模糊植物病虫害图像的检测

关于植物病虫害图像准确检测问题,由于通过卫星遥感技术采集的图像不清。传统的植物病虫害检测算法依靠清晰的像素信息,因采集的病虫害图像质量较低,图像模糊,造成关键像素信息的丢失、混浊,算法存在着病斑检测结果不准确的问题。为了提高准确性,提出了低质量图像的病虫害检测方法。通过建立细节点的特征库,利用模糊判别的方法对建立的少量细节特征进行检测,然后判别是否为病虫害特征.新方案只需极少的细节特征就能完成了检测,避免了对大规模像素的依赖。实验表明,采用的方法能够有效分割大部分低质量图像的病虫害特征,取得了比较好的效果。     

Role of digital,hyper spectral,and SAR images in detection of plant disease with deep learning network

In agriculture, plants plays a major role and taking attention of plants is very critical. Generally, the plant are affected through various diseases like fungi, virus and bacteria. Finding of these diseases are main challenging task for a plant disease identification and classification. In the past few years, machine learning (ML) methods have been developed for the plant disease detection. But, the advancement in a subsection of ML, that is, DL (deep learning) models provide a great solution in the agricultural areas in the recent decades. The main objective of the paper is to provide the survey of numerous DL classification models for the plant disease detection by analysing the digital, hyper spectral and SAR images. This paper provide the review of different deep learning architectures which is utilized for plant disease identification and classification. The role of digital, hyper spectral and SAR images with deep learning models for plant disease detection is reviewed. Further, the different well-known DL architecture for plant disease classification is studied. In addition, the current challenges and their solutions of plant disease identification are discussed. Also, the application of DL and advantages/disadvantages of DL structure in plant domain are presented. Finally, the future scope of DL structure for plant domain is discussed. The preparation of this review is to permit future research to learn higher competences of deep learning while identifying plant diseases by enhancing system performance and accuracy.

     

基于TensorRT的植物叶片病害实时检测分类模型优化

为了提高边缘计算设备对植物叶片病害检测的识别速率,本研究采用卷积神经网络搭建了植物叶片目标识别模型和植物叶片病害分类模型,并且使用OpenCV将两个模型整合成植物叶片病害检测系统.通过SSD (single shot multibox detector)算法对植物叶片的目标区域进行定位并裁剪,再利用植物叶片病害分类模型对裁剪的植物叶片区域进行病害分类.同时,通过TensorRT加速推理对分类模型进行优化处理,以及在同一台主机设备和Jetson Nano计算平台上,对优化前后的模型进行了对比实验.实验表明,在同一主机设备上优化后的植物分类模型识别速率提升22倍.同时,优化后的分类模型使植物叶片病害检测系统识别速率提升7倍.而将优化后的系统部署在Jetson Nano计算平台上,对比优化前的植物叶片病害检测速率提升10倍,实现了实时的植物叶片病害检测.     

案例推理式园林植物病虫害诊断系统的实现

为减少园林植物养护管理对于病虫害专家的依赖,运用知识工程相关理论,进行了园林植物病虫害诊断系统的知识库、案例库及推理机制的设计。通过专家系统技术与主流计算机网络信息技术的结合,构建了服务于我国园林管理机构、面向我国数字化园林的新型网络智能系统。     

基于二进制区分矩阵的属性约简方法及应用

以粗糙集理论为基础,以属性重要度为启发式信息来指导约简过程,提出了一种改进的基于二进制区分矩阵的属性约简算法。以兰州市各区县主要经济林重点病虫害发生情况为例,使用该算法对影响病虫害发生的条件属性集进行约简,为决策者提供辅助决策支持,同时验证了该算法是有效可行的。     

基于图像分析的橘科植物病害识别技术

为实现橘科植物病害的计算机识别和病害程度的科学评价,提出通过分析病害图像,自动提取有效特征,设计分类器模型识别的方法．深入研究了怎样对病害图像进行自动增强处理、病斑分割、特征提取,以及怎样构建分类器模型等技术．最后以常见也容易混淆的五种柠檬病害为例,提取其病斑色调、纹理、形态三种特征向量,分别采用支持向量机和BP神经网络进行训练、测试．实验结果表明,该方法能很好识别植物病害类别,为科学防治和病害危害程度评价提供科学依据．     

植物抗病分子机制及抗病基因工程研究进展

本文从植物与病原物互作的遗传基础角度讨论了植物抗病分子机制以及植物抗病毒、真菌、细菌和线虫病基因工程的主要研究进展,并对有关进展作了简单评析。     

An automatic plant leaf disease identification using DenseNet-121 architecture with a mutation-based henry gas solubility optimization algorithm

Neural Computing and Applications - Farmers are struggling to provide the fast-growing population with sufficient agricultural products, while plant diseases result in devastating food loss. The...     

图像处理技术在柠檬病害诊断中的应用

文章深入探讨了图像增强,基于病斑颜色与外轮廓相结合的病斑分割,有效特征提取,以及分类器构建等相关技术。并以五种容易混淆的病害为例,提取其病斑的色调、纹理、形态三种特征向量,分别采用支持向量机和BP神经网络进行训练、测试。实验结果表明该方法能很好的识别柠檬病害类别,为科学防治和病害危害程度评价提供科学依据。     

生物农药与设施农业病害的防治

设施农业是我国传统农业向现代农业转变的主要形式之一,同时为我国无公害农业的产业化经营提代了设施保障。植物病害频繁发生是威胁设施农业发展的主要限制因子,目前由于采用化学防治措施所带来的病害抗药性、农药残留和环境污染等问题已无法满足设施农业生产优质农副产品的要求,所以生物农药的研发与应用,为解决这一难题提供了技术保证,生物农药产业化、生物农药的质量监测与风险评估、加大对生物农药开发扶持力度,是我国设施农业病害生物防治中亟等解决的问题。     

设施栽培中逆境对园艺作物生长发育及其病害的影响祆

根据近年来国内外的相关研究进展,针对设施园艺生产中普遍出现的作物生长不良和病害严重等问题,在分析设施内各环境因子改变的基础上,综述了光照不足、温湿度不适、土壤次生盐渍化、酸化、养分失衡以及土壤生物学环境恶化等设施逆境对作物生长发育及其病害的影响。     

Low complexity image enhancement GAN-based algorithm for improving low-resolution image crop disease recognition and diagnosis

Multimedia Tools and Applications - Image analysis plays a crucial role in many real-world applications such as smart agriculture. For plant diseases diagnosis, one of the most recent challenges is...     

沈阳市城区园林树木病虫害的调查

沈阳市城区园林树木病虫害种类及发生现状调查结果表明：病虫害在和平、沈和、皇姑、大东，铁西、东陵和于洪等地区分布普遍。共发现为害园林树木主要害虫37种，其中叶部害虫14种，枝干部害虫19种、根部害虫4种；主要病害15种，其中叶部病害8种，茎干部病害6种、根部病害1种。树木长期生长不良，栽培密植、树种间不合理配植，不遵守适地适树原则等是沈阳市区内园林树木发生病虫害的主要原因。     

知识管理框架ReKM在农业病虫害诊断中的应用

在农业病虫害诊断领域，传统的专家系统往往在设计时就硬编码好了知识结构，在不重新改动代码的情况下，具有很差的扩展性。介绍了一个基于本体和案例推理的可重构知识管理框架ReKM。该框架利用本体作为知识结构的描述，由知识存储层、本体层和应用层组成。利用本体模型的可重构特性，一定程度上克服了传统专家系统可扩展性不足的缺点。文中详细阐述了框架中基于案例的检索算法和简单推理的概念。最后介绍了基于该框架开发的一个系统在某农科院病虫害诊断领域的应用情况和今后研究的方向。     

Plant leaf disease classification and damage detection system using deep learning models

Agriculture is the primary source of livelihood for about 70% of the rural population in India. The crop variety cultivated in India is very diverse. There are more than 500 crop varieties grown in India. Despite the technological advances, the agricultural practices are still manual and involve less automation than western countries. Most of the diseases affecting a plant will reflect the damage in the leaves. The diseases affecting the plant can thus be identified from the leaf images. This paper presents an automatic plant leaf damage detection and disease identification system. The first stage of the proposed method identifies the type of the disease based on the plant leaf image using DenseNet. The DenseNet model is trained on images categorized according to their nature, i.e., healthy and the type of the disease. This model is then used for testing new leaf images. The proposed DenseNet model produced a classification accuracy of 100%, with fewer images used during the training stage. The second stage identifies the damage in the leaf using deep learning-based semantic segmentation. Each RGB pixel value combination in the image is extracted, and supervised training is performed on the pixel values using the 1D Convolutional Neural Network (CNN). The trained model can detect the damage present in the leaves at a pixel level. Evaluation of the proposed semantic segmentation resulted in an accuracy of 97%. The third stage suggests a remedy for the disease based on the disease type and the damage state. The proposed method detects various defects in different plants in the experimental analysis, namely apple, grape, potato, and strawberry. The proposed model is compared with the existing techniques and obtained better performance in comparison with those methods.

     

Research Progress in Monitoring and Forecasting of Crop Diseases and Pests by Remote Sensing

Crop diseases and pests are the first natural biological hazards that threaten food production and quality.The investigation and sampling in field of plant protection department can’t meet demand of the accurate,non-destructive and efficient monitoring and warning.Currently,remote sensing which can monitor dynamically in real time provides the possibility for the rapid acquisition of continuous surface information,and is also the main development direction monitoring and prediction of crop diseases and pests in the future.Research status of three main directions,including classification of different stresses,severity estimation and stress forecasting,are summarized,and the methods of feature extraction,feature selection,and algorithms are expounded.Then,the application of diseases and pests of three major foodsby remote sensing was analyzed by means of domestic retrieval platforms.On this basis,the existing problems and future development trend of monitoring and forecasting of crop diseases and pests by remote sensing are discussed to promotethe long-term mechanism of agricultural sustainable development.     

A Hybrid Approach for Plant Disease Detection Using E-GAN and CapsNet

Crop protection is a great obstacle to food safety, with crop diseases being one of the most serious issues. Plant diseases diminish the quality of crop yield. To detect disease spots on grape leaves, deep learning technology might be employed. On the other hand, the precision and efficiency of identification remain issues. The quantity of images of ill leaves taken from plants is often uneven. With an uneven collection and few images, spotting disease is hard. The plant leaves dataset needs to be expanded to detect illness accurately. A novel hybrid technique employing segmentation, augmentation, and a capsule neural network (CapsNet) is used in this paper to tackle these challenges. The proposed method involves three phases. First, a graph-based technique extracts leaf area from a plant image. The second step expands the dataset using an Efficient Generative Adversarial Network E-GAN. Third, a CapsNet identifies the illness and stage. The proposed work has experimented on real-time grape leaf images which are captured using an SD1000 camera and PlantVillage grape leaf datasets. The proposed method achieves an effective classification of accuracy for disease type and disease stages detection compared to other existing models.     

基于物联网的苹果树病虫防治专家系统设计与应用可行性研究

通过物联网感知技术、传输技术和处理技术为载体,结合专家系统软件,实现苹果树病虫害防治的智能化识别、定位、监控和管理的一种精细农业生产规范化管理模式,能够完成对果树及果品的生长情况的全天监测,实现对果树病虫害发生发展情况的跟踪分析,并根据专家系统的诊断意见进行早期干预,减少果树病虫害的发生、消除或降低农药的使用,为发展绿色无公害有机果品,提高苹果品质和产量创造了条件。     

融合注意力机制的药用植物文本命名实体识别

药用植物文本的命名实体识别对中医药领域的信息抽取和知识图谱构建起着重要作用。针对药用植物属性文本存在长序列语义稀疏的问题,提出一种基于注意力机制的双向长短时记忆网络(BiLSTM)和条件随机场(CRF)模型相结合的疾病实体识别方法（BiLSTM+ATT-CRF,BAC）。首先对药用植物属性文本进行预处理和半自动化标注构建数据集,并进行预训练得到低维词向量;然后将这些低维词向量输入BiLSTM网络中,得到双向语义依赖的特征向量;Attention层把注意力集中到与当前输出特征高度相关的信息上;最后通过条件随机场（CRF）算法获取最优的标签序列并解码输出。实验结果表明,BAC方法针对药用植物属性文本的长序列语义稀疏问题,疾病命名实体识别效果较传统方法更优。利用BAC方法训练好的模型从1680条文本句子中识别疾病命名实体,共抽取出1422个疾病实体。与药用植物名称进行匹配,共抽取出4316个药用植物治疗疾病的三元组数据。     

A Novel NanoPipetting Solution for the Development of High Quality BioChip Arrays for Diagnostic Applications

The dramatic progress in the analysis of human, animal and plant genomes as well as parallel developments such as the human cancer gene anatomy project have created an enormous demand for low-cost high throughput technologies for DNA and RNA analysis. Chip-based molecular techniques- if available in satisfactory quality for diagnostic applications- will enable major analytical issues in health care such as predisposition, cancer, infectious diseases and others to be addressed.