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1.
Precision agriculture needs integrated pest management (IPM), for which detection and identification of target invertebrate species is a prerequisite. Researchers have been developing various technologies to detect pests more efficiently and accurately. However, these existing sensing technologies still have limitations for effective infield applications. This review paper aims to explore the relative technologies and find a sensing method that has potential to detect and identify common invertebrates on crops, such as butterflies, locusts, snails and slugs. It was found that there are two main research branches for invertebrate detection and identification: acoustic sensing and machine vision system (MVS). Acoustic sensing is suitable for detecting and identifying pests in soil, stored grains and wood, while usually acoustic sensors need to be attached to samples for inspection, which causes difficulties for efficient infield applications. MVS has the potential to provide a more effective and flexible way to detect and identify invertebrates on crops. In recent work with MVS, the technologies of invertebrate identification have been intensively studied, however, infield detection is relatively weak. This review points out the current research gaps and then discusses the potential research directions. 相似文献
2.
Machine vision technologies have shown advantages for efficient and accurate plant inspection in precision agriculture. Regarding the balance between accuracy of inspection and compactness for infield applications, multispectral imaging systems would be more suitable than RGB colour cameras or hyperspectral imaging systems. Multispectral image registration (MIR) is a key issue for multispectral imaging systems, however, this task is challenging. First of all, in many cases, two images needing registration do not have a one-to-one linear mapping in 2D space and therefore they cannot be aligned in 2D images. Furthermore, the general MIR algorithms are limited to images with uniform intensity and are incapable of registering images with rich features. This study developed a machine vision system (MVS) and a MIR method which replaces 2D-2D image registration by 3D-3D point cloud registration. The system can register 3D point clouds of ultraviolet (UV), blue, green, red and near-infrared (NIR) spectra in 3D space. It was found that the point clouds of general plants created by images of different spectral bands have a complementary property, and therefore a combined point cloud, called multispectral 3D point cloud, is denser than any cloud created by a single spectral band. Intensity information of each spectral band is available in a multispectral 3D point cloud and therefore image fusion and 3D morphological analysis can be conducted in the cloud. The MVS could be used as a sensor of a robotic system to fulfil on-the-go infield plant inspection tasks. 相似文献
3.
Development of an automatic monitoring system for rice light-trap pests based on machine vision 
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下载免费PDF全文 YAO Qing FENG Jin TANG Jian XU Wei-gen ZHU Xu-hua YANG Bao-jun Lü Jun XIE Yi-ze YAO Bo WU Shu-zhen KUAI Nai-yang WANG Li-jun 《农业科学学报》2020,19(10):2500-2513
Monitoring pest populations in paddy fields is important to effectively implement integrated pest management. Light traps are widely used to monitor field pests all over the world. Most conventional light traps still involve manual identification of target pests from lots of trapped insects, which is time-consuming, labor-intensive and error-prone, especially in pest peak periods. In this paper, we developed an automatic monitoring system for rice light-trap pests based on machine vision. This system is composed of an intelligent light trap, a computer or mobile phone client platform and a cloud server. The light trap firstly traps, kills and disperses insects, then collects images of trapped insects and sends each image to the cloud server. Five target pests in images are automatically identified and counted by pest identification models loaded in the server. To avoid light-trap insects piling up, a vibration plate and a moving rotation conveyor belt are adopted to disperse these trapped insects. There was a close correlation(r=0.92) between our automatic and manual identification methods based on the daily pest number of one-year images from one light trap. Field experiments demonstrated the effectiveness and accuracy of our automatic light trap monitoring system. 相似文献
4.
为实现油菜作物模型的可视化研究,给油菜作物的数字化管理提供数据基础,以感染虫害的苗期油菜为研究对象,采用MVS序列图像技术,搭建MVS技术的序列图像采集平台。根据SFM和PMVS算法获得虫害油菜的稀疏点云数据和稠密点云数据,同时,探索序列图像数量对于特征点匹配的影响。对MVS序列图像技术获得的虫害油菜三维点云数据,采用滤波、精简、Alpha-Shape曲面重建等处理,得到虫害油菜的三维形态曲面模型。结果显示,使用图像数目多和8邻域匹配两者相结合的方法可以又快又好地匹配图像特征点;在获得合适的Alpha值情况下,Alpha-Shape算法可以真实形象地表现出虫害油菜的生长状态。 相似文献
5.
Integrating multispectral reflectance and fluorescence imaging for defect detection on apples 总被引：1，自引：0，他引：1
Diwan Ariana Daniel E. Guyer Bim Shrestha 《Computers and Electronics in Agriculture》2006,50(2):148-161
This research investigated multispectral imaging to detect various defects on apples. An integrated approach using multispectral imaging in reflectance and fluorescence modes was used to acquire images of three varieties of apples. Eighteen images from a combination of filters ranging from the visible region through the NIR region and from three different imaging modes (reflectance, visible light induced fluorescence, and UV induced fluorescence) were acquired for each apple as a basis for pixel-level classification into normal or disorder tissue. Artificial neural network classification models were developed for two classification schemes, a two-class and a multiple-class. In the two-class scheme, pixels were categorized into normal or disordered tissue, whereas in the multiple-class scheme, pixels were categorized into normal, bitter pit, black rot, decay, soft scald, and superficial scald tissues. A 10-fold cross validation technique was used to assess the performance of the neural network models. The integrated imaging model of reflectance and fluorescence was effective on Honeycrisp variety, whereas single imaging models of reflectance or fluorescence was effective on Redcort and Red Delicious. The technique is promising for accurate recognition of different types of disorder on apple. 相似文献
6.
为利用计算机或人工智能技术协助番茄病虫害防治，以存在病虫害侵害问题的番茄植株图像为研究对象，针对番茄病虫害目标小而密的特点提出基于Swin Transformer的YOLOX目标检测网络，用于精确定位图像中的病虫害目标，并采用基于经典卷积神经网络构建的旋转不变Fisher判别CNN分类网络，以此提高病虫害分类的准确率。结果表明:1)将测试结果与传统的目标检测模型和分类模型作对比，基于Swin Transformer的YOLOX网络在番茄病虫害测试集上的精确度比Faster R-CNN和SSD分别高了7.9%和9.5%，旋转不变Fisher判别CNN对病虫害类别的识别准确率与AlexNet、VGGNet相比分别提升了8.7%和5.2%；2)与基于Transformer的目标检测模型DETR和近年来新兴的图像分类模型Vision Transformer(ViT)在番茄病虫害测试集上的结果相比较，本研究的检测和分类方法也存在优势，病虫害检测精度和分类准确率分别提高了3.9%和4.3%。此外消融试验也证明了本研究方法改进的有效性。总之，本研究所构建的网络在番茄病虫害的目标检测和分类识别方面的性能优于其他网络，有助于提升番茄病虫害的防治效果，对计算机视觉在农业领域的应用具有重要意义。 相似文献
7.
The design and calibration of a three-band image acquisition system was reported in this paper. The prototype system developed in this research was a three-band spectral imaging system that acquired two visible-band images and one NIR image simultaneously. This was accomplished by using a three-port imaging system that consisted of three identical monochrome cameras, an optical system, and three interchangeable optical filters. Spectral reflectance from an object was collimated by a front lens, and split in three ways by a cold mirror and beamsplitter: a cold mirror reflects 90% visible light and transmits 80% NIR light. The visible light was again split identically into two directions by an additional beamsplitter. Focusing lenses then projected each image onto its corresponding sensor. By incorporating an interchangeable filter design, the imaging system can measure any two visible spectral bands that range between 400 nm and 700 nm, and one NIR band that ranges between 700 nm and 1000 nm without any complicated manufacturing process. In order to co-register the three images, a system-specific calibration algorithm was developed that compensates for lens-sensor geometric misalignments.The prototype imaging system and the system calibration algorithm were tested and evaluated for image alignment accuracy. The imaging system acquired three-band images of 3D objects with 0.39 pixel misalignment error on average. 相似文献
8.
A new method is presented for detecting rows of rice seedlings to facilitate the navigation of rice transplanters. Generally, an independent NIR or RGB camera is used as a vision sensor for agricultural vehicles; however, strong reflections on the water surface make row detection more difficult in flooded paddy fields compared to dry fields. To solve this problem, we developed a dual-spectral camera system that consists of a pair of low-cost monochrome cameras with optical filters. Different wavelength images of the same location can be taken simultaneously in real-time. An experiment conducted under cloudy conditions showed that this system could reduce water surface noise and clearly detect seedling rows. 相似文献
9.
【目的】研究一种基于卷积神经网络的危害棉叶症状识别技术,提高棉花病虫害的识别准确率。【方法】基于caffe深度学习框架,在CaffeNet网络结构基础上增加一层全连接层(记为CaffeNet+1),并结合迁移学习方法对网络进行训练。采集健康、红叶茎枯、红蜘蛛、枯萎、黄萎、双斑萤叶甲、蚜虫、褐斑棉叶图像各975张作为样本集。随机选取验本集中80%的图像样本作为训练集,剩余20%作为测试集。【结果】迁移学习方式下学习率取0.005时的CaffeNet+1模型最优,在测试集上其识别准确率可达98.9%。【结论】在与全新学习模式下的CaffeNet模型相比,该方法可加速网络模型收敛,且具有更高的识别准确率,该技术方法在准确识别田间病虫害棉叶后表现症状的图像写出来具体方面具有重要的应用价值。 相似文献
10.
Currently, sugar snap peas are harvested manually. In high-cost countries like Norway, such a labour-intensive practise implies particularly large costs for the farmer. Hence, automated alternatives are highly sought after. This project explored a concept for robotic autonomous identification and tracking of sugar snap pea pods. The approach was based on a combination of visible–near infrared reflection measurements and image analysis, along with visual servoing. A proof-of-concept harvesting platform was implemented by mounting a robotic arm with hand-mounted sensors on a mobile unit. The platform was tested under plastic greenhouse conditions on potted plants of the sugar snap pea variety Cascadia using LED-lights and a partial shade. The results showed that it was feasible to differentiate the pods from the surrounding foliage using the light reflection at the spectral range around 970 nm combined with elementary image segmentation and shape modelling methods. The proof-of-concept harvesting platform was tested on 48 representative agricultural environments comprising dense canopy, varying pod sizes, partial occlusions and different working distances. A set of 104 images were analysed during the teleoperation experiment. The true positive detection rate was 93 and 87% for images acquired at long distances and at close distances, respectively. The robot arm achieved a success rate of 54% for autonomous visual servoing to a pre-grasp pose around targeted pods on 22 untouched scenarios. This study shows the potential of developing a prototype robot for semi-automated sugar snap pea harvesting. 相似文献
11.
基于叶片图像多特征融合的观叶植物种类识别 总被引：9，自引：4，他引：5
叶片图像特征提取对于植物自动分类识别有着重要的研究意义。本文以观叶植物叶片为研究对象,综合提取叶片图像的颜色、形状和纹理特征,基于支持向量机(SVM)原理提出了基于图像分析的观叶植物自动识别分类方法。通过对50种观叶植物样本图像进行训练和识别,与BP神经网络和KNN识别方法进行比较,本文所采用的SVM分类器的识别率能够达到91.41%,取得了较好的识别效果。 相似文献
12.
为探究斑马鱼在不同光照条件下的趋光行为差异,记录了六月龄的斑马鱼成鱼在紫外光（ultraviolet, UV）和可见光照射下的行为反应和在无光、紫光（420 nm）、蓝光（460 nm）、绿光（500 nm）、黄光（585 nm）和红光（620 nm）六种光照条件下的趋光分布情况。结果表明,刺激光为UV时,斑马鱼优先游向黑暗环境一侧,刺激光为可见光时,斑马鱼优先游向可见光一侧;UV和可见光分别设置在试验区A和试验区B时,随着UV刺激强度增加,斑马鱼的偏好指数未呈现明显的增加趋势,UV和可见光同时设置在试验区B时,随着UV刺激强度增加,斑马鱼的偏好指数呈现出明显的下降趋势;5 min和30 min时斑马鱼的平均分布率均呈现红光区>紫光区>蓝光区>黄光区,绿光区的平均分布率在5 min时高于红光区,在30 min时介于紫光区和蓝光区之间。研究表明,斑马鱼趋向可见光而远离紫外光,表现出明显的避UV性和趋可见光性,且避UV性与趋可见光性之间存在显著的拮抗作用,而无协同作用。此外,斑马鱼对红光、绿光和紫光的喜好程度较高,对黄光的喜好程度较低。研究结果可为斑马鱼视觉生态的研究提供参考。 相似文献
13.
Lettuce calcium deficiency detection with machine vision computed plant features in controlled environments 总被引：1，自引：0，他引：1
Conventional greenhouse environmental conditions are determined by observation. However, destructive or invasive contact measurements are not practical for real-time monitoring and control applications. At the canopy scale, machine vision has the potential to identify emerging stresses and guide sampling for identification of the stressor. A machine vision-guided plant sensing and monitoring system was used to detect calcium deficiency in lettuce crops grown in greenhouse conditions using temporal, color and morphological changes of the plant. The machine vision system consisted of two main components: a robotic camera positioning system and an image processing module. The machine vision system extracted plant features to determine overall plant growth and health status, including top projected canopy area (TPCA) as a morphological feature; red-green-blue (RGB) and hue-saturation-luminance (HSL) values as color features; and entropy, energy, contrast, and homogeneity as textural features. The machine vision-guided system was capable of extracting plant morphological, textural and temporal features autonomously. The methodology developed was capable of identifying calcium-deficient lettuce plants 1 day prior to visual stress detection by human vision. Of the extracted plant features, TPCA, energy, entropy, and homogeneity were the most promising markers for timely detection of calcium deficiency in the lettuce crop studied. 相似文献
14.
小麦是中国主要粮食作物,栽培品种多、种植面积大、分布区域广、生长周期长,容易遭受病虫害威胁,快速监测和准确识别病虫害成为一项重要的课题。基于前期构建的小麦物联网监控系统平台,研发了集成图像获取、图像识别诊断于一体的应用系统。初步研究了小麦比较常见的三种病虫害的识别与诊断方法,并利用图像分割、特征提取及数字图像分类识别技术,将物联网系统获取的感白粉病、锈病、蚜虫的不健康叶片与健康小麦叶片的图片分别进行对比实验研究。实验结果显示,识别率都较为理想,其中白粉病的识别率为82.5%,锈病、蚜虫和健康叶片的识别率都在95%以上。将病虫害图像识别技术与物联网技术结合,方便病虫害图像的远程传输、多点获取等优点,大幅度提升对病虫害远程识别和诊断能力,具有广阔的发展前景。 相似文献
15.
基于卷积神经网络的水稻虫害识别方法 总被引：1，自引：0，他引：1
为解决人工识别水稻虫害难度大、费时费力的问题,研究了一种自动识别水稻虫害的方法。选取2 500张红袖蜡蝉、稻绿蝽、稻螟蛉、点蜂缘蝽、大螟图片建立数据库,利用深度卷积神经网络提取水稻虫害数据集特征,采用随机梯度下降法训练,得到最优权重文件。测试训练好的模型,并对比Faster-RCNN、SSD和YOLOv3算法的效果。结果表明,YOLOv3算法的平均精度最高,其在红袖蜡蝉、稻绿蝽、稻螟蛉、点蜂缘蝽、大螟5种水稻虫害中分别为97.40%、88.76%、85.74%、92.96%、94.78%,五类水稻虫害mAP为91.93%。与Faster-RCNN算法相比,平均准确率高1.43个百分点,单张图像检测耗时减少853.68 ms;与SSD算法相比,平均准确率高5.56个百分点,单张图像检测耗时减少2.9 ms。选择5类比较具有代表性的水稻虫害图片进行测试,对于叶片遮挡目标和相似背景等情况,YOLOv3算法能够正确识别不会出现漏检错检,且识别准确率大于98%。将YOLOv3算法引入田间复杂情况下的水稻虫害识别是可行的,具有较高的平均准确率以及较快的检测速度,能够准确识别水稻虫害,这对于水稻虫害防治和田间喷药等方面具有重要意义。 相似文献
16.
Temporal and spatial changes in parameters of fast chlorophyll fluorescence kinetics (ground fluorescence, Fo and maximal
fluorescence, Fm) and red/NIR reflectance were assessed with a Pulse-Amplitude-Modulated (PAM)-Imaging system on a daily basis
over a period of 2 weeks following inoculation of wheat leaves with powdery mildew and leaf rust. The early detection of these
infections by means of fluorescence imaging was possible 2–3 days before visual symptoms or significant changes in normalised-differenced-vegetation
index (NDVI) became apparent. The initial infection of both fungi caused an increase in Fo and decrease in photochemical efficiency
(Fv/Fm, Fv/Fo). The appearance and development of fungal pustules was accompanied by reduction in Fo and Fm. This resulted
mainly from lower absorption of fluorescence exciting light by the leaf mesophyll due to the shielding effect of fungal mycelium,
and to lesser extent from the chlorophyll breakdown underneath pustules. Among the evaluated fluorescence parameters, Fv/Fo
displayed the most pronounced response to both kinds of infection. Mildew infection influenced chlorophyll fluorescence neither
in the direct vicinity of mycelium nor in the apparently healthy leaf regions. Rust infected plants, in contrast, displayed
significantly reduced photochemical efficiency Fv/Fm and Fv/Fo in chlorotic tissue around pustules. The same, but less pronounced
tendency was found in the apparently healthy regions of rust infected leaves in the last days of the experiment. Dark adaptation
of leaves proved to be necessary for accurate detection of both pathogen infections by means of fluorescence imaging. Additional
experiments are needed to estimate the potential of this technique for remote sensing under field conditions. 相似文献
17.
【目的】智能虫情测报灯诱捕到的农业害虫因种类繁多、虫体姿态多样、鳞片脱落等原因造成有些害虫图像存在种间相似和种内差异的现象。为了提高农业灯诱害虫识别率,针对YOLOv4检测模型检测到且容易混淆的19种灯诱害虫,本文提出了基于双线性注意力网络的农业灯诱害虫细粒度图像识别模型。【方法】首先,根据灯诱害虫外观图像的相似性和检测误检的情况,将19种害虫分为6类;将所有害虫图像通过补边操作使得长宽相等,并缩放至统一尺寸224×224像素。为了提高模型的鲁棒性和泛化能力,对害虫图像进行镜像翻转、旋转180度、高斯噪声和均值滤波的数据增强,训练集、验证集和测试集样本量按照8:1:1比例划分。然后,针对6类19种农业灯诱害虫细粒度图像,建立了基于双线性注意力网络的农业灯诱害虫识别模型（bilinear-attention pest net,BAPest-net）,模型包括双线性特征提取、注意力机制和分类识别3个模块;通过修改特征提取模块的下采样方式提高特征提取能力;添加注意力机制模块让整个模型更关注于局部细节的特征,将双线性结构中的上下两个注意力机制的输出进行外积运算增加细粒度特征的权重,提高识别的准确性和学习效率;模型优化器使用随机梯度下降法SGD,分类模块中使用全局平均池化,旨在对整个网络从结构上做正则化防止过拟合。最后,在同一个训练集训练VGG19、Densenet、ResNet50、BCNN和BAPest-net 5个模型,对6类相似的19种农业灯诱害虫进行识别,以精准率、Precision-Recall（PR）曲线和平均识别率作为模型的评价指标。【结果】BAPest-net对6类相似的19种农业灯诱害虫平均识别率最高,达到94.9%;BCNN次之,为90.2%;VGG19模型最低,为82.1%。BAPest-net识别的6类害虫中4类鳞翅目害虫的平均识别率均大于95%,表明该模型能较好地识别出鳞翅目害虫。测试结果中仍存在少数相似度较高的害虫误判,特别当害虫腹部朝上或侧身,种类特征不够明显的时候容易引起相似害虫的误判。对于区分度较低的相似害虫需要更多的训练样本以获取更多的特征,提高模型的识别率和泛化能力。【结论】基于双线性注意力网络的农业灯诱害虫细粒度图像识别模型可以自动识别6类相似的19种农业灯诱害虫,提高了农业灯诱害虫自动识别的准确率。 相似文献
18.
Perla Chávez Christian Yarlequé Hildo Loayza Víctor Mares Paola Hancco Sylvie Priou María del Pilar Márquez Adolfo Posadas Percy Zorogastúa Jaume Flexas Roberto Quiroz 《Precision Agriculture》2012,13(2):236-255
Potato bacterial wilt, caused by the bacterium Ralstonia solanacearum race 3 biovar 2 (R3bv2), affects potato production in several regions in the world. The disease becomes visually detectable when extensive damage to the crop has already occurred. Two greenhouse experiments were conducted to test the capability of a remote sensing diagnostic method supported by multispectral and multifractal analyses of the light reflectance signal, to detect physiological and morphological changes in plants caused by the infection. The analysis was carried out using the Wavelet Transform Modulus Maxima (WTMM) combined with the Multifractal (MF) analysis to assess the variability of high-resolution temporal and spatial signals and the conservative properties of the processes across temporal and spatial scales. The multispectral signal, enhanced by multifractal analysis, detected both symptomatic and latently infected plants, matching the results of ELISA laboratory assessment in 100 and 82%, respectively. Although the multispectral method provided no earlier detection than the visual assessment on symptomatic plants, the former was able to detect asymptomatic latent infection, showing a great potential as a monitoring tool for the control of bacterial wilt in potato crops. Applied to precision agriculture, this capability of the remote sensing diagnostic methodology would provide a more efficient control of the disease through an early and full spatial assessment of the health status of the crop and the prevention of spreading the disease. 相似文献
19.
农作物受害虫侵害严重,给农业生产带来巨大损失,喷施化学杀虫剂使害虫产生抗药性,且污染环境。近年来,转基因技术研究不断深入,抗虫转化研究工作进展迅速。鉴定转基因植物的方法技术有很多种,在此主要阐述了利用标记基因进行鉴定,分子标记,免役技术三大类方法,并对其原理分别进行了简单介绍。 相似文献
20.
Testing the vision of cataract patients by means of laser-generated interference fringes 总被引：3，自引：0，他引：3
D G Green 《Science (New York, N.Y.)》1970,168(936):1240-1242
In a new technique for measuring the visual acuity of cataract patients the light from a laser is used to form interference patterns of variable fineness on the patient's retina. The fineness of the interference pattern that the patient can detect gives an indication of the potential for improved vision. Comparison of this estimate of the potential with the patient's vision after cataract extraction shows that this test can indicate the condition of the fovea behind a cataract. 相似文献