The level of mechanized harvesting of wheat in China has reached over 97%, and the impurity rate is one of the important indicators of mechanized wheat harvesting. In order to realize the online detection of the impurity rate in the wheat mechanized harvesting process, an online detection method of the wheat machine harvesting impurity rate was proposed based on the improved U-Net model combined with attention. Based on the wheat sample images collected by machine, the Labelme was used to manually label the images, and the images were enhanced by random rotation, scaling, shearing, and horizontal mirroring to construct a basic image dataset; an improved U-Net model combined with attention was designed. The model was classified and identified, and the offline training of the model was implemented under the torch 1.2.0 deep learning framework; the optimal offline model was transplanted to the Nvidia jetson tx2 development kit, and a quantification model of impurity rate was designed based on image information, so as to realize wheat on-line detection of impurity content in mechanized harvesting. The experimental results showed that the comprehensive evaluation index F1 of the improved U-Net model combined with attention was 76.64% and 85.70%, respectively, which were 10.33 percentage points and 2.86 percentage points higher than that of the standard U-Net, and 10.22 percentage points and 11.62 percentage points higher than that of DeepLabV3, which was 18.40 percentage points and 14.67 percentage points higher than that of PSPNet. Quantitative analysis of the detection results of impurity rate showed that in the bench test and field test, the average online detection of impurity rate of the device was 1.69% and 1.48%, respectively, which was higher than the manual detection by 0.26 percentage points and 0.13 percentage points. Qualitative analysis of the test results of impurity rate showed that whether it was a bench test or a field test, the test results of the device and the labor were all less than 2%. It was judged that the operation performance of the combine harvester during the test process met the national standards, and the test results were consistent. Therefore, the online detection method of wheat impurity rate proposed can provide technical support for the online quality control of wheat combined harvesting operations.

介绍了农机在新冠肺炎疫情防控中的作用,分析了疫情对江苏省农机工作的影响,提出在防控疫情的同时进一步做好农机化工作的对策建议.

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介绍了拖拉机试验中速度测量方法，主要包括五轮仪测速、雷达测速、GPS测速等。对比分析各测速方法的优缺点，指出拖拉机速度测试应考虑试验的精度要求、工作环境、成本等因素，选择最佳的测量方法。提出拖拉机试验鉴定中速度测量存在的问题及建议。

<正>本文研究的是适用性跟踪测评法,该方法是在明示的农业机械产品使用地区,选择若干有代表性的农业生产作业条件下对实际用户进行跟踪考核,利用考核结果评价农业机械适用性的方法。一、跟踪测评条件1.确定典型适用区域适用性影响因素主要包括气象条件、农艺要求、作业对象、田间作业条件、机具配套条件等,将其相对固定不变的因素

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目前,国内柴油机上铭牌功率标定的型式多种多样,如"总功率:XXXkW""最大功率:XXXkW""额定功率:XXXkW""净功率:XXXkW""标定功率:XXXkW""1h功率:XXXkW""12h功率:XXXkW""标定功率IFN:XXXkW",等等,五花八门。柴油机的功率如何定义和分类,柴油机出厂时铭牌上的功率应该如何标定（注）,一台柴油

<正>0概述稳定性是指拖拉机在坡道上行驶不致发生翻倾和滑移的性能,是评价拖拉机的坡地作业适应性的重要指标,对一些变型拖拉机,如高地隙拖拉机尤为重要。某公司开发了一款新式高地隙宽轮距拖拉机。该款高地隙宽轮距拖拉机主要用于棉花的化学调控喷药等中耕作业,其使用情况多样、复杂。拖拉机稳定性的好坏,对其使用

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<正>保护性耕作是以保水保土为核心的少耕免耕、残茬覆盖、生物覆盖和作物轮作相结合的技术体系,是减少土壤风蚀、水蚀,提高土壤肥力和抗旱能力的一项先进耕作技术。据统

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