基于DBN-LSSVR的南美白对虾养殖溶解氧预测

为了提高南美白对虾(Litopenaeus vannamei)养殖溶解氧预测的精度,提出了深度信念网络融合最小二乘支持向量回归机(Deep belief nets-least squares support vector regression,DBN-LSSVR)的南美白对虾养殖溶解氧预测模型.首先,采用深度信念网络(Deep belief nets,DBN)方法,多尺度提取养殖水质时序数据的特征向量;然后,使用提取的养殖水质特征向量训练和优化DBN-LSSVR,构建了基于DBN-LSSVR的对虾养殖水质溶解氧预测模型;最后,以广州市番禺区南美白对虾养殖水质溶解氧实测数据为基础,对预测模型进行了实验验证,并与浅层BP神经网络、标准最小二乘支持向量回归机进行了对比分析.所构建的模型具有较高的预测精度和泛化性能,是一种有效的南美白对虾养殖溶解氧预测方法.     

影响南美白对虾苗种质量的关键因素研究

南美白对虾是我国虾类养殖的主导品种,由于对虾养殖是一项风险高、利润大的系统工程,在众多风险因子中苗种质量又是养殖南美白对虾成功与否的首要限制因素,因此有效选择优质对虾苗种对我国南美白对虾的持续健康养殖有着重要意义。从育苗和放苗环节综述了影响南美白对虾苗种质量的关键因素,以期为对虾养殖生产中进行优质苗种选择和放养提供必要的参考依据。     

南美白对虾养殖勿过热

南美白对虾是咸水淡水均能集约化养殖的优良对虾品种。去年,南美白对虾养殖喜获丰收,不少农户看样跟风,拟将鱼塘、蟹塘改为养殖南美白对虾。然而,南美白对虾养殖存在下列风险,需要引起农户警惕:     

四川南美白对虾养殖现状综述

南美白对虾原产于南美洲太平洋沿岸海域,适应广温广盐,与罗氏沼虾、斑节对虾为世界养殖产量最高的3大虾类。1988年由中国科学院从美国引进,并于1992年突破育苗难关,进行全国各地推广养殖。因其生长速度快,肉质鲜美,营养价值高,迅速被国内消费市场接受。随着市场需求量的提升与海水养殖疾病爆发,淡水养殖南美北对虾成为了近年来的热点~([1])。四川作为对虾消费大市场,养殖潜力巨大,早在10年前已开始进行南美白对虾淡水养殖试验,但受制于地域环境与养殖技术水平等条件,并未取得大面积养殖成功。本文就四川省南美白对虾产业发展状况、存在问题及其对策建议作一概述。     

南美白对虾工厂化循环水养殖技术分析

近年来,随着南美白对虾养殖面积的不断扩大和养殖产量的不断上升,养殖环境的破坏程度也越来越严重,引发各种虾疾病,阻碍了南美白对虾养殖产业可持续发展。因此探索出一条绿色、可控、生态的可持续发展养殖模式在当前对虾养殖产业中具有重要意义~([1])。为此,通过大量试验研究发现,运用水质微生态调控技术的循环水系统,能够有效解决对虾养殖业当前的困境。鉴于此,本文对南美白对虾工厂化循环水养殖技术进行深入研究,以供虾养殖者进行参考。     

南美白对虾池塘高效健康养殖技术研究

南美白对虾因其适应性强、适盐性广、生长快、抗病力强、易管理等优点,近几年养殖南美白对虾在全国沿海地区迅速发展,慈溪市在2015年养殖南美白对虾面积就达2666.67余公顷。但随着高密度大面积的养殖,对虾病害严重威胁南美白对虾的养殖发展,其塘内排放出的大量池水也易引起养殖污染。本课题组在淡水养殖区进行试验,现将南美白对虾池塘高效健康养殖技术总结如下。     

玉环水产养殖协会组织养殖户赴椒江培训

正最近,浙江省玉环县水产养殖协会组织当地部分南美白对虾养殖户,赴台州椒江区参加南美白对虾疫病防控技术培训会。培训会由中国水产科学研究院专家主讲,全市60多位养殖大户参加培训。针对近年来南美白对虾养殖过程中疫病多发的实际情况,专家从疫病发生原因、症状、生物安保等方面深入浅出地进行了讲解,诸如对虾排塘最常见的原因有哪些、对虾偷死常见原因有哪些、对虾不长个的原因有哪些等,以     

南美白对虾养殖气象指数预报方法

根据浙江省宁海县鸡笼山2013—2014年气象资料与温盐特种站资料,利用统计检验结果,结合气象因子与南美白对虾海水养殖直接影响因子(水温、盐度等)的影响关系,根据影响对虾生长发育的关系大小对因子进行权重叠加,得到南美白对虾养殖气象指数。最后利用2015—2016年数据进行验证,对养殖气象指数进行修正改进,最终得到南美白对虾养殖气象指数。     

浦东新区南美白对虾绿色养殖技术探讨

为规范上海市浦东新区南美白对虾的淡水养殖行为,从放养前准备、苗种驯养、养成管理、病害防治、捕捞收获等环节,总结了浦东新区南美白对虾绿色养殖技术,以期进一步推行南美白对虾标准化养殖技术,不断提高养殖户科学合理用药、正确投喂饲料的能力和自觉性,提高南美白对虾的质量,增强市场竞争力,实现优质优价。     

南美白对虾高密度养殖管理技术探析

笔者根据多年的海水养殖南美白对虾经验,分别从池塘基本条件、池塘的清淤、晒塘和消毒、水质选择、苗种选择及放苗、饵料的选择和控制、水质调控及病害防治等7个方面介绍了南美白对虾高密度海水养殖技术,为今后实际养殖南美白对虾提供可行的有效的技术指导依据。     

基于Android平台的水稻病害智能诊断关键技术研究

针对手机客户端上传图片缓慢、损耗数据流量、图像检索和处理能力有限等问题,提出客户端对病害图像进行实时采集和压缩并上传、远程服务端识别并返回结果的方法,阐述了系统实现过程中的一些关键技术(如JPEG图像压缩技术、Open CV图像识别处理技术等),且基于Android平台构建了水稻病害智能诊断平台。结果表明,该系统能及时、准确地鉴别病害种类,提供准确的诊断信息和防治方法,同时还能解决客户端图像处理能力低下、图像上传缓慢等问题,具有较强的实用性。     

罗氏沼虾Toll样受体基因cDNA片段的克隆及序列分析

提取罗氏沼虾(Macrobrachium rosenbergii)上海养殖群体活体鳃、肌肉、肝胰腺、血液和雄性腺5种组织的总RNA;应用RT-PCR技术,从鳃组织中克隆到Toll样受体(TLR)基因部分cDNA序列,并进行生物信息学分析。基因序列分析表明:所得罗氏沼虾TLR基因cDNA序列长1 875 bp,包含1 728 bp的开放阅读框,可编码575个氨基酸残基;该部分TLR是一个跨膜蛋白,存在胞外区LRR、LRR-CT、LRR-NT结构域及关键的胞内区TIR结构域,具备Toll样受体家族的典型结构特征;TIR结构域与凡纳滨对虾(Litopenaeus vannamei)和日本囊对虾(Marsupenaeus japonicus)、中国明对虾(Fenneropenaeus chinensis)、斑节对虾(Penaeus monodon)、中华绒螯蟹(Eriocheir sinensis)、拟穴青蟹(Scylla paramamosian)Toll样受体基因TIR区域的氨基酸序列具有很高的相似性。     

Intelligent diagnosis of northern corn leaf blight with deep learning model

Maize (Zea mays L.), also known as corn, is the third most cultivated crop in the world. Northern corn leaf blight (NCLB) is a globally devastating maize foliar disease caused by Setosphaeria turcica (Luttrell) Leonard and Suggs. Early intelligent diagnosis and warning is an effective and economical strategy to control this disease. Today, deep learning is beginning to play an essential role in agriculture. Notably, deep convolutional neural networks (DCNN) are amongst the most successful machine learning techniques in plant disease detection and diagnosis. Our study aims to identify NCLB in the maize-producing area in Jilin Province based on several DCNN models. We established a database of 985 leaf images of healthy and infected maize and applied data augmentation techniques including image segmentation, image resizing, image cropping, and image transformation, to expand to 30 655 images. Several proven convolutional neural networks, such as AlexNet, GoogleNet, VGG16, and VGG19, were then used to identify diseases. Based on the best performance of the DCNN pre-trained model GoogleNet, some of the recent loss functions developed for deep facial recognition tasks such as ArcFace, CosFace, and A-Softmax were applied to detect NCLB. We found that a pre-trained GoogleNet architecture with the Softmax loss function can achieve an excellent accuracy of 99.94% on NCLB diagnosis. The analysis was implemented in Python with two deep learning frameworks, Pytorch and Keras. The techniques, training, validation, and test results are presented in this paper. Overall, our study explores intelligent identification technology for NCLB and effectively diagnoses NCLB from images of maize.     

亚热带地区马尾松林碳储量的遥感估算——以长汀河田盆地为例

全球气候的变化已使得人类日益关注森林生态系统的碳储量变化.以福建省长汀县河田盆地为例,开展马尾松林碳储量估算模型的研究.通过2010年的野外样地调查获得了马尾松林的实测数据,并将其与同年的ALOS遥感影像对应样地的植被光谱信息进行比较.通过研究5种遥感植被指数与马尾松林碳储量之间的相关关系,从中选取了基于归一化植被指数(NDVI)的研究区最佳马尾松林碳储量反演模型.精度分析表明,该模型平均相对误差为-1.95％,均方根误差为3.01 t/hm2,因此可以有效地用于反演研究区的马尾松林碳储量.利用该模型反演出河田盆地2010年马尾松林的总碳储量为114.58×104 t,碳密度为34.92 t./hm2.     

丝黑穗病菌侵染对转Bt基因玉米抗病性相关指标的影响

为明确外源Bt基因的导入对玉米抗病性的影响,本研究以转Bt基因玉米株系和其受体亲本郑58为材料,比较了接种丝黑穗病菌(Sphacelotheca reiliana(Kühn)Clint.)后转Bt基因玉米防御酶系统、激素水平及次生代谢的变化情况,探讨了转Bt基因玉米对病害抗性的相关生理机制。研究结果表明:接种丝黑穗病菌后,转Bt基因玉米在吐丝期的发病率达到24%,单穗总粒重较对照下降18%,百粒重下降12%,而郑58发病率为15%,产量与对照无显著差异。转Bt基因玉米的超氧化物歧化酶(SOD),过氧化物酶(POD),过氧化氢酶(CAT)等防御酶活性比郑58玉米在抵御病害侵染时表现的较弱,丙二醛(MDA)含量显著增加,膜脂过氧化程度较为严重,这可能是造成转Bt基因玉米产量下降明显的原因之一。生育前期生长素(IAA)、赤霉素(GA)、玉米素核苷(ZR)含量明显下降,而脱落酸(ABA)含量明显升高使得转Bt基因玉米生长受限,同样造成产量的显著下降。此外,在各个生育时期体内的单宁和总酚的含量都低于其受体郑58玉米,生化防御机制受限也可能造成抗病性的降低,进而产量下降。     

Field monitoring support system for the occurrence of Leptocorisa chinensis Dallas (Hemiptera: Alydidae) using synthetic attractants,Field Servers,and image analysis

To realize effective insect counting in pheromone traps set in remote sites, a remote monitoring and image processing system based on a sensor network system of “Field Servers” has been developed, and two practical methods based on image analysis using this system has been proposed. This system has been employed to monitor the occurrence of the rice bug, Leptocorisa chinensis, in rice paddy fields as a means of reducing the burden of manual insect counting work.A Field Server with a high-resolution digital camera was installed near the pheromone trap for close monitoring. The image data and other monitoring data such as temperature were sent via wireless LAN and the Internet every 5 minutes. A remote management system for the Field Server, located about 7.5 km from the experimental field, managed data collection and analyzed the data to provide useful information on insect count. An image analysis algorithm based on a background differencing technique has been developed to support counting L. chinensis by implementing an image-processing module in the remote management system. The image-processing module provides three analysis functions: cropping, subtracting, and binarizing the target image.One method is to filter extraneous image data containing no observed target insects (end-members) on the pheromone trap. In this method, the difference between collected image data and the reference image data was calculated, and the total number of pixels whose value was greater than a threshold value for the difference result (number of white pixels) was used for filtering. This method managed to maintain Sensitivity at 100% during the experiment. Accuracy was observed to be 89.1% on average. Using this method, the time spent looking at extraneous image data without L. chinensis can be reduced by 85%.The other method for reducing labor in counting involves estimating the number of end-members automatically using a partial image area that is cropped to focus on a low-noise area, permitting easy analysis. With this method, the image data was analyzed using the first method, and the entire number of end-members was estimated using the number of white pixels and a pixel value equivalent to one end-member. The results of this method correspond reasonably closely to the results obtained by manual counting. The correlation coefficient for the daily occurrence rate was 0.974 and that for the hourly rate was 0.916.     

The Machine Recognition for Population Feature of Wheat Images Based on BP Neural Network

Recognition and analysis of dynamic information about population images during wheat growth periods can be taken for the base of quantitative diagnosis for wheat growth. A recognition system based on self-learning BP neural network for feature data of wheat population images, such as total green areas and leaves areas was designed in this paper. In addition, some techniques to create favorable conditions for image recognition was discussed, which were as follows: (1) The method of collecting images by a digital camera and assistant equipment under natural conditions in fields. (2) An algorithm of pixei labeling was used to segment image and extract feature. (3)A high pass filter based on Laplacian was used to strengthen image information. The results showed that the ANN system was availability for image recognition of wheat population feature.     

基于梯度图像的玉米种胚褶皱识别

针对玉米品种自动识别中玉米种胚特征的提取问题,提出一种根据玉米种胚图像梯度图,采用基于密度的K-均值聚类算法识别分析玉米种胚褶皱的方法。根据阈值识别出胚部区域,计算其长轴方向的梯度;利用Sobel算子与膨胀算子,去除梯度图像边缘噪声,利用中值滤波去除孤立点噪声;以像素点的R、G、B梯度为特征,组成特征向量空间;采用基于密度的初始中心点优化算法,启发式的找到初始聚类中心;K-均值聚类分析,得到褶皱区域,标记区域并统计褶皱个数。对300颗玉米籽粒进行胚部褶皱识别分析,结果表明:无论是深褶皱、还是浅褶皱的,该方法均能有效识别,与人工观测值的平均吻合率达82.7%。通过本方法获取的褶皱信息体现了玉米种胚纹理特性,为玉米籽粒品种自动识别中特征参数的选取提供了新的思路和方法。     

植物叶片智能分析系统的设计

为避免农作物病害智能诊断过程中人为主观因素的影响,客观准确的表达叶片信息,利用数字图像处理技术和农业植保专家知识相结合,设计了适合于大田作物的植物叶片图像处理与分析系统。该系统主要包括植物叶片几何失真校正模块,几何特征计算模块、颜色识别模块以及病害区域识别模块;以校正后非线性失真现象的叶片图像为基础,实现了叶片几何特征值和颜色值的计算,并提取其病斑区域图像。试验结果表明,该方法满足病害智能诊断要求,具有良好的适应性和实用性。     

几株共同发酵水产下脚料菌株的筛选及鉴定

目前水产下脚料不能得到合理利用,被大量废弃。本研究筛选几株能高效发酵水产下脚料的菌株,分解下脚料产生游离态氮、磷、钾等植物所需营养素。从市售微生物肥料、土壤、腐败鱼体中的微生物分离出44株菌,分别进行单一发酵水产下脚料实验、解磷解钾固氮实验、协同拮抗实验以确定能较好共同发酵水产下脚料的菌株;并通过建立聚类树,结合菌株形态学、生理生化特点,推断菌株的生物属性。最终筛选得到的4株菌分别为,GP2食酸菌、GS4假单胞菌、ZP1黑曲霉和ZP3酵母菌。筛选确定的4株菌,具有较好的发酵水产下脚料产生氮、磷、钾等植物所需营养素的能力,且4株菌作用的发酵液中氮、磷、有机质含量均超过国家微生物肥料标准,这4株菌可以用于水产下脚料发酵。