基于信息抽取的食品安全事件自动问答系统方法研究

针对从海量食品安全事件新闻报道中很难抽取出所需答案的问题，以食品安全事件语料库为研究对象，提出了一种基于信息抽取技术的自动问答系统。首先，利用深度学习模型TextCNN对用户输入的问题进行分类，得到其所属类型。其次，对于输入问题，借助Lucene搜索引擎找到其最佳匹配文档。再次，根据输入问题的类型，从食品安全事件数据库（采用信息抽取技术自动提取的一个结构化数据库）中筛选出该文档所包含的答案候选句集合。最后，利用深度学习模型Bi LSTM及基于答案候选句上下文的特征提取方法构建一个答案抽取模型，该模型能从给定的答案候选句集合中提取出最终答案。为检查基于食品安全事件数据库的答案候选句筛选方式及基于答案候选句上下文的特征提取方式对整个自动问答系统性能的影响，进行了多种比较实验，结果表明含有基于食品安全事件数据库的答案候选句筛选方式和基于答案候选句上下文的特征提取方式的问答系统表现最佳，其回答准确率达到44%。这相比于传统的问答系统，具有明显的优势。     

全膜双垄沟双幅覆膜覆土联合作业机设计与试验

为进一步提升全膜双垄沟覆膜种床机械化耕整作业效果与作业效率,基于全膜双垄沟种床双幅覆膜覆土农艺技术栽培模式,设计全膜双垄沟双幅覆膜覆土联合作业机,实现了全膜双垄沟种床构建过程中的旋耕、起垄、施肥、双幅覆膜覆土及镇压的一体化作业。在设定联合作业机传动系统的前提下,对样机关键部件进行设计与选型,确定其旋耕刀组、排肥装置、提土-种床覆土装置的工作参数与功耗,同时解析了刮板式提土装置、水平双向螺旋输土装置作业过程,得到满足双幅覆膜种床各部位覆土的必要条件。应用离散单元法对提土-种床覆土装置"提土-输土-覆土"动态作业过程进行数值模拟,分析了各工作环节输土、覆土特性,对影响覆土作业效果的相关因素进行了探讨。田间试验结果表明,当全膜双垄沟双幅覆膜覆土联合作业机前进速度为1. 10 m/s时,采光面地膜机械破损程度为39. 8 mm/m~2、种床膜边覆土宽度合格率为94. 5%、大垄垄体中心覆土宽度合格率为91. 6%、种床覆土厚度合格率为97. 5%、种床起垄高度合格率为93. 2%,试验指标均符合国家与行业标准要求,试验结果满足设计和实际作业要求,田间试验工况与仿真结果基本一致。     

基于农林植物表型的智能喷雾机械研究进展

传统喷雾机械通常仅关注无生命的机械作业性能，而忽略了有生命的植物因素，造成喷雾机械智能化水平不高。文中在分析农林植物表型对喷雾机械设计方法和喷雾性能影响的基础上，系统综述国内外开展的智能喷雾机械研究，提出基于植物表型的智能喷雾机械设计方法研究展望，探讨分析基于农林植物表型特征参数的新型、高水平智能化喷雾机械，并指出农林植物表型技术的发展必将推动绿色智能喷雾机械的创新。     

Projecting the suitability of global and local habitats for myrtle rust (Austropuccinia psidii) using model consensus

Myrtle rust (caused by Austropuccinia psidii) affects more than 500 known host species in the Myrtaceae family. Three different modelling approaches (CLIMEX, MaxEnt and Multi-Model Framework) were used to project the habitat suitability for myrtle rust at both global and local scales. Current data on the global occurrence of myrtle rust were collected from online literature and expert solicitation. Long-term averages of climate data (1960–1990) were sourced from WorldClim and CliMond websites. Recent reports of myrtle rust in New Zealand were used for validation of model outputs but not in model training and testing. The model outputs were combined into a consensus model to identify localities projected to be suitable for myrtle rust according to two or three models (hotspots). In addition to the locations where the pathogen is currently present, all models successfully projected independent occurrence data in New Zealand suitable for establishment of the pathogen. Climate suitability for the pathogen was primarily related to temperature followed by rainfall in MaxEnt and the CLIMEX model. The results confirmed the optimum temperature range of this pathogen in the literature (15–25 °C). Additional analysis of the precipitation variables indicated that excessive rain (more than 2000 mm in warmest quarter of the year) combined with high temperatures (>30 °C) constrain pathogen establishment. The results of the current study can be useful for countries such as New Zealand, China, South Africa and Singapore where the pathogen has not fully spread or established.     

Is it possible to differentiate between environmental and fishery effects on abundance‐biomass variation? A case study of blackspot seabream (Pagellus bogaraveo) in the Strait of Gibraltar

We assessed the potential for simulation and modelling of the blackspot seabream (Pagellus bogaraveo) population in the Strait of Gibraltar to discriminate the environmental effects of fishery impacts. A discrete biomass–abundance dynamic model was implemented to obtain a simulated monthly time series of blackspot seabream biomass. On this simulated time series, autoregressive integrated moving average (ARIMA) models were fitted. The best ARIMA fit provided a significant correlation of 0.76 and persistence index higher than 0.85. The proportion of variance non‐explained by the ARIMA models was correlated with a time series of sea surface temperature (SST) and North Atlantic Oscillation (NAO). The analysis of global, annual and winter correlation between the proportion of variance not explained by the ARIMA models and environmental variables showed that significant associations were not detected over the full time series. Our analysis therefore suggests that overexploitation is the main factor responsible for the commercial depletion of blackspot seabream in the Strait of Gibraltar.     

Harvesting machine for kelp culture in floating raft

Kelp is not only a kind of food of sea vegetable for human, but also widely used as raw materials in processing food, chemical engineering, pharmaceutical and cosmetic industries. In China kelp is generally planted on floating raft by using horizontal kelp rope cultivation method and often harvested manually. Harvesting kelp by hand is a difficult process with low production efficiency and high labor intensity. In this paper, a harvesting machine was developed for floating raft culture of kelp. It consists of mainly two locomotion modules, a collection/transmission module and a power module. The structure and working mechanism of this machine was presented and analyzed in detail. The tests of experiments were conducted as well with the developed machine. The manual harvest efficiency is 16.7%; however the mechanized harvest efficiency average value is 35.8%. The harvest time could be reduced by half if the company takes the same people to work. It is proven that the design of the machine is feasible.     

基于卷积神经网络的小麦产量预估方法

小麦产量是评估农业生产力的重要指标之一,针对小麦产量人工预估困难,提出将卷积神经网络运用于小麦产量预估,为农业生产力的预估提供参考,指导农业生产管理决策。利用无人机分别在河南省新乡、漯河两地进行图片采集,并以之构建麦穗数据集,分为正样本(麦穗)和负样本(叶子和背景)。针对小麦常规的生理形态和生长环境,设计卷积神经网络识别模型,以图像金字塔构建多尺度滑动窗口,以非极大值抑制(NMS)去除重叠率较高的目标框,实现对单位面积内麦穗的计数,并利用随机采样的方式对大田麦穗进行单位面积图像采样,以采样图像中麦穗数量的平均值作为产量预估基准,进一步实现麦穗产量预估。随机抽取100幅不同小麦图片进行测试,与人工计数结果进行对比,准确率达到97.30%,漏检率为0.34%,误检率为2.36%,误差率为2.70%。试验结果表明,此方法能够克服环境中的多种噪声干扰,能够在不同光照条件下对麦穗进行计数和产量预估。     

纵横刀组协同式马铃薯种薯切块装置设计与试验

针对马铃薯种薯切块机械化程度低的问题,设计了一种纵横刀组协同式马铃薯种薯切块装置,对其关键部件进行设计,通过对马铃薯种薯切割过程的力学分析、运动学分析和能量学分析,建立了切种能量的数学模型,确定了影响马铃薯切种效果的主要因素。以切种效率、切种合格率为评价指标,以圆盘刀半径、输送辊与圆盘刀垂直中心距、圆盘刀轴转速和夹持辊轴转速为试验因素,进行了四因素四水平正交试验。对正交试验结果进行方差和极差分析,结果表明:当圆盘刀半径为180 mm、输送辊与圆盘刀垂直中心距为190 mm、圆盘刀轴转速为115 r/min、夹持辊轴转速为56 r/min时,切种效率为74.5 kg/min,切种合格率为98.8%,满足马铃薯切种作业要求。     

基于生活史特征的数据有限条件下渔业资源评估方法比较

渔业资源评估是开展渔业资源管理,维系渔业可持续发展的基础工作。传统的渔业资源评估方法需要统计产量、资源丰度指数甚至年龄结构等大量数据,由于调查经费和数据的缺乏,全球仅1%的鱼种进行过系统性的资源评估。近年来,在数据有限(data-limited)条件下如何开展资源评估已日益成为学术界的关注热点。本文将基于生活史特征的评估方法分为仅需要生活史参数,需要产量数据和生活史参数,需要产量数据、生活史参数及体长或年龄数据等3大类,分别从方法、数据要求、输出结果及局限性进行了系统回顾分析,提供了关于生活史特征参数的常见估算方法,并就其中两种模型对北大西洋大青鲨(Prionace glauca)的可持续渔获量进行了初步评估与比较。最后,对数据缺乏模型的使用及模型在中国近海渔业资源评估中的运用提出了建议。     

基于显著性增强和迁移学习的鱼类识别研究

针对水下鱼类无法快速准确识别的难点,提出一种具有图像主体自动增强功能的鱼类迁移学习方法。该方法将鱼类RGB图像转换至Lab颜色空间后,利用中央周边算子计算得到整个输入图像的显著性值,进而提供鱼类目标的潜在区域,并结合GrabCut算法获取鱼类分割图像,最终将融合分割图的原始图像送入优化后的残差网络中进行训练。通过对23种鱼类进行识别试验,结果显示,固定ImageNet数据集上ResNet-50预训练模型的conv1层和conv2层参数,微调高层参数的方法能够取得最好的识别效果,且在公开的Fish4Knowledge数据集上,该模型取得了最高的识别准确率,平均识别精度达到99.63%。与其他卷积神经网络方法的对比结果显示,本方法在Fish4Knowledge和Fish30Image数据集上的识别精度和时间性能均具有较大优势,其中识别准确率至少提升4.98%。多个数据集上的试验验证了模型的有效性。