电动机驱动玉米气吸排种器总线控制系统设计与试验

车速对电动机驱动玉米气吸式排种器排种性能具有重要影响,为此设计了一种电动机驱动排种器CAN总线控制系统,采用CAN总线通讯的方法探究系统驱动排种器随车速的变化特性。该系统主要由人机交互设备、排种监测ECU、排种驱动ECU组成,参照ISO 11783协议,对播种机具总线系统进行了设计。以4行气吸式玉米排种器为对象,搭建试验台,对总线控制排种盘转速精度进行了试验。通过总线提取的排种盘转速闭环调控结果得出,排种盘转速位置PID控制调整过程中存在低速调节时间长、超调量大的问题。采用分段PID参数控制的方法,由试验结果将排种盘转速设定值分为低速(15～20 r/min)、中速(20～40 r/min)、高速(40～55 r/min) 3个阶段,分阶段赋予对应闭环调节参数,得出排种盘目标转速在低速阶段时平均响应时间、平均超调量分别为1. 84 s、38. 51%,与位置PID控制相比较,分别降低1. 63 s、34. 41%; 15～55 r/min时平均稳态误差绝对值为0. 97 r/min,标准差为0. 76 r/min,平均稳态误差绝对值减小0. 13 r/min。进行了总线系统落种监测精度试验,设定粒距20 cm,排种盘孔数为26个,车速4～12 km/h时,系统排种监测平均准确率为97. 53%,标准差为0. 48%。采用排种总线系统对车速影响排种器性能进行了试验,风机驱动轴转速为540 r/min,车速范围为4～8 km/h,测得风压范围为-6. 0～-5. 9 k Pa,播种合格指数平均为95. 68%,标准差为2. 29%;车速达到9 km/h时,合格指数降到90%以下,排种器漏播较严重。通过对播种总线系统车速和4行排种驱动电动机实时转速的监测,进行了车速阶跃变化播种系统响应试验,结果表明在车速4～12 km/h、2 km/h间隔递增过程中,系统对排种盘目标转速平均响应时间为2. 00 s,标准差为0. 34 s; 2 km/h间隔递减过程中,系统对排种盘目标转速平均响应时间为1. 83 s,标准差为1. 07 s,表明按照车速阶跃变化,该总线控制系统具有较好的响应性能。     

播种机气动式下压力控制系统设计与试验

为保证播种机适宜的压实力和稳定的播种深度,提高种子出苗品质,促进后期生长发育,针对现有下压力测量方式灵敏度低、且缺少快速有效精准控制模型的问题,提出一种基于气囊压力和仿形四连杆倾角的播种下压力控制方法。采用一阶低通滤波的轴销传感器下压力监测方式,设计了气动式下压力监控系统,包括气压驱动装置、倾角传感器、数据采集控制卡及上位机控制软件等,轴销传感器和倾角传感器分别实时测量限深轮对地下压力和仿形四连杆倾角,并反馈给上位机,经过模型计算后控制数据采集控制卡发送信号调节气压驱动装置,保证限深轮对地下压力在设定范围内。室内建模和响应测试结果表明,在不同气囊压力和四连杆倾角设置下,建立的播种下压力控制模型校正决定系数为0. 974 3,均方根误差为49. 41 N,试验验证模型预测均方根误差为39. 51 N,对播种下压力具有较好的控制准确性;在0. 1～0. 6 MPa压力设定下,气囊充气阶跃响应平均超调量3. 83%,平均稳态误差0. 005 2 MPa,平均调节时间0. 42 s,满足作业需求。田间播种深度控制性能试验结果表明,在6～10 km/h作业速度范围内,气动式下压力控制系统对播种深度具有稳定可靠的控制性能,系统播种深度合格率不小于98. 91%,特别是在10 km/h高速作业时,播种深度标准差为3. 46 mm,变异系数为6. 97%,显著优于被动弹簧式下压力调节方式。     

智能化无人机植保作业关键技术及研究进展

搭载高性能传感器和施药装备的农业植保无人机系统是精准农业领域具有代表性的智能装备之一。本研究首先从前端田间作业环境动态感知技术出发,阐述了无人机光谱成像遥感、多传感器融合的SLAM实时环境建模等技术在无人机植保作业方面的应用情况;然后对精准施药过程建模与优化控制有关的前沿技术进行了分析,包括旋翼下方风场结构演化及雾滴沉积过程仿真建模、多区域全覆盖条件下的智能作业路径规划、精准变量施药控制等;最后论述了作业效果评估与过程监管相关技术的发展现状,包括施药作业质量评价方法、基于云平台数据管理的全过程可视化监管等。在总结现有技术发展现状基础上,对未来智能化无人机植保关键技术发展趋势进行了预测,阐明了光谱图像获取与计算智能的深度学习识别聚类、基于高精度雾滴谱和风场模型预测的精准变量施药作业路径规划、基于传感器实时数据的作业质量评估和作业监管等新技术手段,将在遥感信息反演、药液飘移抑制、作业效率优化、施药过程管控等方面带来革命性的进步,使植保作业数据化、透明化,全过程可观化可控制,推动农业生产管理从机械化向智能化和智慧化迈进。     

负水头灌溉下番茄不同生育期声发射特征研究

为探明番茄不同生育期的声发射信号特征规律,通过负水头灌溉下番茄盆栽试验,测定6 000 Pa吸力值下番茄植株耗水量、土壤含水率及声发射特征参数等指标,研究负水头灌溉下番茄不同生育期植株耗水量、土壤含水率、声发射波形信息及频谱特征参数变化。结果表明,番茄初果期和盛果期植株耗水量显著高于幼苗期和开花期,土壤含水率的变化为逐步下降后趋于稳定;对声发射信号的连续监测结果表明,番茄开花期至盛果期声发射信号波形参数呈逐渐下降的趋势,共振峰频率呈逐渐上升的趋势。Pearson相关性分析结果表明,温室环境温度和开花期土壤含水率均与声发射波形参数幅值呈显著正相关。综上,通过对番茄不同生育期的声发射信号特点、波形参数和频谱特征的比较分析,可将番茄超声发射信号数量、频率和能量与番茄生长阶段建立联系。     

基于优化植被指数的多生育期茶叶游离氨基酸含量估算

不同季节的茶叶外形和品质均具有较大差异,针对运用植被指数检测不同季节茶鲜叶游离氨基酸含量存在难度,选取了10个茶树品种3个季节（春茶、夏茶和秋茶）茶鲜叶中游离氨基酸含量数据和高光谱数据进行分析。首先,对原始光谱数据进行5种光谱变换：倒数T_1/R、对数T_1gR、一阶微分T_R’、倒数的一阶微分T_（1/R）’和对数的一阶微分T_（1gR）’,并进一步利用不同光谱变换优化了经典植被指数,最终比较了经典植被指数和优化植被指数对不同季节茶鲜叶游离氨基酸模型的影响。结果表明：茶鲜叶建模集和验证集游离氨基酸含量的变化趋势从大到小均为春茶游离氨基酸含量、秋茶游离氨基酸含量、夏茶游离氨基酸含量;光谱变换优化后的植被指数与茶鲜叶游离氨基酸含量的相关性均高于经典植被指数与茶鲜叶游离氨基酸含量相关性,相关系数绝对值范围为0.10～0.30;基于T_lgR-VI构建的多元线性回归（MLR）模型在不同季节的建模集和验证集中均得到了较好的精度,且适用于多生育时期茶鲜叶氨基酸含量的估测。基于T...     

Monitoring the number and size of pests based on modulated infrared beam sensing technology

In order to identify the number and size of pests, a sensor based on modulated infrared sensing technology with narrow-line laser beam was developed. A mathematical model between the light current change and time was established when a pest passed through the sensor. This model indicated that the factors that influence light current change not only involved the pest dimensions but also other dimensions, such as the width of narrow-line laser beam and the height between the taper collecting mouth and the narrow-line laser beam. Therefore, information on pest numbers and dimensions, obtained from similar sensors in previous literature was inaccurate. However, quantitative analysis of the model showed that the external dimensions could be neglected if an appropriate width of the narrow-line laser beam was selected. Hence, it was set at 0.0005 m in this sensor. Experimental results indicated that the sensor could eliminate the influence of ambient light and had a good precision of pest counting and classifying, which were larger than 98 and 86.7% respectively.     

基于调制近红外反射光谱的土壤养分近场遥测方法研究

土壤养分作为农业生产的重要指标，含量过少会降低农作物产量，过多则会造成环境污染。因此，快速、准确检测土壤养分对于精准施肥和提高作物产量具有重要意义。基于取样和化学分析的传统方法能够全面准确地检测土壤养分，但检测过程中土壤的取样及预处理过程繁琐、操作复杂、费时费力，不能实现土壤养分的原位快速检测。本研究基于调制近红外光谱，提出了一种土壤养分主动式近场遥测方法，可有效避免土壤反射自然光的干扰。该方法使用波长范围1260~1610 nm的8通道窄带激光二极管作为近红外光源，通过测量8通道激光光束的土壤反射率，建立土壤养分中氮（N）关于土壤反射率的计量模型，实现了N的快速检测。在74组已知N含量的土壤样品中，选取54组作为训练集，20组作为预测集。基于一般线性模型，对训练集中土壤N含量与土壤反射率的定量化参数进行训练，筛选显著波段后的计量模型R²达到0.97。基于建立的计量模型，预测集中土壤N含量预测值与参考值的决定系数R²达到0.9，结果表明该方法具有土壤养分现场快速检测的能力。     

微生物菌肥对黄河上游地区设施土壤微生物及酶活性的影响

为了研究施入微生物菌肥对黄河上游地区设施土壤微生物数量及酶活性的影响,试验以设施哈蜜瓜土壤为研究对象,设7个处理,分别为EM的不同施用方式及另外3种不同微生物菌肥,另加CK处理,在哈蜜瓜生长的不同时期取土样测定微生物数量及酶活性。结果表明,在哈密瓜同一生长时期,各处理土壤细菌、放线菌数量均比CK多;整个生长时期内,细菌、放线菌数量均先增加后减少;真菌数量采收期较苗期和膨瓜期大幅度增加,但CK增加的幅度最大,数量最多;土壤脲酶活性呈下降趋势,蔗糖酶活性先降低后升高。因此,施入微生物菌肥有利于改善设施哈密瓜土壤的微生物结构,有助于促使土壤微生物从低肥的"真菌型"向高肥的"细菌型"土壤转化;增加土壤脲酶、蔗糖酶、过氧化物酶活性。表现突出的为处理2和处理6,即"叶面喷施+根部冲施EM原液"和"冲施氮磷钾‘A50',植株喷清水"。     

Automatic image analysis and spot classification for detection of fruit fly infestation in hyperspectral images of mangoes

Fruit fly infestation of mangos is a major concern for growers and exporters, leading to requirements for quarantine treatments such as vapor heat treatment or irradiation and subsequent reduction in quality and consumer acceptance. An on-line method for detection and removal of infested fruit would thus benefit producers and consumers. An algorithm has been developed to identify spots generated in hyperspectral images of mangoes infested with fruit fly larvae. The algorithm incorporates background removal, application of a Gaussian blur, thresholding, and particle count analysis to identify locations of infestations. Each of the four algorithm steps involves adjustable parameters which were iteratively tested to find the optimal combination for detection in terms of false positive and false negative results. For algorithm parameters selected to minimize false negative results, a false negative error rate of 1.0% was achieved with 11.1% false positive error and 6.0% overall error in heavily infested samples. For the same sample set, the lowest overall error rate achieved was 2.0%, with 1.0% false positive and 3.0% false negative. For samples with lower infestation rates, the error rates were much higher, the lowest overall error being 12.3%. This therefore demonstrates the feasibility of hyperspectral imaging for fruit fly detection while highlighting the need for technology with improved resolution and signal to noise ratio to allow detection of single larvae.     

Effect of fruit moving speed on predicting soluble solids content of ‘Cuiguan’ pears (Pomaceae pyrifolia Nakai cv. Cuiguan) using PLS and LS-SVM regression

Visible (Vis)/near infrared (NIR) spectroscopy is an excellent technique for non-destructive fruit quality assessment. This research was focused on evaluating the use of Vis/NIR spectroscopy for measuring soluble solids content (SSC) of intact ‘Cuiguan’ pears (Pomaceae pyrifolia Nakai cv. Cuiguan) on-line. Also, the effect of fruit moving speed on SSC measurements was investigated. Diffuse transmission spectra were collected using a fiber spectrometer equipped with a 3648-element linear silicon CCD array detector in the wavelength range of 345–1040 nm, and all sample spectra were collected three times at different fruit moving speeds of 0.3 m s^?1, 0.5 m s^?1 and 0.7 m s^?1. Spectral pre-processing such as derivative, standard normal variate transformation (SNV) and multiplicative scatter correction (MSC) was used before calibration. Partial least squares (PLS) and least squares support vector machines (LS-SVM) were used to develop calibration models for SSC. The results show that fruit moving speed has few effects on spectra and model performance at a fruit moving speed of 0.3–0.7 m s^?1. At 0.5 m s^?1, the best model for SSC was PLS regression coupled with original spectra, its coefficient of determination (R²) and root mean square error of prediction (RMSEP) being 0.916% and 0.530%, respectively.