基于HSV与形状特征融合的花椒图像识别

光照条件变化会对花椒目标识别率产生影响,关系到机器视觉技术能否有效应用于生产现场的花椒采摘。通过对HSV特性图像识别技术的分析,提出HSV和形状特征融合的花椒识别算法。该算法采用同态滤波方法对光照进行补偿,解决因为光照不均匀而导致的花椒识别率低的问题,最后利用花椒圆度特征,排除树枝及树叶等的干扰,实现花椒的准确识别。利用同态滤波方法对光照进行补偿,对于光照不强或者发生遮挡的花椒图像有较大改善,通过试验得出其平均识别率达到94.0%,比单独采用HSV特性识别时,在顺光,背光和遮阴条件下,识别率分别提高4%,13%和21%,此外在遮阴条件下运行时间缩短14.6%。为遮阴条件下提高花椒识别率提供一种方法。     

Change features of time-series climate variables from 1962 to 2016 in Inner Mongolia,China

Detecting change features of climate variables in arid/semi-arid areas is essential for understanding related climate change patterns and the driving and evolution mechanism between climate and arid/semi-arid ecosystems.This paper takes Inner Mongolia of China,a unique arid/semi-arid ecosystem,as the study area.We first detected trend features of climate variables using the linear trend analysis method and then detected their trend-shift features using the breaks for additive seasonal and trend method based on the time-series of monthly precipitation and monthly mean temperature datasets from 1962 to 2016.We analyzed the different change features of precipitation and temperature on a regional scale and in different ecological zones to discover the spatial heterogeneity of change features.The results showed that Inner Mongolia has become warmer-wetter during the past 54 years.The regional annual mean temperature increased 0.4°C per decade with a change rate of 56.2%.The regional annual precipitation increased 0.07 mm per decade with a slightly change rate of about 1.7%,but the trend was not statistically significant.The warmer trend was contributed by the same positive trend in each season,while the wetter trend was contributed by the negative trend of the summer precipitation and the positive trend of the other three seasons.The regional monthly precipitation series had a trend-shift pattern with a structural breakpoint in the year 1999,while the regional monthly mean temperature series showed an increasing trend without a periodical trend-shift.After the year 2000,the warmer-wetter trend of the climate in Inner Mongolia was accelerated.The late 20th century was a key period,because the acceleration of the wetter trend in some local zones(I and II)and the alleviation of the warmer trend in some local zones(Ⅶ,Ⅷand IX)occurred simultaneously.Moreover,the change features had a strong spatial heterogeneity,the southeastern and southwestern of Inner Mongolia went through a warmer-drier trend compared with the other areas.The spatio-temporal heterogeneity of the climate change features is a necessary background for various types of research,such as regional climate change,the evolution of arid/semi-arid ecosystems,and the interaction mechanisms between climate and arid/semi-arid ecosystems based on earth-system models in Inner Mongolia.     

基于机器视觉的温室番茄裂果检测

该文通过对温室番茄果实进行定位及裂果检测,可为番茄裂果率预估及后续裂果自动筛选提供参考。针对自然光照下采集的各类番茄图像,在相关颜色空间中进行阈值预分割,利用前期支持向量机训练得到的纹理特征分类器对预分割区域进行二次判别;之后在前景区域利用显著性角点分割构造边缘轮廓集,利用基于最小二乘法修正的改进霍夫变换拟合单个番茄目标;最后利用二维Gabor小波算子对拟合的单个番茄区域进行纹理特征提取及裂果判别。文中共采集82幅番茄图像,其中50幅图像作为训练集图像,32幅图像作为验证集,所提算法对测试集中总共128个番茄的果实正确检出率为91.41%,对其中35裂果的正确判别率为97.14%,裂果判别部分平均耗时21 ms。试验结果表明,该方法具有较好的鲁棒性与可靠性,对成熟期番茄裂果率的估计研究及采摘过程中裂果的自动分级筛选具有较好的指导意义,为未来实现温室番茄果实生长状态在线监测提供参考。     

土壤游离氧化铁高光谱特征与定量反演

【目的】建立基于可见-近红外光谱的土壤游离铁精确预测模型,简单、快速、经济地预测土壤游离铁,有助于研究土壤发生和分类。【方法】采集广西壮族自治区的铁铝土、富铁土、淋溶土和雏形土等82个旱地土壤剖面的B层土壤,进行室内土壤化学分析、光谱测定,分析不同光谱变换后的光谱反射率与土壤游离铁含量的相关性。基于特征波段利用偏最小二乘回归(PLSR)和逐步多元线性回归(SMLR)法建立土壤游离铁含量光谱预测模型,通过决定系数(R2)、均方根误差(RMSE)和相对预测偏差(PRD)确定最优模型。【结果】土壤光谱曲线分别在457、800和900 nm波段附近有明显的游离铁吸收和反射峰特征;土壤游离铁含量与原始光谱反射率呈负相关;原始光谱经过微分变换后,游离铁含量与光谱反射率相关性显著提高;基于400～580和760～1 300 nm特征波段和一阶微分光谱变换的SMLR模型预测精度最高,其验证集的R2和RPD分别为0.85和2.62,RMSE为8.41 g·kg~(-1)。【结论】将可见近红外光谱技术应用于土壤游离铁含量高效快速地预测具有良好的可行性。广西旱地土壤光谱反射率与土壤游离铁含量具有高度的相关性,应用逐步多元线性回归方法可以很好地建立土壤游离铁含量反演模型。     

基于边缘检测和区域定位的玉米根茎导航线提取方法

基于玉米根茎图像信息，提出一种基于边缘检测和区域定位的玉米根茎导航线提取方法。首先，利用最大类间方差法自动分割2G-R-B灰度图像，得到二值化图像，结合形态学处理、位置/面积去噪方法提高二值化图像质量，对去噪图像按列累加得到列像素累加曲线；针对传统方法得到的特征点中伪特征点较多的问题，引入高斯滤波器平滑累加曲线，并运用极值法减少玉米根茎伪特征点的干扰，在提取玉米茎秆边缘直线时，提出基于最远茎秆成像宽度的双侧边缘判别思路，通过扫描每条边缘直线的四边形封闭邻域有效剔除伪边缘直线；最后，根据边缘直线二次定位玉米的根茎区域范围，并剔除伪特征点，采用最小二乘线性拟合方法准确提取导航线。试验表明，本文算法处理一幅1280像素×720像素图像耗时约236ms，特征点拟合准确率为92%。与传统方法相比，本文算法精度高、实时性好，在缺苗、杂草较多和株距不标准的情况下仍具有较强的鲁棒性。     

北疆盐碱地空间分布特征分析

为了探究北疆盐碱地空间分布过程中盐碱化程度与经度、纬度及海拔高度之间的关系，基于1985年土壤调查结果，结合2015年Landest 8遥感解译去除人工绿洲结果，采用2 km×2 km网格单元，以盐碱地面积占总面积的比例作为检验区域盐碱化程度的指标，对盐碱地分布情况进行统计和分析。结果表明，北疆地区盐碱化程度受地形和降水的影响明显，特别是受土壤类型的影响更为密切；北疆地区盐碱化程度，由西向东呈增大趋势（P=0838），主要集中分布在E90°～E96°范围内，盐碱土占总面积的比例为7562%；由南向北呈减小趋势（P=-0768），主要集中分布在N40°～N43°范围内，盐碱土占总面积的比例为7782%；由低到高呈减小趋势（P=-0767），主要集中分布在-160～500 m范围内，盐碱土占总面积的比例为4741%。北疆盐碱地空间分布特征的分析对于揭示北疆盐碱化程度的变化规律，促进北疆盐碱土资源的合理开发和维持地区生态安全具有重要的意义。     

基于随机森林的高寒湿地地区土地覆盖遥感分类方法

高寒湿地是青藏高原典型独特的生态系统,是全球气候变化的敏感地带和预警区。利用遥感技术快速、准确地分类提取高寒湿地的土地覆盖信息,对当地生态安全监测和保护具有重要意义。本文以若尔盖湿地国家级自然保护区为研究区,首先,以高分一号(GF-1)遥感影像为数据源,融合光谱特征、水体指数、地形特征、植被指数和纹理信息等26个变量进行随机森林(Random forest,RF)分类实验;然后,根据袋外数据(Out of bag,OOB)的特征变量重要性得分和精度评价结果,选出高寒湿地地区土地覆盖类型的最优分类方案和特征;最后,对特征变量进行降维,并基于相同的变量,采用极大似然法(Maximum likelihood classification,MLC)、支持向量机(Support vector machine,SVM)、人工神经网络(Artificial neural network,ANN)和RF等方法进行分类,比较不同方法的优适性。结果表明:结合GF-1影像光谱、水体、植被、纹理特征和地形信息,使用26个变量的RF模型的分类精度最高,总体精度(Overall accuracy,OA)为90.07%,Kappa系数为0.86;通过RF模型的变量重要性分析可以有效选出重要的特征信息,在降低特征变量维度的同时,还能保证较高的分类精度; 4种分类方法中,RF算法是高寒湿地地区较合适的分类方法,OA比MLC基准方法高17.63个百分点,比SVM和ANN等机器学习算法分别高6.98、6.56个百分点。     

甜玉米‘闽甜208’的生育特性与群体结构分析

多年生育特性观测表明,‘闽甜208’甜玉米春播采青生育期86-90d,秋播采青生育期73-76d,生育动态呈现苗期弱,中间旺长稳定期短,后期早衰快的生育特性,栽培上要注重精细播种,各项田间管理要及时到位。群体结构试验表明,‘闽甜208’叶面积系数（LAI）及叶片净同率（NAR）较高,峰值持续时间较长,大喇叭口至抽雄期LAI对NAR影响最大,通过密植保证一定的总粒数,增加抽雄后群体叶面积及干物质积累,提高成粒率,是高产的关键措施,适宜种植密度为4.95万-5.40万株·hm-2。     

Detect unique molecular structure associated with physiochemical properties in CDC varieties of oat grain with unique nutrient traits [Feed Type vs. Milling Type] in comparison with barley grain using advanced molecular spectroscopy as a non-destructive biological tool

The objective of this study was to determinate grain unique protein inherent molecular structure that are related physiochemical and nutrient profiles in CDC developed oat varieties [CDC Nasser (Feed Type) and CDC Seabiscuit (Milling Type)] grown in cool climate condition in western Canada in comparison with conventional barley variety of CDC Meredith as a control using advanced molecular spectroscopy. Multivariate analyses, including an agglomerative hierarchical cluster analysis (CLA) and principal component analysis (PCA), were performed to identify protein molecular structural differences among the grains. The results revealed that CDC Seabiscuit contained greater (P < 0.05) protein structural Amide I and II than CDC Nasser and CDC Meredith, while the greater (P < 0.005) structural Amide I to II area and height ratios was detected in CDC Meredith. New oat grains had greater (P < 0.05) β-sheet height than barley grains, however, there was no difference in α-helix to β-sheet ratio values among the varieties. In conclusion, CDC Nasser and CDC Meredith had no difference in protein molecular structural features, while CDC Seabiscuit contains different protein structural characteristics as compared to CDC Meredith grain. The molecular structure features are highly associated with physiochemical and nutrient profiles in grains, which indicate that it also affect nutrient utilization and availability.     

地貌发育阶段判识方法研究——以泥河沟流域和枣子沟流域为例

为更加准确判识小流域地貌发育阶段，以黄土高原综合治理小流域——陕西省淳化县泥河沟流域和乾县枣子沟流域为例，分别应用盒分维方法和Horton分维方法进行水系分维值的计算，并利用面积高程积分和地貌信息熵进行验证。结果表明，泥河沟和枣子沟流域水系盒分维值分别为1.886和1.827。根据Strahler曲线积分值和地貌信息熵方法验证结果表明，盒分维方法计算的水系分维值对流域地貌发育阶段的判识更加准确、可靠；这2个流域地貌处于侵蚀发育阶段的壮龄期，其生态敏感性整体上处于较低水平，生态环境抗干扰能力增强并趋于更加稳定的状态。