Automated identification of sugar beet diseases using smartphones

Cercospora leaf spot (CLS) poses a high economic risk to sugar beet production due to its potential to greatly reduce yield and quality. For successful integrated management of CLS, rapid and accurate identification of the disease is essential. Diagnosis on the basis of typical visual symptoms is often compromised by the inability to differentiate CLS symptoms from similar symptoms caused by other foliar pathogens of varying significance, or from abiotic stress. An automated detection and classification of CLS and other leaf diseases, enabling a reliable basis for decisions in disease control, would be an alternative to visual as well as molecular and serological methods. This paper presents an algorithm based on a RGB‐image database captured with smartphone cameras for the identification of sugar beet leaf diseases. This tool combines image acquisition and segmentation on the smartphone and advanced image data processing on a server, based on texture features using colour, intensity and gradient values. The diseases are classified using a support vector machine with radial basis function kernel. The algorithm is suitable for binary‐class and multi‐class classification approaches, i.e. the separation between diseased and non‐diseased, and the differentiation among leaf diseases and non‐infected tissue. The classification accuracy for the differentiation of CLS, ramularia leaf spot, phoma leaf spot, beet rust and bacterial blight was 82%, better than that of sugar beet experts classifying diseases from images. However, the technology has not been tested by practitioners. This tool can be adapted to other crops and their diseases and may contribute to improved decision‐making in integrated disease control.     

基于无人机图像的小麦主要生育时期LAI估算

LAI是表征作物生长状况的重要指标之一.为了快速无损监测小麦LAI,设置了3个密度和4个氮肥水平以形成不同的小麦生长群体,利用无人机搭载的RGB相机获取田间图像,并同步取样测定LAI.在小麦越冬期、返青期、拔节期、开花期和灌浆期选取R、G、B构建的9种颜色特征指数,与实测的LAI进行相关性分析.结果表明,在小麦生长前期...     

乌桕秋叶显色的生理生化与数字图像分析

研究不同颜色乌桕秋叶的色素含量、全氮及碳水化合物含量,定量分析叶片的RGB值,并观察叶绿素、类胡萝卜素及花色素苷在叶片中的分布情况。结果表明：色素含量与单一的RGB（红、绿、兰色）值无直接相关关系,而Chl a与（G-B）/（R-B）呈正相关,Chl b和总叶绿素均与R/（R+G+B）呈显著负相关,花色素苷与R/（G-B）呈显著正相关。绿叶全氮含量最高,而在转色后的叶片中,红色和紫色叶片的氮含量又高于橙色和黄色叶片。全氮与总叶绿素含量呈显著正相关,蔗糖与花色素苷呈正相关,淀粉与叶绿素ａ和总叶绿素均呈极显著正相关,非结构性碳水化合物总量与类胡萝卜素含量呈显著正相关。本研究期望从生理及结构等方面找到影响叶片呈色的相关因素,丰富叶片呈色机制的基础资料,为选育秋季叶色纯正的乌桕优良观赏株系提供理论依据。     

基于颜色和深度信息融合的目标识别方法

传统的机器视觉采用二维RGB图像,难以满足三维视觉检测的要求,深度图像能直接反映物体表面的三维特征,正逐渐受到重视。该文提出的方案将RGB和深度信息相结合,分割出物体所在区域,并利用梯度方向直方图(HOG, histograms of oriented gradients)分别提取RGB图像和深度图像特征信息。在分类算法上,该文采用k最邻近节点算法（k-NN）对特征进行筛选,识别出目标物体。试验结果表明,综合利用深度信息和RGB信息,识别准确率很高,此方案能够对物体和手势进行很好识别。     

Quantitative thermographic analysis method for evaluating the thermal properties of PET irradiated by ultra-violet

A thermograph is used to determine the real-time temperature distribution on the skin temperature wearing clothing treated by different ultra-violet (UV) energy. The thermogram images of body wearing clothing with the 4-channel PET knit fabric irradiated by UV, were compared visually with each other and analyzed quantitatively with image analysis. We analyzed the thermogram in a color image. For image analysis, the Inspector 4.0 (Matrox Electronic System, Ltd.) was used. The surface temperatures, calculated based on the percentage surface area of a given temperature range with an interval of 1 °C, were averaged of five subjects’ surface temperature. From the results of the microclimate temperature, there were not significant differences among the subjects’ surface temperatures wearing different time treated clothes. However, subjective evaluation shows that the clothing treated by UV for 90 min had the lowest thermal sensation and the highest comfort sensation. Based on the image analysis of the thermogram, the calculated thermal sensation of the clothes irradiated by UV for 0 min, 30 min and 90 min, were coincident with the subjective thermal sensation.     

甘蓝型油菜白花性状的主基因+多基因遗传分析

 【目的】对甘蓝型油菜白花性状进行量化观察,研究其数量遗传特性,为育种利用提供理论依据。【方法】利用扫描仪和颜色提取软件对油菜新鲜花瓣进行处理,获得花瓣颜色特征值（CIE RGB值）,选择能反映花瓣颜色差异的B值,应用植物数量性状主基因+多基因混合遗传模型多世代联合分析方法,对甘蓝型油菜杂交组合（HW243×HZ21-1和HW243×中油821）的P1、P2、F1、B1、B2和F2世代群体进行分析。【结果】甘蓝型油菜白花性状表现为一数量性状,其遗传符合两对加性-显性-上位性主基因+加性-显性-上位性多基因遗传模型,以主基因作用为主,多基因的作用相对较小。两对主基因的加性、显性和上位性效应均具有较大的作用。在F2群体中主基因的遗传率为96.94%和95.83%,多基因遗传率为3.93%和2.47%;在B1群体中主基因的遗传率为54.58%和49.57%,多基因遗传率分别为35.64%和46.9%;在B2群体中主基因的遗传率为98.14%和97.67%,多基因遗传率分别为0.98%和2.06%。【结论】甘蓝型油菜白花性状具有数量性状的遗传特性,其遗传符合两对加性-显性-上位性主基因+加性-显性-上位性多基因遗传模型,以主基因效应为主,多基因效应相对较小。主基因的遗传力较高,受环境影响较小。     

基于RGB图像处理预测哈密瓜叶片叶绿素研究

为提高植物叶绿素检测设备的普遍性和实用性,通过研究手机和单片机拍摄的RGB图像与植物叶片叶绿素含量有无拟合关系,以图像处理的方式进行叶绿素预测的相关试验,为将来基于深度学习的植物叶绿素动态无损检测提供试验依据。通过OpenCV对图像提取感兴趣区域(RoI),并进行均值滤波、高斯滤波和中值滤波,对原图和三种滤波后的图像进行三通道颜色特征分离,利用最小二乘法(LS)将颜色特征参数的多种组合与叶绿素实测值进行拟合分析,发现4种图像中均值滤波的拟合效果都普遍较好。在均值滤波中,手机K40拍摄的图像存在(B-G-R)/(B+G)特征组合与叶片叶绿素拟合决定系数为0.912。单片机ESP32_CAM拍摄的图像存在(G-B)B/(R+G)特征组合与叶片叶绿素拟合决定系数为0.778。运用梯度运算将均值滤波的RoI进行迭代处理,发现K40的决定系数略微下降,ESP32_CAM的决定系数出现好转。通过对K40与ESP32_CAM进行预测模型验证,两者都表现为随机森林（RF）回归模型的性能最好,在K40中训练集决定系数为0.953、训练集均方根误差为1.161,预测集决定系数为0.930、预测集均方根误差为1.516,在ESP32_CAM中训练集决定系数为0.794、训练集均方根误差为2.510,预测集决定系数为0.695、预测集均方根误差为2.985。     

VTag: a semi-supervised pipeline for tracking pig activity with a single top-view camera

Precision livestock farming has become an important research focus with the rising demand of meat production in the swine industry. Currently, the farming practice is widely conducted by the technology of computer vision (CV), which automates monitoring pig activity solely based on video recordings. Automation is fulfilled by deriving imagery features that can guide CV systems to recognize animals’ body contours, positions, and behavioral categories. Nevertheless, the performance of the CV systems is sensitive to the quality of imagery features. When the CV system is deployed in a variable environment, its performance may decrease as the features are not generalized enough under different illumination conditions. Moreover, most CV systems are established by supervised learning, in which intensive effort in labeling ground truths for the training process is required. Hence, a semi-supervised pipeline, VTag, is developed in this study. The pipeline focuses on long-term tracking of pig activity without requesting any pre-labeled video but a few human supervisions to build a CV system. The pipeline can be rapidly deployed as only one top-view RGB camera is needed for the tracking task. Additionally, the pipeline was released as a software tool with a friendly graphical interface available to general users. Among the presented datasets, the average tracking error was 17.99 cm. Besides, with the prediction results, the pig moving distance per unit time can be estimated for activity studies. Finally, as the motion is monitored, a heat map showing spatial hot spots visited by the pigs can be useful guidance for farming management. The presented pipeline saves massive laborious work in preparing training dataset. The rapid deployment of the tracking system paves the way for pig behavior monitoring.     

基于RGB模式的一种草地盖度定量快速测定方法研究

盖度是研究植物群落结构的一个重要数量指标,传统目视估算法精度低且受人的影响较大。本研究分析了大量荒漠草地实地照片的RGB颜色模式特征,构造了RGB颜色判别决策树区分植被与非植被像元来计算植被的覆盖度,同时在Matlab 7.0平台上将数码照片的导入、地物判别、盖度计算、划分结果显示、对照等功能模块集成,构建用户界面友好、人机交互便捷软件模块,从而能够准确、高效地计算草地盖度,方便了草原实地调查工作。与针刺法相比,该方法计算得到的植被覆盖度最大偏差绝对值不超过5%,精度在95%以上,解决了常规人工方法中估测草地盖度工作量大、不能准确定量测定的问题。