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A Review on Plant Disease Detection Using Hyperspectral Imaging 利用高光谱成像检测植物病害综述
Pub Date : 2023-11-29 DOI: 10.1109/TAFE.2023.3329849
Rakiba Rayhana;Zhenyu Ma;Zheng Liu;Gaozhi Xiao;Yuefeng Ruan;Jatinder S. Sangha
Agriculture production is one of the fundamental contributors to a nation's economic development. Every year, plant diseases result in significant crop losses that threaten the global food supply chain. Early estimation of plant diseases could play an essential role in safeguarding crops and fostering economic growth. Recently, hyperspectral imaging techniques have emerged as powerful tools for early disease detection, as they have demonstrated capabilities to detect plant diseases from tissue to canopy levels. This article provides an extensive overview of the principles, types, and operating platforms of hyperspectral image sensors. Furthermore, this article delves into the specifics of these sensors' application in plant disease detection, including disease identification, classification, severity analysis, and understanding genetic resistance. In addition, this article addresses the current challenges in the field and suggests potential solutions to mitigate these pressing issues. Finally, this article outlines the promising future trends and directions of hyperspectral imaging in plant disease detection and analysis. With continuous improvement and application, these imaging techniques have great potential to revolutionize plant disease management, thereby enhancing agricultural productivity and ensuring food security.
农业生产是促进国家经济发展的基本因素之一。每年,植物病害都会给农作物造成重大损失,威胁全球粮食供应链。对植物病害的早期估计可在保护作物和促进经济增长方面发挥至关重要的作用。最近,高光谱成像技术已成为早期病害检测的有力工具,因为它们已证明有能力检测从组织到冠层的植物病害。本文广泛概述了高光谱图像传感器的原理、类型和操作平台。此外,本文还深入探讨了这些传感器在植物病害检测中的具体应用,包括病害识别、分类、严重程度分析和了解遗传抗性。此外,本文还探讨了该领域当前面临的挑战,并提出了缓解这些紧迫问题的潜在解决方案。最后,本文概述了高光谱成像在植物病害检测和分析中的未来发展趋势和方向。随着这些成像技术的不断改进和应用,它们有望彻底改变植物病害管理,从而提高农业生产力,确保粮食安全。
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引用次数: 0
Bioimpedance Measurement of Avocado Fruit Using Magnetic Induction Spectroscopy 利用磁感应光谱学测量鳄梨果实的生物阻抗
Pub Date : 2023-08-28 DOI: 10.1109/TAFE.2023.3303177
Michael D. O'Toole;Marcin Glowacz;Anthony J. Peyton
Avocado fruit is a popular, nutritious, and commercially valuable product that, with a short window of ripeness and heterogeneous maturity, presents particular challenges when bringing to market. There is significant value in being able to measure avocado fruit ripeness and maturity, especially nondestructively, with the prospect of improvements in consignment management, food loss, and consumer satisfaction. In this article, we explore the bioimpedance spectra of avocado fruit. Bioimpedance has been found to correlate with ripeness in avocado fruit over a frequency range termed the $beta$-dispersion where cell polarization effects are significant. Our contribution is to use magnetic induction spectroscopy to measure conductivity across this range, an entirely noncontact method that uses eddy currents induced in the fruit flesh by magnetic fields rather than penetrative or surface electrodes as in previous work. We were able to measure a clear $beta$-dispersion curve, finding fruit conductivity rising from $sim$0.6 mS/cm at 100 kHz to $sim$4 mS/cm at 10 MHz. This agrees with the literature at higher and lower frequencies, and completes a gap in the spectra not previously reported. Further, we find evidence of changes to the conductivity spectra as the fruit ages and ripens, with the spectra broadly flattening according to a set of identified trends. This indicates a relation between bioimpedance spectra and ripeness, although high intersample variability precludes the spectra as a direct estimation technique at this stage.
牛油果是一种广受欢迎、营养丰富且具有商业价值的产品,由于成熟期短且成熟度参差不齐,在推向市场时面临着特殊的挑战。能够测量牛油果果实的成熟度和成熟度具有重要价值,尤其是非破坏性测量,有望改善托运管理、食品损耗和消费者满意度。本文将探讨牛油果果实的生物阻抗光谱。生物阻抗与牛油果果实的成熟度相关,其频率范围被称为 $beta$ 分散,在该频率范围内细胞极化效应显著。我们的贡献是使用磁感应光谱法测量这一范围内的电导率,这是一种完全非接触式的方法,使用磁场在果肉中引起的涡流,而不是以前工作中的穿透电极或表面电极。我们能够测量一条清晰的 $beta$ 分散曲线,发现水果的电导率从 100 kHz 时的 $sim$0.6 mS/cm 上升到 10 MHz 时的 $sim$4 mS/cm。这与文献中更高和更低频率的结果一致,并填补了以前未报道过的光谱空白。此外,我们发现有证据表明,随着果实的老化和成熟,电导率光谱也会发生变化,光谱会根据一系列已确定的趋势大致变平。这表明生物阻抗光谱与成熟度之间存在关系,尽管样本间的高变异性排除了在现阶段将光谱作为直接估算技术的可能性。
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引用次数: 0
LuXSensing Beacon: Batteryless IoT Sensor, Design Methodology, and Field Test for Sustainable Greenhouse Monitoring LuXSensing 信标:用于可持续温室监测的无电池物联网传感器、设计方法和现场测试
Pub Date : 2023-08-02 DOI: 10.1109/TAFE.2023.3295383
Kang Eun Jeon;Tsz Ngai Lin;James She;Simon Wong;Rajesh Govindan;Tareq Al-Ansari;Bo Wang
Greenhouse farming is a trending practice to secure food production in desert environments. Such a practice often requires sensing systems to monitor the greenhouse microclimate. However, traditional monitoring systems are often limited by their feature size, energy consumption, and maintenance cost. To address these issues, this article introduces a luXSensing beacon—an energy harvesting sensing device empowered with Bluetooth communication technology to perform continuous environmental sensing. To enable long-lasting or even batteryless operation of the sensing device, we propose a novel and generic design methodology to suggest minimum energy harvesting hardware requirements, namely the photovoltaic panel's area and supercapacitor's size for energy storage. In addition, a lifetime model is also proposed to calculate the extended lifetime of a hybrid energy harvesting device if it is equipped with a backup battery. Based on the proposed methodology, a prototype system is developed, deployed, and tested in a desert greenhouse. The luXSensing beacon demonstrated its capability of monitoring air temperature and illuminance continuously in a 24/7 manner. The comparative compactness and low-energy consumption of the system are advantageous not only to its deployment in greenhouses but also to the reduction of energy budget and the maintenance cost of greenhouse farming.
温室种植是确保沙漠环境中粮食生产的一种趋势性做法。这种做法通常需要传感系统来监测温室的微气候。然而,传统的监测系统往往受限于其功能尺寸、能耗和维护成本。为了解决这些问题,本文介绍了一种 luXSensing 信号信标--一种采用蓝牙通信技术的能量收集传感设备,可进行连续的环境传感。为使传感设备能够长效甚至无电池运行,我们提出了一种新颖的通用设计方法,以建议最低的能量采集硬件要求,即用于储能的光电板面积和超级电容器尺寸。此外,我们还提出了一个寿命模型,用于计算混合能量收集装置在配备备用电池的情况下可延长的寿命。根据提出的方法,开发了一个原型系统,并在沙漠温室中进行了部署和测试。luXSensing 信标展示了其全天候连续监测空气温度和光照度的能力。该系统结构紧凑、能耗低,不仅有利于在温室中部署,还能降低温室种植的能源预算和维护成本。
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引用次数: 0
A Scheme for Pest-Dense Area Localization With Solar Insecticidal Lamps Internet of Things Under Asymmetric Links 非对称链接下的太阳能杀虫灯物联网害虫密集区定位方案
Pub Date : 2023-07-07 DOI: 10.1109/TAFE.2023.3286699
Yuan Li;Bangsong Du;Lin Luo;Yusheng Luo;Xing Yang;Ye Liu;Lei Shu
The combination of solar insecticidal lamps (SILs) and wireless sensor networks has spawned a green and efficient solution for agricultural pest control, called solar insecticidal lamps Internet of Things (SIL-IoTs). In realistic large-scale SIL-IoTs deployment scenarios, the integrated pest information collected across the network enables effective localization of pest-dense areas. However, the problem of asymmetric links caused by various factors, such as irregular wireless communication range and discharge interference of nodes, is the main obstacle to the deployment of SIL-IoTs. Motivated by this problem, the pest-dense area localization strategy (PALS) based on asymmetric links is proposed. First, the asymmetric nodes in the network are judged by analyzing the one-hop and two-hop information of SIL nodes. Subsequently, the Gabriel graph or relative neighborhood graph planarization algorithm is used to planarize the symmetric links in the network. Then, the quick rejection method and straddle experiment are used to remove the cross sections after planarization. Finally, by counting the number of SIL node discharges and facilitating the left-hand rule, PALS successfully reduces the difference between the calculated and actual pest-dense areas. The experiments showed that PALS achieves an average improvement of 15% and a maximum improvement of up to 42.2% across the four experimental settings, indicating its higher accuracy and robustness compared with the state-of-the-art algorithms.
太阳能杀虫灯(SIL)与无线传感器网络的结合为农业害虫控制提供了一种绿色高效的解决方案,即太阳能杀虫灯物联网(SIL-IoTs)。在现实的大规模 SIL-IoTs 部署场景中,通过网络收集的综合害虫信息可以有效定位害虫密集区域。然而,无线通信距离不固定、节点放电干扰等各种因素导致的非对称链路问题是 SIL-IoTs 部署的主要障碍。受此问题的启发,本文提出了基于非对称链路的害虫密集区定位策略(PALS)。首先,通过分析 SIL 节点的一跳和两跳信息来判断网络中的非对称节点。然后,使用 Gabriel 图或相对邻域图平面化算法对网络中的对称链路进行平面化处理。然后,使用快速剔除法和跨距实验去除平面化后的横截面。最后,通过计算 SIL 节点的放电次数和促进左手定则,PALS 成功缩小了计算得出的害虫密集区与实际密集区之间的差异。实验结果表明,在四种实验设置中,PALS 平均提高了 15%,最高提高了 42.2%,这表明与最先进的算法相比,PALS 具有更高的准确性和鲁棒性。
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引用次数: 1
A “Plant-Wearable System” for Its Health Monitoring by Intra- and Interplant Communication 通过植物内部和植物之间的通信监测植物健康状况的 "植物可穿戴系统
Pub Date : 2023-06-29 DOI: 10.1109/TAFE.2023.3284563
Umberto Garlando;Stefano Calvo;Mattia Barezzi;Alessandro Sanginario;Paolo Motto Ros;Danilo Demarchi
A step forward in smart agriculture is moving to direct monitoring plants and crops instead of their environment. Understanding plant status is crucial in improving food production and reducing the usage of water and chemicals in agriculture. Here, we propose a “plant-wearable,” low-cost, and low-power method to measure in-vivo green plant stem frequency as the indicator for plant watering stress status. Our method is based on measuring the frequency of a digital signal obtained with a relaxation oscillator where the plant is a part of the feedback loop. The frequency was correlated with the soil water potential, used as a critical indicator of plant water stress, and an 85% correlation was found. In this way, the measuring system matches all the requirements of smart agriculture and Internet of Things (IoT): ultra-low-cost, low-complexity, ultra-low-power, and small sizes, introducing the concept of wearability in plant monitoring. The proposed solution exploits the plant and the soil as a communication channel: the signal carrying the plant watering stress status information is transmitted to a receiving system connected to a different plant. The system's current consumption is lower than 50 $bm {mu }$A during the transmission in the plant and 40 mA for wireless communication. During inactivity periods, the total current consumption is lower than 15 $bm {mu }$A. Another important aspect is that the system has to be energy autonomous. Our proposal is based on energy harvesting solutions from multiple sources: solar cells and plant microbial fuel cells. This way, the system is batteryless, thanks to supercapacitors as a storage element. The system can be deployed in the fields and used to monitor plants directly in their environment.
智能农业向前迈出的一步是直接监测植物和作物,而不是它们所处的环境。了解植物的状态对于提高粮食产量、减少农业用水和化学品的使用至关重要。在这里,我们提出了一种 "植物可穿戴"、低成本、低功耗的方法,用于测量体内绿色植物茎的频率,作为植物浇水压力状态的指标。我们的方法基于测量通过弛豫振荡器获得的数字信号的频率,植物是反馈回路的一部分。该频率与作为植物水分胁迫关键指标的土壤水势相关,相关性达到 85%。因此,该测量系统符合智能农业和物联网(IoT)的所有要求:超低成本、低复杂性、超低功耗和小尺寸,并在植物监测中引入了可穿戴概念。建议的解决方案利用植物和土壤作为通信渠道:将携带植物浇水压力状态信息的信号传输到连接到不同植物的接收系统。在植物中传输时,系统的电流消耗低于 50 mA,无线通信时低于 40 mA。在非活动期间,总电流消耗低于 15 mA。另一个重要方面是,该系统必须具有能源自主性。我们的建议基于多种能源收集解决方案:太阳能电池和植物微生物燃料电池。这样,由于采用了超级电容器作为存储元件,该系统就无需电池。该系统可部署在田间地头,用于直接监测植物所处的环境。
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引用次数: 0
IEEE Circuits and Systems Society Information IEEE电路与系统学会信息
Pub Date : 2023-06-21 DOI: 10.1109/TAFE.2023.3282059
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引用次数: 0
IEEE Circuits and Systems Society Information IEEE电路与系统学会信息
Pub Date : 2023-06-21 DOI: 10.1109/TAFE.2023.3282061
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引用次数: 0
Effects of Carasau Dough Composition on the Microwave Dielectric Spectra up to 20 GHz 卡拉索面团成分对20 GHz微波介电光谱的影响
Pub Date : 2023-06-06 DOI: 10.1109/TAFE.2023.3277790
Matteo Bruno Lodi;Claudia Macciò;Nicola Curreli;Andrea Melis;Giuseppe Mazzarella;Alessandro Fanti
Carasau bread is a traditional product from Sardinia (IT). This flat bread is experiencing industrial advancement, through automation, and has great market potential. However, there is lack of understanding of how the composition (water content, salt, and yeast concentration) affects the product quality. In this work, a microwave dielectric spectroscopy study is performed to investigate how the composition of Carasau bread doughs influences the spectra of this food product up to 20 GHz. A third-order Cole–Cole model was used for the physical and quantitative understanding of the electromagnetic properties of this food product. Then, it has been studied how salt, yeast, and water variations affected the model parameters. This work could pave the route to the development of non-destructive, contactless microwave sensors for Carasau bread quality assessment.
卡拉索面包是撒丁岛的传统产品。这种扁平面包通过自动化正在经历工业进步,具有巨大的市场潜力。然而,人们对成分(含水量、盐和酵母浓度)如何影响产品质量缺乏了解。在这项工作中,进行了微波介电光谱研究,以研究卡拉索面包面团的成分如何影响这种食品在20 GHz以下的光谱。三阶Cole–Cole模型用于对该食品的电磁特性进行物理和定量了解。然后,研究了盐、酵母和水的变化如何影响模型参数。这项工作可能为开发用于卡拉索面包质量评估的非破坏性、非接触式微波传感器铺平道路。
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引用次数: 0
Wheat Spikes Height Estimation Using Stereo Cameras 利用立体相机估算小麦穗高
Pub Date : 2023-04-19 DOI: 10.1109/TAFE.2023.3262748
Amirhossein Zaji;Zheng Liu;Gaozhi Xiao;Pankaj Bhowmik;Jatinder S. Sangha;Yuefeng Ruan
There is a positive correlation between wheat plant height and lodging, yield, and biomass. So, in precision agriculture, a high-throughput estimation of the wheat plant's height in terms of its spikes is essential. This study aims to develop a straightforward, cost-effective method for measuring the height of wheat plants using stereo cameras. To collect the required datasets, we conducted an experiment in which we collected RGB images along with their depth layer using two renowned stereo cameras, OAKD and D455. Then, we used a deep learning model called mask region-based convolutional neural networks to localize and distinguish the spikes in the collected images. In this study, we localized the wheat spikes using object detection (OD) and instance segmentation (IS) models. The advantage of the OD model over the IS model is that its bounding box annotation procedure in the data preparation phase is significantly faster than the IS model's polygon annotation. However, the disadvantage of OD is that there are many background pixels in each predicted bounding box, which reduces the performance of height estimation. To facilitate the annotation process of the collected datasets, we also developed a hybrid scale-invariant feature transform random forest-based active learning algorithm to transfer the annotations of one camera to the other. The results show that the OAKD camera performs better than the D455 camera for wheat plant height estimation due to its higher RGB quality and better matching of the mono camera images. Using the OAKD camera and IS model, the algorithm proposed in this study is able to estimate wheat height with mean absolute percentage error values of 0.75% and 0.67% at the spike and plot levels, respectively.
小麦株高与倒伏、产量和生物量呈正相关。因此,在精准农业中,通过高通量估计小麦植株的穗高是至关重要的。这项研究旨在开发一种简单、经济高效的方法,使用立体相机测量小麦植株的高度。为了收集所需的数据集,我们进行了一项实验,使用两个著名的立体相机OAKD和D455收集RGB图像及其深度层。然后,我们使用一种称为基于掩模区域的卷积神经网络的深度学习模型来定位和区分采集图像中的尖峰。在本研究中,我们使用对象检测(OD)和实例分割(IS)模型对小麦穗进行了定位。OD模型相对于IS模型的优势在于,其在数据准备阶段的边界框注释过程明显快于IS模型的多边形注释。然而,OD的缺点是在每个预测的边界框中都有许多背景像素,这降低了高度估计的性能。为了方便收集数据集的注释过程,我们还开发了一种基于比例不变特征变换随机森林的混合主动学习算法,将一个相机的注释转移到另一个相机。结果表明,OAKD相机在小麦株高估计方面比D455相机表现更好,因为它具有更高的RGB质量和更好的单相机图像匹配性。利用OAKD相机和IS模型,本研究提出的算法能够在穗部和小区水平上估计小麦高度,平均绝对百分误差值分别为0.75%和0.67%。
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引用次数: 1
Intelligent Mango Fruit Grade Classification Using AlexNet-SPP With Mask R-CNN-Based Segmentation Algorithm 基于掩码R-CNN-的AlexNet SPP芒果分级智能分割算法
Pub Date : 2023-03-05 DOI: 10.1109/TAFE.2023.3267617
Jui-Feng Yeh;Kuei-Mei Lin;Chen-Yu Lin;Jen-Chun Kang
In this article, the grades of mangoes were classified using an AlexNet–spatial pyramid pooling network (SPP-Net) with a segmentation algorithm based on a Mask region-based convolutional neural network (R-CNN). Computer vision technologies have begun to be used for fruit grade classification, and this is a major topic of interest in agricultural automation. However, because insufficient fruit grade classification accuracy is achieved with these technologies, manual processing must be performed. The accuracy of fruit grade classification can be enhanced using a Mask R-CNN, SPP-Net, and specific background processing. The designed mango grade classification system contains four modules: 1) a user interface module, 2) an object detection module, 3) an image preprocessing module, and 4) a fruit grade classification module. A camera is used to capture images of mangoes for display on the user interface. The object segmentation module generates a mango shape mask and bounding box by using a Mask R-CNN. The image preprocessing module uses the generated bounding box and mango shape mask to crop the mango and color the background blue. Finally, AlexNet–SPP-Net outputs the fruit grade. We validated the proposed approach by implementing it in mango grade classification and comparing its accuracy with that of relevant existing methods from the literature. According to the experimental results, the proposed approach outperforms the traditional AlexNet-based approach.
在本文中,使用AlexNet–空间金字塔池网络(SPP-Net)和基于Mask区域的卷积神经网络(R-CNN)的分割算法对芒果的等级进行了分类。计算机视觉技术已开始用于水果等级分类,这是农业自动化领域的一个主要研究课题。然而,由于这些技术无法达到足够的水果等级分类精度,因此必须进行手动处理。使用Mask R-CNN、SPP-Net和特定的背景处理可以提高水果等级分类的准确性。设计的芒果等级分类系统包括四个模块:1)用户界面模块,2)物体检测模块,3)图像预处理模块,4)水果等级分类模块。相机用于捕捉芒果的图像以显示在用户界面上。对象分割模块使用mask R-CNN生成芒果形状的掩码和边界框。图像预处理模块使用生成的边界框和芒果形状遮罩来裁剪芒果并将背景染成蓝色。最后,AlexNet–SPP-Net输出水果等级。我们通过在芒果等级分类中实施该方法,并将其准确性与文献中现有的相关方法进行比较,验证了所提出的方法。根据实验结果,所提出的方法优于传统的基于AlexNet的方法。
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引用次数: 3
期刊
IEEE Transactions on AgriFood Electronics
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