tracking algorithm was designed. Laboratory tests showed that the accuracy of crop row recognition was 95.75%, and the average algorithm time-consuming was 77ms. Field tests showed that under different periods of illumination, the recognition algorithm had the best test results in the time periods of 09:00—11:00 and 14:00—16:00, and the average recognition deviation was kept below 2.32cm. In the weed press test, the average accuracy rate of the recognition algorithm was 95.56%, indicated that the algorithm had strong robustness.in the comparison test with other recognition algorithms, the algorithm proposed had the shortest average time consumption and the highest recognition accuracy rate, and it could be used for real-time operations.in the field row-oriented spray control system tests, the system row-oriented accuracy rate reached 93.33%, and the control algorithm could control row-oriented deviation within 1.54cm, which could meet the requirements of practical field applications.

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韓長杰,鄭康,趙學(xué)觀,鄭申玉,付豪,翟長遠.大田甘藍作物行識別與對行噴霧控制系統(tǒng)設(shè)計與試驗[J].農(nóng)業(yè)機械學(xué)報,2022,53(6):89-101. HAN Changjie, ZHENG Kang, ZHAO Xueguan, ZHENG Shenyu, FU Hao, ZHAI Changyuan. Design and Experiment of Row Identification and Row-oriented Spray Control System for Field Cabbage Crops[J]. Transactions of the Chinese Society for Agricultural Machinery,2022,53(6):89-101.

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  • 收稿日期:2022-01-17
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  • 在線發(fā)布日期: 2022-04-02
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