Vigilance, visual search and attention in an agricultural task

In a fragile agricultural environment, such as Western Australia (WA), introduced exotic plant species present a serious environmental and economic threat. Skeleton weed, centaurea juncea, a Mediterranean daisy, was accidentally introduced into WA in 1963. It competes with cash crops such as wheat. When observed in the fields, farms are quarantined and mechanised teams search for the infestations in order to destroy them. Since the search process requires attention, visual search and vigilance, the present investigators conducted a number of controlled field trials to identify the importance of these factors in detection of the weed. The paper describes the basic hit rate, vigilance decrement, effect of search party size, effect of target size, and some data on the effect of solar illumination of the target. Several recommendations have been made and incorporated in the search programme and some laboratory studies undertaken to answer questions arising.     

基于深层连接注意力机制的田间杂草识别方法

为了实现田间杂草图像快速、准确识别,提出了一种基于深层连接注意力机制残差网络(DCECA-Resnet50-a)的田间杂草识别模型.以残差网络为基准,改进残差块下采样的位置,同时引入注意力机制和连接注意力机制模块以更好地提取图片中的特征信息,结合迁移学习的策略缓解小样本数据集造成的过拟合现象,提高模型的泛化性并大大减少...     

基于改进 YOLOv4 和图像处理的蔬菜田杂草检测

以蔬菜苗田内幼苗期 7 种常见蔬菜和田间杂草为研究对象,针对田间杂草种类多和分布复杂导致检测方法效率低、精度差和鲁棒性不足等问题,逆向将杂草检测转换为作物检测,提出一种基于优化 YOLOv4和图像处理的蔬菜苗田杂草检测算法。在 YOLOv4 目标检测算法基础上,主干特征提取网络嵌入 SA 模块增强特征提取能力,引入 Transformer 模块构建特征图长距离全局语义信息,改进检测头和损失函数提高检测定位精度。改进模型单幅图像平均识别时间为 0.261 s,平均识别精确率为 97.49%。在相同训练样本以及系统环境设置条件下,将改进方法与主流目标检测算法 Faster RCNN,SSD 和 YOLOv4 算法对比,结果表明改进 YOLOv4模型在蔬菜苗期的多种蔬菜检测具有明显优势。采用改进 YOLOv4 目标检测算法检测作物,作物区域外的植被为杂草,超绿特征结合 OTSU 阈值分割算法获取杂草前景,最后标记杂草前景连通域输出杂草质心坐标和检测框位置,可以较好解决蔬菜苗田杂草检测问题。     

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.     

Bayesian classification and unsupervised learning for isolating weeds in row crops

This paper presents a weed/crop classification method using computer vision and morphological analysis. Subsequent supervised and unsupervised learning methods are applied to extract dominant morphological characteristics of weeds present in corn and soybean fields. The novelty of the presented technique resides in the feature extraction process that is based on spatial localization of vegetation in fields. Features from the weed leaf area distribution are extracted from the cultivation inter-rows, then features from the crop are inferred from the mixture model equation. Those extracted features are then passed to a naive bayesian classifier and a gaussian mixture clustering algorithm to discriminate weed from crop plant. The presented technique correctly classifies an average of 94 % of corn and soybean plants and 85 % of the weed (multiple species) without any prior knowledge on the species present in the field.     

Robust joint stem detection and crop‐weed classification using image sequences for plant‐specific treatment in precision farming

Conventional farming still relies on large quantities of agrochemicals for weed management which have several negative side‐effects on the environment. Autonomous robots offer the potential to reduce the amount of chemicals applied, as robots can monitor and treat each plant in the field individually and thereby circumventing the uniform chemical treatment of the whole field. Such agricultural robots need the ability to identify individual crops and weeds in the field using sensor data and must additionally select effective treatment methods based on the type of weed. For example, certain types of weeds can only be effectively treated mechanically due to their resistance to herbicides, whereas other types can be treated trough selective spraying. In this article, we present a novel system that provides the necessary information for effective plant‐specific treatment. It estimates the stem location for weeds, which enables the robots to perform precise mechanical treatment, and at the same time provides the pixel‐accurate area covered by weeds for treatment through selective spraying. The major challenge in developing such a system is the large variability in the visual appearance that occurs in different fields. Thus, an effective classification system has to robustly handle substantial environmental changes including varying weed pressure, various weed types, different growth stages, changing visual appearance of the plants and the soil. Our approach uses an end‐to‐end trainable fully convolutional network that simultaneously estimates plant stem positions as well as the spatial extent of crop plants and weeds. It jointly learns how to detect the stems and the pixel‐wise semantic segmentation and incorporates spatial information by considering image sequences of local field strips. The jointly learned feature representation for both tasks furthermore exploits the crop arrangement information that is often present in crop fields. This information is considered even if it is only observable from the image sequences and not a single image. Such image sequences, as typically provided by robots navigating over the field along crop rows, enable our approach to robustly estimate the semantic segmentation and stem positions despite the large variations encountered in different fields. We implemented and thoroughly tested our approach on images from multiple farms in different countries. The experiments show that our system generalizes well to previously unseen fields under varying environmental conditions—a key capability to deploy such systems in the real world. Compared to state‐of‐the‐art approaches, our approach generalizes well to unseen fields and not only substantially improves the stem detection accuracy, that is, distinguishing crop and weed stems, but also improves the semantic segmentation performance.     

基于颜色特征的杂草图像分割技术研究

为了解决目前杂草识别中受光照影响大、环境适应性差等问题,提出了基于颜色特征的分割算法。此算法在统计分析杂草和土壤背景各颜色因子的基础上,得到适于杂草图像分割的颜色分量,实现了复杂场景、光照条件下杂草区和背景区的分割。实验结果表明:R-G,2G-R-B,Hmean,Smean,Hmean Smean颜色特征对于杂草区和背景区的分割能够取得很好的效果,可广泛应用于田间杂草识别、树种识别、人脸识别等受光照变化影响较大的领域。     

遥感技术在毒草识别中的研究进展

毒草的滋生蔓延严重破坏草地生境,制约草地畜牧业的发展。遥感技术作为牧场管理的一种重要的技术手段,其传感器自身的空间分辨率和光谱分辨率的高低是决定毒草识别成功与否的关键。于毒草独特的物候特征出现时获取影像数据能帮助提高分类识别的精度。回顾了3种遥感技术在毒草识别中的研究进展。航空摄影成本高、数据处理复杂,难于得到推广 ;多光谱卫星遥感大多空间分辨率低,仅在识别大面积滋生、密度较大的毒草方面展现出了一定的潜力 ;高光谱遥感的出现改善了对植被分类识别的精度,是未来毒草识别的主要依据。由于高光谱数据的冗余性和复杂性,数据处理技术和分类方法的选择也是影响毒草识别精度的重要因素。     

A Rotary-wing Unmanned Air Vehicle for Aquatic Weed Surveillance and Management

This paper addresses the novel application of an autonomous rotary-wing unmanned air vehicle (RUAV) as a cost-effective tool for the surveillance and management of aquatic weeds. A conservative estimate of the annual loss of agricultural revenue to the Australian economy due to weeds is in the order of A$4 billion, hence the reason why weed control is of national significance. The presented system locates and identifies weeds in inaccessible locations. The RUAV is equipped with low-cost sensor suites and various weed detection algorithms. In order to provide the weed control operators with the capability of autonomous or remote control spraying and treatment of the aquatic weeds the RUAV is also fitted with a spray mechanism. The system has been demonstrated over inaccessible weed infested aquatic habitats.     

Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop

This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed–crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The inferred categorical variable values for the weed–crop competitiveness along with three other categorical variables extracted from estimated maps for the weed seed production and weed coverage are then used as input for a second Bayesian network classifier to infer categorical variables values for the risk of infestation. Weed biomass and yield loss data samples are used to learn the probability relationship among the nodes of the first and second Bayesian classifiers in a supervised fashion, respectively. For comparison purposes, two types of Bayesian network structures are considered, namely an expert-based Bayesian classifier and a naïve Bayes classifier. The inference system focused on the knowledge interpretation by translating a Bayesian classifier into a set of classification rules. The results obtained for the risk inference in a corn-crop field are presented and discussed.     

Early identification of ineffective cooperative learning teams

Cooperative learning has many pedagogical benefits. However, if the cooperative learning teams become ineffective, these benefits are lost. Accordingly, this study developed a computer‐aided assessment method for identifying ineffective teams at their early stage of dysfunction by using the Mahalanobis distance metric to examine the difference between the sequential test scores of the unknown team and the test scores of a reference group of functioning teams. The effectiveness of the proposed method was verified by conducting field experiments over an 18‐week engineering course in Taiwan. Forty‐eight students were randomly assigned to cooperative learning teams. The students' learning performance was evaluated by means of unit tests and homework tests. The functioning of the cooperative teams was examined at seven different points during the course of the study. The ineffective teams were identified with quantified type I errors. It was found that some teams failed persistently. Such teams require some form of external intervention to remedy the group dynamics. The results also showed that teams can become ineffective at any stage of the cooperative learning process. Thus, continuous monitoring is required to ensure that appropriate remedial actions are taken in a timely manner.     

基于可信语义深度学习的文本文献搜索方法

针对传统文本搜索返回结果不准确、不满意的问题，提出一种基于可信语义深度学习的文本搜索方法。首先为了充分挖掘文本的可信语义，通过文本中的信任事实，以及人机交互标注的方式计算文本的可信度。利用网络爬虫抓取大量文本文献学习训练数据，并且构建深度学习神经网络模型，以文本的语义矩阵为输入，以文本的可信度为输出，通过有监督学习，训练出评估文本可信度的深度学习神经网络模型。最后应用该神经网络模型实现文本文献的搜索。通过“中国政治党建”领域的搜索实验表明：该方法在平均可信度方面优于传统搜索方法。     

Hierarchical visibility for guaranteed search in large-scale outdoor terrain

Searching for moving targets in large environments is a challenging task that is relevant in several problem domains, such as capturing an invader in a camp, guarding security facilities, and searching for victims in large-scale search and rescue scenarios. The guaranteed search problem is to coordinate the search of a team of agents to guarantee the discovery of all targets. In this paper we present a self-contained solution to this problem in 2.5D real-world domains represented by digital elevation models (DEMs). We introduce hierarchical sampling on DEMs for selecting heuristically the close to minimal set of locations from which the entire surface of the DEM can be guarded. Locations are utilized to form a search graph on which search strategies for mobile agents are computed. For these strategies schedules are derived which include agent paths that are directly executable in the terrain. Presented experimental results demonstrate the performance of the method. The practical feasibility of our approach has been validated during a field experiment at the Gascola robot training site where teams of humans equipped with iPads successfully searched for adversarial and omniscient evaders. The field demonstration is the largest-scale implementation of a guaranteed search algorithm to date.     

A LABORATORY APPROACH TO INTERPERSONAL ASPECTS OE TEAM PEREORJ1ANCE

Thin paper brings together and re-appraises a number of studios, mostly already published elsewhere, from a laboratory research programme to deal in an experimental fashion with certain problems of team performance. An attempt has been made to bo sufficiently comprehensive to anticipate a variety of team performance problems observed in the Air i'oreo and other work-group settings. Rosoareh to date in this programme has dealt primarily with ream training, although ono may also noto implications in this work for the probloms of tho distribution of displays and controls among team members,

The conceptual approach adopted is traceable to contemporary stimulus-responso thoorios in psychology. Basic concepts and methods are briefly describod followed by an analysis of stimulus-response arrangements in dyads. Feedback (reinforcing) stimuli in social settings are first given special attention sinco they seom central to almost any training problem.

Experiments are summarized which deal with parameters relevant to socially affected feedback. A mimbor of task parameters are also givon experimental consideration.

Finally, initial studies of discrimination learning within a team context, and tho effects of individuals' past histories on a discrimination task, as those determine toam outputs, are also described.     

面向深度学习的公平性研究综述

深度学习是机器学习研究中的一个重要领域,它具有强大的特征提取能力,且在许多应用中表现出先进的性能,因此在工业界中被广泛应用然而,由于训练数据标注和模型设计存在偏见,现有的研究表明深度学习在某些应用中可能会强化人类的偏见和歧视,导致决策过程中的不公平现象产生,从而对个人和社会产生潜在的负面影响.为提高深度学习的应用可靠性...     

Process and implementation of participatory ergonomic interventions: a systematic review

Participatory ergonomic (PE) interventions may vary in implementation. A systematic review was done to determine the evidence regarding context, barriers and facilitators to the implementation of participatory ergonomic interventions in workplaces. In total, 17 electronic databases were searched. Data on PE process and implementation were extracted from documents meeting content and quality criteria and synthesised. The search yielded 2151 references. Of these, 190 documents were relevant and 52 met content and quality criteria. Different ergonomic teams were described in the documents as were the type, duration and content of ergonomic training. PE interventions tended to focus on physical and work process changes and report positive impacts. Resources, programme support, ergonomic training, organisational training and communication were the most often noted facilitators or barriers. Successful PE interventions require the right people to be involved, appropriate ergonomic training and clear responsibilities. Addressing key facilitators and barriers such as programme support, resources, and communication is paramount.

Statement of Relevance: A recent systematic review has suggested that PE has some effect on reducing symptoms, lost days of work and claims. Systematic reviews of effectiveness provide practitioners with the desire to implement but do not provide clear information about how. This article reviews the literature on process and implementation of PE.     

A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method

This paper presents a system for weed mapping, using imagery provided by unmanned aerial vehicles (UAVs). Weed control in precision agriculture is based on the design of site-specific control treatments according to weed coverage. A key component is precise and timely weed maps, and one of the crucial steps is weed monitoring, by ground sampling or remote detection. Traditional remote platforms, such as piloted planes and satellites, are not suitable for early weed mapping, given their low spatial and temporal resolutions. Nonetheless, the ultra-high spatial resolution provided by UAVs can be an efficient alternative. The proposed method for weed mapping partitions the image and complements the spectral information with other sources of information. Apart from the well-known vegetation indexes, which are commonly used in precision agriculture, a method for crop row detection is proposed. Given that crops are always organised in rows, this kind of information simplifies the separation between weeds and crops. Finally, the system incorporates classification techniques for the characterisation of pixels as crop, soil and weed. Different machine learning paradigms are compared to identify the best performing strategies, including unsupervised, semi-supervised and supervised techniques. The experiments study the effect of the flight altitude and the sensor used. Our results show that an excellent performance is obtained using very few labelled data complemented with unlabelled data (semi-supervised approach), which motivates the use of weed maps to design site-specific weed control strategies just when farmers implement the early post-emergence weed control.     

基于卷积神经网络的中文医疗弱监督关系抽取

随着医疗领域受到越来越多的关注,自然语言处理的理论和应用逐渐拓展到该领域,其中信息抽取技术在该领域的应用成为研究热点。针对信息抽取技术在医疗领域实体关系抽取中的应用,提出一种基于卷积神经网络的弱监督关系抽取方法。该方法通过添加人工规则使训练语料带有实体关系标签,然后将该弱关系训练语料转换为向量特征矩阵,并输入到卷积神经网络进行分类模型训练,最终实现实体关系抽取。实验结果表明,该方法比常规机器学习方法更加准确高效。     

Multi-spectral vision system for weed detection

The proposed online system distinguishes crop from weeds based on multi-spectal reflectance gathered with an imaging spectrograph. Under field conditions, up to 86% of the vegetation samples (80% of crop, 91% of weed) were recognized herbicide reductions of up to 90%.     

最小二乘迁移生成对抗网络

现有的生成对抗网络（Generative Adversarial Networks，GAN）损失函数已经被成功地应用在迁移学习方法中。然而，发现这种损失函数在学习过程中可能会出现梯度消失的问题。为了克服该问题，提出了一种学习领域不变特征的新方法，即最小二乘迁移生成对抗网络（Least Squares Transfer Generative Adversarial Networks，LSTGAN）。LSTGAN采用最小二乘生成对抗网络（Least Squares Generative Adversarial Networks，LSGAN）损失函数，通过单领域判别的训练方式来减少领域分布之间的差异。通过研究表明，所提方法与其他有竞争力的算法相比较具有一定的优越性。