免耕对土壤养分的影响

系统分析了免耕对土壤N、P、K、S、微量元素以及pH值的影响,并提出了N、P、K肥和石灰、石膏的施用方法.     

不同施肥制度对大豆生育期土壤微生物量的影响

通过测定不同施肥制度下大豆土壤微生物量C、N的动态变化,探讨了不同施肥制度对大豆土壤的培肥效应。研究结果表明,与无肥、单施有机肥、单施化肥相比,有机肥与N、P、K化肥配合施用能显著增加大豆各生育时期的土壤微生物量C、N,促进土壤微生物量显著增长,增强了土壤养分容量的供应强度,有利于培肥土壤。     

长期定位施肥对花生产量和品质的影响

在1997~2003年进行长期定位施肥的基础上,研究了氮、磷、钾配合施用对花生产量和品质形成的研究。结果表明:与不施肥处理相比,长期施用N肥可以提高花生籽粒产量和籽粒中蛋白质含量;施用NK肥,籽粒中粗脂肪含量降低,施用NP肥,降低了籽粒中蛋白质含量;施用PK肥,降低籽粒中蛋白质含量:施用NPK肥,显著地提高了籽粒产量和蛋白质含量,说明NPK肥配合施用有利于籽粒产量的提高和籽粒品质的形成,有利于保持和提高土壤肥力。     

河西走廊旱塬长期定位施肥对土壤理化性质及春小麦增产效果的研究

在河西走廊旱塬灌漠土上连续11年肥料定位试验结果表明:有机肥料、化学N肥、P肥3种肥料配合施用,对土壤理化性质和小麦产量有良好的影响。其中有机肥料与化学N肥、P肥(MNP)配合施用与化学N肥、P肥(NP)配合施用比较,0~20 cm耕层土壤有机质、碱解N、速效P、速效K分别增加1.40g kg-1、14.01mg kg-1、7.04 mg kg-1、14.80mg kg-1;土壤总孔度、团粒结构、自然含水量、土壤贮水量分别增加4.15%、8.32%、66.85g kg-1、177.15m3hm-2;小麦产量、产值、施肥利润分别增加1.94 t hm-2、0.21×104元hm-2、0.14×104元hm-2;肥料投资效率达1.39元/元。不同处理间的肥效是MNP>MN、MP、NP>M、N、P>CK。     

新型包膜肥在早熟禾上的试验研究

通过自制长效包膜肥与常规施肥在草地早熟禾盆栽上的试验研究表明,在同一施肥量的情况下作基肥一次施入,长效包膜肥在整个试验期间可以满足早熟禾整个试验期对养分的需求,但常规施肥则出现了缺肥症状。包膜肥加工过程及加入缓释剂对N素养分和P、K养分的释放规律有所不同:缓释剂的加入有利于提高N肥效但却加速了肥料中P、K的释放;包膜却对N、P、K均有延缓释放的作用。     

覆膜旱作稻N、P、K养分利用特征

在田间试验条件下对覆膜旱作稻体内N、P、K养分浓度、吸收动态及N肥利用率作了研究。结果表明,相同施肥处理条件下不同生育期覆膜旱作稻N、P、K养分浓度及吸收量均高于水作稻,尤以生育中后期较为明显;覆膜旱作稻全生育期N、P、K养分吸收量明显高于水作稻,分布在籽粒中的N、P、K养分含量也有所提高,而在整株中的占有率差异不大,N肥利用率提高12%左右。     

利用硅藻土提高化肥肥效的研究

通过对硅藻土结构观察、养分吸附和不同硅藻土与肥料比例肥效试验,结果表明,硅藻土具有多孔质结构,湿润后孔隙明显增大,对N、P有较强吸附作用,能吸附2.5%的N和37.4%的P;施用1∶10硅藻土肥的比施用化学肥料的增产9.8%,比施用化学肥料的多吸收20.4%的N,18.6%的P和12.8%的K,值得在生产上推广应用。     

不同NPK配置对强筋小麦群体生物量与产量的效应研究

在关中川道灌区以强筋小麦陕253为材料,研究了不同NPK配置对其不同生育期生物产量、群体数量和产量的效应,结果表明提高施肥量可显著增加生物产量、群体数量及产量,处理平均值分别比常规处理增加11.9%~43.6%、4.1%~17.4%和8.3%,其最佳N、P、K配置依次为N135kg hm-2,P2O5和K2O各120kg hm-2、N135kg hm-2,P2O5225kghm-2,K2O120kg hm-2和N225kg hm-2,P2O5和K2O各120kg hm-2。在目前常规投肥水平下适度增加钾肥有利于生物产量的显著提高,增加磷肥和钾肥有利于群体数量的提高,增施氮钾肥有显著的增产效果。产量的最佳NPK配比较常规处理增产19.1%,其增加养分的平均产量4.44 kg kg-1,比各处理平均养分产量高1.6倍。     

含钾有机废弃物糠醛渣改土培肥效应研究

在河西走廊的盐化潮土上设置了糠醛渣的改土培肥田间试验,结果表明:甜菜施用糠醛渣与施用化肥比较,土壤容重、全盐、pH分别降低0.21g cm-3、0.36g kg-1、0.37;总孔度、自然含水量、贮水量、团粒结构、有机质、速效N、P、K、CEC分别增加7.93%、79.90g kg-1、131.95m3 hm-3、4.75%、0.61g kg-1、12.96mg kg-1、1.24mg kg-1、6.78mg kg-1、1.89cmol kg-1。施肥成本降低90元hm-2,施肥利润增加1716元hm-2。且施用糠醛渣能提高土壤中磷的活性和磷的利用率。     

黄花菜配方施肥技术研究

通过对黄花菜盆栽砂培试验、田间正交和氮磷肥单因素试验,研究了黄花菜配方施肥技术。结果是:黄花菜对N∶P2O5∶K2O三要素的最佳比例为2∶1∶2;对N、P、K三要素的总量以每亩40kg为宜(包括土壤供给量)。对不同肥力的土壤,每亩施用量的计算公式是:土壤中黄花菜可利用速效养分含量-每亩土壤速效性养分含量×黄花菜根系实际占用营养面积/栽培面积;在试验土壤条件下,要求施入N素9.5~1 3.5kg,P2O56.6~8.4kg;N、P单独施用,应酌量增加;N、P配合施用应适当减少;最佳的施肥时期是三月下旬一次施入。     

辽宁省农田杂草种类和分布的调查研究

农田杂草种类和分布的调查研究对制订综合除草措施,开发利用植物资源和盘点基因库都是重要的.九年调查,辽宁省杂草(维管束植物)83科359属756种。全省划分六个杂草分布区:1.辽东湿润山区;2.半岛半湿润丘陵区;3.西北半干旱风沙区;4.辽河上中游半湿润低丘平原区;5.辽河口及滨海湿润、半湿润平原区;6.辽西半湿润半干旱山地丘陵区。描述了各区自然条件和杂草种类,讨论了杂草分布规律,提出78种主要防治对象及杂草利用的建议。     

基于颜色特征的杂草图像分割技术研究

为了解决目前杂草识别中受光照影响大、环境适应性差等问题,提出了基于颜色特征的分割算法。此算法在统计分析杂草和土壤背景各颜色因子的基础上,得到适于杂草图像分割的颜色分量,实现了复杂场景、光照条件下杂草区和背景区的分割。实验结果表明:R-G,2G-R-B,Hmean,Smean,Hmean Smean颜色特征对于杂草区和背景区的分割能够取得很好的效果,可广泛应用于田间杂草识别、树种识别、人脸识别等受光照变化影响较大的领域。     

变邻域杂草算法在多目标柔性作业车间调度中的应用*

针对最小化最大完工时间、总机床负荷最小及最大负载最小的多目标柔性作业车间调度问题,提出了变邻域杂草算法。首先,基于随机键编码方式,构造单链杂草,实现了杂草空间到调度空间的映射。其次,迭代后期执行变邻域搜索,对精英杂草局部深入挖掘,并通过反解码过程将调度空间的优良解反馈回杂草空间。对比实验表明,变邻域杂草算法在求解多目标基准问题时,非劣解集中解的数量和质量有一定优势。变邻域杂草算法是求解多目标柔性作业车间调度问题的有效方法。     

Estimation of plant species by classifying plants and leaves in combination

Information on which weed species are present within agricultural fields is a prerequisite when using robots for site‐specific weed management. This study proposes a method of improving robustness in shape‐based classifying of seedlings toward natural shape variations within each plant species. To do so, leaves are separated from plants and classified individually together with the classification of the whole plant. The classification is based on common, rotation‐invariant features. Based on previous classifications of leaves and plants, confidence in correct assignment is created for the plants and leaves, and this confidence is used to determine the species of the plant. By using this approach, the classification accuracy of eight plants species at early growth stages is increased from 93.9% to 96.3%.     

综合颜色和形态特征的小麦田杂草识别方法

利用机器视觉技术把杂草精确识别出来是精细农业领域研究的热点问题之一。针对杂草与小麦叶子交叠的情况，提出一种综合颜色和形态特征的方法进行杂草识别。在L*a*b*颜色空间，选取a*作为特征量并用改进的最大类间方差法进行阈值分割获得植物图像；在HSI颜色空间，利用多层的同质性分割算法分离小麦与杂草；结合形态学特征开闭运算滤波及二值逻辑与运算获得杂草图像；模拟化学除草系统，从理论上评价整个系统的识别效率。实验结果表明：杂草正确识别率高达92.6%以上，且除草剂的使用量减少超过72.4%。     

Uncertainty analysis and evaluation of a complex,multi-specific weed dynamics model with diverse and incomplete data sets

Weed dynamics models are needed to test prospective cropping systems but are rarely evaluated with independent data (“validated”). Here, we evaluated the FlorSys model which quantifies the effects of cropping systems and pedoclimate on multispecific weed dynamics with a daily time step. We adapted existing validation methodologies and uncertainty analyses to account for multi-specific, multi-annual and diverse outputs, focusing on missing input data, incomplete and imprecise weed time series. Field data ranged from entirely monitored cropping system trials to annual snapshots recorded on farm fields by the French Biovigilance-Flore network. FlorSys satisfactorily predicted weed seed bank, plant densities and crop yields, at daily and multi-annual scales, at well monitored sites. It overestimated plant biomass and underestimated total flora density. Missing processes (photoperiod dependency in flowering, crop:weed competition for nitrogen) and inadequately predicted scenarios (weed dynamics in untilled fields, floras with summer-emerging species) were identified. Guidelines for model use were proposed.     

Bayesian classification and unsupervised learning for isolating weeds in row crops

This paper presents a weed/crop classification method using computer vision and morphological analysis. Subsequent supervised and unsupervised learning methods are applied to extract dominant morphological characteristics of weeds present in corn and soybean fields. The novelty of the presented technique resides in the feature extraction process that is based on spatial localization of vegetation in fields. Features from the weed leaf area distribution are extracted from the cultivation inter-rows, then features from the crop are inferred from the mixture model equation. Those extracted features are then passed to a naive bayesian classifier and a gaussian mixture clustering algorithm to discriminate weed from crop plant. The presented technique correctly classifies an average of 94 % of corn and soybean plants and 85 % of the weed (multiple species) without any prior knowledge on the species present in the field.     

基于改进 YOLOv4 和图像处理的蔬菜田杂草检测

以蔬菜苗田内幼苗期 7 种常见蔬菜和田间杂草为研究对象,针对田间杂草种类多和分布复杂导致检测方法效率低、精度差和鲁棒性不足等问题,逆向将杂草检测转换为作物检测,提出一种基于优化 YOLOv4和图像处理的蔬菜苗田杂草检测算法。在 YOLOv4 目标检测算法基础上,主干特征提取网络嵌入 SA 模块增强特征提取能力,引入 Transformer 模块构建特征图长距离全局语义信息,改进检测头和损失函数提高检测定位精度。改进模型单幅图像平均识别时间为 0.261 s,平均识别精确率为 97.49%。在相同训练样本以及系统环境设置条件下,将改进方法与主流目标检测算法 Faster RCNN,SSD 和 YOLOv4 算法对比,结果表明改进 YOLOv4模型在蔬菜苗期的多种蔬菜检测具有明显优势。采用改进 YOLOv4 目标检测算法检测作物,作物区域外的植被为杂草,超绿特征结合 OTSU 阈值分割算法获取杂草前景,最后标记杂草前景连通域输出杂草质心坐标和检测框位置,可以较好解决蔬菜苗田杂草检测问题。     

基于无尺度网络的行间作物杂草清除方法

为了减少除草剂在经济作物上的使用,降低除草剂对环境的压力,本文提出采用机器视觉识别杂草网络、对行间作物杂草无尺度网络摧毁的方法.通过机器视觉识别出作物、土壤和杂草,依据植化物质的作用建立并绘制杂草无尺度网络,通过对杂草网络的节点偏好性、增长性和聚类性的研究,发现杂草网络对随机节点故障具有鲁棒性,对蓄意攻击具有脆弱性,依据此特点提出摧毁杂草网络节点的方法.与现有方法相比,新方法符合生态经济管理原则.     

An Agricultural Mobile Robot with Vision-Based Perception for Mechanical Weed Control

This paper presents an autonomous agricultural mobile robot for mechanical weed control in outdoor environments. The robot employs two vision systems: one gray-level vision system that is able to recognize the row structure formed by the crops and to guide the robot along the rows and a second, color-based vision system that is able to identify a single crop among weed plants. This vision system controls a weeding-tool that removes the weed within the row of crops. The row-recognition system is based on a novel algorithm and has been tested extensively in outdoor field tests and proven to be able to guide the robot with an accuracy of ±2 cm. It has been shown that color vision is feasible for single plant identification, i.e., discriminating between crops and weeds. The system as a whole has been verified, showing that the subsystems are able to work together effectively. A first trial in a greenhouse showed that the robot is able to manage weed control within a row of crops.