农业中的物联网智能喷洒技术

过度喷洒除草剂给农业带来了严峻的挑战.农民每年花费250亿美元购买30亿磅的除草剂,但是,这些化学物质中的绝大部分都没有喷洒到杂草上,而是落在土壤或健康作物上,或者被雨水带走.采用传统的喷洒技术,农民因徒劳地喷洒除草剂而蒙受损失.此外,这些化学物质污染了土壤,破坏了环境,同时,杂草本身也会对除草剂产生抗药性.     

基于图像块能量梯度的多聚焦图像融合

针对多聚焦图像,提出一种基于图像分块的融合方法。将源图像分为大小相同数量相等的子块,采用能量梯度算子作为对焦评价函数,计算各个图像子块能量梯度匹配度,设置匹配度阈值分离出源图像中的清晰区域。源图像中的清晰区域直接作为融合图像相应的区域,其它区域的处理中,构造与相应子块能量梯度大小相关的图像序列,以及像素点到各个子块中心距离相关的融合函数,然后用融合函数对图像序列融合。实验结果表明该方法有效性和合理性。     

基于区域特征的胶囊图像分割算法

针对胶囊缺陷检测中存在的图像分割效果不理想的问题, 提出了一种基于区域特征的胶囊图像分割算法. 首先将原图像分割成5个子图像, 然后分别在子图图像中分割提取胶囊. 子图图像首先对图像高亮区域作去高光处理、去除噪声, 然后将图像区域的每一行作为一个子区域, 根据胶囊在图像区域中所在的位置特点, 通过判断子区域中链板域与背景域是否存在边界点以及胶囊与链板上的链齿是否连接来识别不同类型的子区域, 寻找子区域中胶囊与非胶囊区域的边界, 然后去除非胶囊区域. 最终对图像区域逐行扫描处理完成后从图像中提取出胶囊. 实验表明该算法与传统方法相比, 不仅速度较快, 准确性和鲁棒性也得到了改善.     

基于边缘平滑性的图像拼接定位盲取证算法

为在像素级上提高对图像拼接区域的定位精度,分析图像中自然边缘像素点和拼接边缘像素点与其邻域像素点之间存在的平滑过渡关系,提出一种有效的图像拼接盲取证定位算法。对图像中所有的边缘像素点提取平滑度相关性标量值,将其与判决阈值进行比较,得到拼接边缘,定位拼接区域。在标准拼接图像数据集中的实验结果表明,该方法能够有效提高图像拼接区域的定位精度。     

基于人工鱼群微细分解的先验未知像素点修复算法

由于未知像素点先验信息缺失,因此模块匹配和边缘结构信息未知,全息修复困难。传统方法采用子空间特征信息多维搜索方法未能实现对图像纹理的微细结构信息的模板匹配,效果不好。引入人工鱼群算法,提出一种基于人工鱼群微细分解和亮度补偿的先验未知像素点全息修复算法,即采用子空间特征信息多维搜索方法进行先验未知像素点置信度的更新,以保持被修复的图像破损区域的连续性。构建人工鱼群算法的图像微细分解模型,结合边缘特征点亮度补偿策略,来实现对先验未知像素点的图像信息修复改进。实验结果表明,改进的图像修复算法具有良好的视觉效果,修复时间和计算开销较少,提高了稳定性和收敛性,图像修复后的信噪比误差较小,保持在6%以内,因此该算法的性能优越。     

基于彩色图像融合技术的研究

主要讨论了一种彩色图像的像素点趋近算法，它的中心思想是把诗融合区域分解为若干个矩形区域，在每个区域里使像素点逐渐过滤到背景图像中。     

一种基于分布的形状特征描述子

形状特征描述在基于内容的图像检索与识别研究中具有重要地位，文中对图像检索及识别中常用的形状描述进行了介绍。提出一个新的基于区域中心分布的方案来对图像形状特征进行描述，该描述子以待识别对象的区域二值图像的中心点为圆心，将各点到圆心的距离进行规范化处理，统计落入以区域中心为圆心的各环内的点数与总像素点数n的比例，从而形成一个基于中心分布的形状特征向量。经过数学证明该描述方案提取的形状特征具有缩放、旋转和平移不变性。文中通过使用该方案提取的图像特征进行图像识别检索实验，取得了理想的实验结果，说明了它是一个有效的图像区域形状描述子。     

基于平面反射模型的夜间路面检测

路面检测对于自动驾驶系统具有极其重要的作用,其具体的应用方面包括检测辅助、避障、自动导航等。基于视觉的路面检测主要就是对图像中每一个像素点进行分类,区分其是否为路面。到目前为止大部分的路面检测算法是应用于白天。在本文中,我们集中解决夜间的路面检测。我们利用一个近红外摄像头来采集夜间图像。检测时,首先利用平面反射模型来对图像中的路面部分进行拟合,然后,一个基于像素点的分类方法被用来对图像中的每一个像素点进行分类。在实验部分,我们将我们的算法与区域增长的方法进行了比较。实验证明,我们的算法相对区域增长有一定的优势。     

基于等级结构的二值文本图像认证水印算法

针对二值文本图像的结构特点, 定义了度量图像中像素点``可翻转性'的像素扩展差, 在此基础上, 提出了一种基于等级结构的用于图像完整性和所有者认证的脆弱水印算法. 根据等级结构, 将原始图像划分为多等级子块, 然后对各等级子块进行独立的水印生成和嵌入. 根据像素扩展差的大小, 将图像块内的像素点划分为``可翻转'和``不可翻转'像素点. 将混沌调制后的``不可翻转'像素点的值映射为混沌系统的初值, 经过混沌迭代生成水印信号, 然后将水印信号替代``可翻转'的像素点, 完成水印的嵌入. 另外, 在图像的最高级子块中嵌入所有者信息, 实现对所有者的认证. 实验结果表明, 该算法具有良好的视觉透明性, 可对二值图像的均匀区域进行有效保护, 并对图像的内容篡改进行多级检测与定位.     

基于区域清晰度的纺织纤维图像融合

针对多焦面纺织纤维图像,提出一种基于区域清晰度的图像融合方法。用像素点灰度的模值衡量像素点的清晰度。首先通过对多焦面图像搜索像素点最大模值的方法,确定每个最清晰像素点(即灰度的模值最大)所在的图层号,并保存在图层号矩阵中。再针对图像中的噪声干扰,根据局部区域模值的最大值,确定区域阈值进行去噪处理,并修正图层号矩阵。然后根据图层号矩阵,用对应图层像素点的灰度值合成得到多焦面融合图像。最后对融合方法提出改进措施,以进一步提高图像处理的速度。实验表明所提出的多焦面图像融合方法行之有效。     

基于图像处理技术的杂草特征提取方法研究

利用计算机视觉技术将杂草从背景中识别出来进行定位喷洒农药已成为精细农业研究的热点。选取颜色空间OHTA中I＇2分量作为特征量;利用基于遗传算法的自动阈值选取方法对特征分量巧进行阈值分割初步分离杂草与小麦;通过颜色聚类和形态滤波获得准确的杂草区域。实验结果表明：直接在彩色空间进行分割,可提高彩色图像的分割效果,利用该方法获得的杂草平均正确识别率达到90．47％。     

A computer vision approach for weeds identification through Support Vector Machines

This paper outlines an automatic computer vision system for the identification of avena sterilis which is a special weed seed growing in cereal crops. The final goal is to reduce the quantity of herbicide to be sprayed as an important and necessary step for precision agriculture. So, only areas where the presence of weeds is important should be sprayed. The main problems for the identification of this kind of weed are its similar spectral signature with respect the crops and also its irregular distribution in the field. It has been designed a new strategy involving two processes: image segmentation and decision making. The image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based attributes measuring the relations among the crops and weeds. The decision making is based on the Support Vector Machines and determines if a cell must be sprayed. The main findings of this paper are reflected in the combination of the segmentation and the Support Vector Machines decision processes. Another important contribution of this approach is the minimum requirements of the system in terms of memory and computation power if compared with other previous works. The performance of the method is illustrated by comparative analysis against some existing strategies.     

A robot to detect and control broad‐leaved dock (Rumex obtusifolius L.) in grassland

Broad‐leaved dock is a common and troublesome grassland weed with a wide geographic distribution. In conventional farming the weed is normally controlled by using a selective herbicide, but in organic farming manual removal is the best option to control this weed. The objective of our work was to develop a robot that can navigate a pasture, detect broad‐leaved dock, and remove any weeds found. A prototype robot was constructed that navigates by following a predefined path using centimeter‐precision global positioning system (GPS). Broad‐leaved dock is detected using a camera and image processing. Once detected, weeds are destroyed by a cutting device. Tests of aspects of the system showed that path following accuracy is adequate but could be improved through tuning of the controller or adoption of a dynamic vehicle model, that the success rate of weed detection is highest when the grass is short and when the broad‐leaved dock plants are in rosette form, and that 75% of weeds removed did not grow back. An on‐farm field test of the complete system resulted in detection of 124 weeds of 134 encountered (93%), while a weed removal action was performed eight times without a weed being present. Effective weed control is considered to be achieved when the center of the weeder is positioned within 0.1 m of the taproot of the weed—this occurred in 73% of the cases. We conclude that the robot is an effective instrument to detect and control broad‐leaved dock under the conditions encountered on a commercial farm. © 2010 Wiley Periodicals, Inc.     

Harris Hawks Optimizer with Graph Convolutional Network Based Weed Detection in Precision Agriculture

Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop yield. Precision agriculture offers a novel solution utilizing a systematic technique for current agricultural problems like balancing production and environmental concerns. Weed control has become one of the significant problems in the agricultural sector. In traditional weed control, the entire field is treated uniformly by spraying the soil, a single herbicide dose, weed, and crops in the same way. For more precise farming, robots could accomplish targeted weed treatment if they could specifically find the location of the dispensable plant and identify the weed type. This may lessen by large margin utilization of agrochemicals on agricultural fields and favour sustainable agriculture. This study presents a Harris Hawks Optimizer with Graph Convolutional Network based Weed Detection (HHOGCN-WD) technique for Precision Agriculture. The HHOGCN-WD technique mainly focuses on identifying and classifying weeds for precision agriculture. For image pre-processing, the HHOGCN-WD model utilizes a bilateral normal filter (BNF) for noise removal. In addition, coupled convolutional neural network (CCNet) model is utilized to derive a set of feature vectors. To detect and classify weed, the GCN model is utilized with the HHO algorithm as a hyperparameter optimizer to improve the detection performance. The experimental results of the HHOGCN-WD technique are investigated under the benchmark dataset. The results indicate the promising performance of the presented HHOGCN-WD model over other recent approaches, with increased accuracy of 99.13%.     

Classification of soil and vegetation by kernel Fisher and kernel PCA

Precision Agriculture is concerned with all sort of within-field variability, spatially and temporally, that reduces the efficacy of agronomic practices applied in a uniform way all over the field. Because of these sources of heterogeneity, uniform management actions strongly reduce the efficiency of the resource input to the crop (i.e. fertilization, water) or for the agrochemicals use for pest control (i.e. herbicide). In particular, weed plants are one of these sources of variability for the crop, as they occur in patches in the field. Detecting the location, size and internal density of these patches, along with identification of main weed species involved, open the way to a site-specific weed control strategy, where only patches of weeds would receive the appropriate herbicide (type and dose). Herein, the first stage of recognition method of vegetal species, the classification of soil and vegetation, is described and is based upon the kernel Fisher discriminant method (KFDM) and on Kernel Principal Analysis (KPCA).     

Classification of soil and vegetation by kernel fisher and kernel PCA

Precision Agriculture is concerned with all sort of within-field variability, spatially and temporally, that reduces the efficacy of agronomic practices applied in a uniform way all over the field. Because of these sources of heterogeneity, uniform management actions strongly reduce the efficiency of the resource input to the crop (i.e., fertilization, water) or for the agrochemicals used for pest control (i.e. herbicide). In particular, weed plants are one of these sources of variability for the crop, as they occur in patches in the field. Detecting the location, size and internal density of these patches, along with identification of main weed species involved, open the way to a site-specific weed control strategy, where only patches of weeds would receive the appropriate herbicide (type and dose). Herein, the first stage of recognition method of vegetal species, the classification of soil and vegetation, is described and is based upon the kernel Fisher discriminant method (KFDM) and on Kernel Principal Analysis (KPCA).     

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%。     

基于神经网络的杂草图像分割算法

在自动除草系统中优化杂草图像分割算法是降低识别误差的有效途径,为此提出了一种基于神经网络的分割算法。首先由训练样本统计出植被和背景在RGB颜色空间的分布概率,接着通过Bayes理论得出最优分割曲面训练BP神经网络,再通过BP神经网络将各种颜色分为植被和背景两类,并据此分割杂草图像。与其他三种杂草图像分割算法比较,新方法以颜色代替像素点为研究对象并据此构造最优分割曲面从而减小了分割误差并具备较好的泛化能力。     

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.     

A vision-based method for weeds identification through the Bayesian decision theory

One of the objectives of precision agriculture is to minimize the volume of herbicides that are applied to the fields through the use of site-specific weed management systems. This paper outlines an automatic computer vision-based approach for the detection and differential spraying of weeds in corn crops. The method is designed for post-emergence herbicide applications where weeds and corn plants display similar spectral signatures and the weeds appear irregularly distributed within the crop's field. The proposed strategy involves two processes: image segmentation and decision making. Image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based measuring relationships between crop and weeds. The decision making determines the cells to be sprayed based on the computation of a posterior probability under a Bayesian framework. The a priori probability in this framework is computed taking into account the dynamic of the physical system (tractor) where the method is embedded. The main contributions of this paper are: (1) the combination of the image segmentation and decision making processes and (2) the decision making itself which exploits a previous knowledge which is mapped as the a priori probability. The performance of the method is illustrated by comparative analysis against some existing strategies.