Role of copper chelating agents: between old applications and new perspectives in neuroscience

The role of copper element has been an increasingly relevant topic in recent years in the fields of human and animal health, for both the study of new drugs and innovative food and feed supplements. This metal plays an important role in the central nervous system, where it is associated with glutamatergic signaling, and it is widely involved in inflammatory processes. Thus, diseases involving copper(Ⅱ) dyshomeostasis often have neurological symptoms, as exemplified by Alzheimer's and other diseases(such as Parkinson's and Wilson's diseases). Moreover, imbalanced copper ion concentrations have also been associated with diabetes and certain types of cancer, including glioma. In this paper, we propose a comprehensive overview of recent results that show the importance of these metal ions in several pathologies, mainly Alzheimer's disease, through the lens of the development and use of copper chelators as research compounds and potential therapeutics if included in multi-target hybrid drugs. Seeing how copper homeostasis is important for the well-being of animals as well as humans, we shortly describe the state of the art regarding the effects of copper and its chelators in agriculture, livestock rearing, and aquaculture, as ingredients for the formulation of feed supplements as well as to prevent the effects of pollution on animal productions.

Image Recognition of Citrus Diseases Based on Deep Learning

In recent years,with the development of machine learning and deep learning,it is possible to identify and even control crop diseases by using electronic devices instead of manual observation.In this paper,an image recognition method of citrus diseases based on deep learning is proposed.We built a citrus image dataset including six common citrus diseases.The deep learning network is used to train and learn these images,which can effectively identify and classify crop diseases.In the experiment,we use MobileNetV2 model as the primary network and compare it with other network models in the aspect of speed,model size,accuracy.Results show that our method reduces the prediction time consumption and model size while keeping a good classification accuracy.Finally,we discuss the significance of using MobileNetV2 to identify and classify agricultural diseases in mobile terminal,and put forward relevant suggestions.

Plant growth-promoting rhizobacteria(PGPR)and its mechanisms against plant diseases for sustainable agriculture and better productivity

Plant growth-promoting rhizobacteria(PGPR)are specialized bacterial communities inhabiting the root rhizosphere and the secretion of root exudates helps to,regulate the microbial dynamics and their interactions with the plants.These bacteria viz.,Agrobacterium,Arthobacter,Azospirillum,Bacillus,Burkholderia,Flavobacterium,Pseudomonas,Rhizobium,etc.,play important role in plant growth promotion.In addition,such symbiotic associations of PGPRs in the rhizospheric region also confer protection against several diseases caused by bacterial,fungal and viral pathogens.The biocontrol mechanism utilized by PGPR includes direct and indirect mechanisms direct PGPR mechanisms include the production of antibiotic,siderophore,and hydrolytic enzymes,competition for space and nutrients,and quorum sensing whereas,indirect mechanisms include rhizomicrobiome regulation via.secretion of root exudates,phytostimulation through the release of phytohormones viz.,auxin,cytokinin,gibberellic acid,1-aminocyclopropane-1-carboxylate and induction of systemic resistance through expression of antioxidant defense enzymes viz.,phenylalanine ammonia lyase(PAL),peroxidase(PO),polyphenyloxidases(PPO),superoxide dismutase(SOD),chitinase andβ-glucanases.For the suppression of plant diseases potent bio inoculants can be developed by modulating the rhizomicrobiome through rhizospheric engineering.In addition,understandings of different strategies to improve PGPR strains,their competence,colonization efficiency,persistence and its future implications should also be taken into consideration.

Fusion of Region Extraction and Cross-Entropy SVM Models for Wheat Rust Diseases Classification

Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20%every year.The wheat rust diseases are identified either through experienced evaluators or computerassisted techniques.The experienced evaluators take time to identify the disease which is highly laborious and too costly.If wheat rust diseases are predicted at the development stages,then fungicides are sprayed earlier which helps to increase wheat yield quality.To solve the experienced evaluator issues,a combined region extraction and cross-entropy support vector machine(CE-SVM)model is proposed for wheat rust disease identification.In the proposed system,a total of 2300 secondary source images were augmented through flipping,cropping,and rotation techniques.The augmented images are preprocessed by histogram equalization.As a result,preprocessed images have been applied to region extraction convolutional neural networks(RCNN);Fast-RCNN,Faster-RCNN,and Mask-RCNN models for wheat plant patch extraction.Different layers of region extraction models construct a feature vector that is later passed to the CE-SVM model.As a result,the Gaussian kernel function in CE-SVM achieves high F1-score(88.43%)and accuracy(93.60%)for wheat stripe rust disease classification.

Advanced biosensing technologies for monitoring of agriculture pests and diseases:A review

The threat posed to crop production by pests and diseases is one of the key factors that could reduce global food security.Early detection is of critical importance to make accurate predictions,optimize control strategies and prevent crop losses.Recent technological advancements highlight the opportunity to revolutionize monitoring of pests and diseases.Biosensing methodologies offer potential solutions for real-time and automated monitoring,which allow advancements in early and accurate detection and thus support sustainable crop protection.Herein,advanced biosensing technologies for pests and diseases monitoring,including image-based technologies,electronic noses,and wearable sensing methods are presented.Besides,challenges and future perspectives for widespread adoption of these technologies are discussed.Moreover,we believe it is necessary to integrate technologies through interdisciplinary cooperation for further exploration,which may provide unlimited possibilities for innovations and applications of agriculture monitoring.

Feature Extraction and Classification of Plant Leaf Diseases Using Deep Learning Techniques

In India’s economy, agriculture has been the most significantcontributor. Despite the fact that agriculture’s contribution is decreasing asthe world’s population grows, it continues to be the most important sourceof employment with a little margin of difference. As a result, there is apressing need to pick up the pace in order to achieve competitive, productive,diverse, and long-term agriculture. Plant disease misinterpretations can resultin the incorrect application of pesticides, causing crop harm. As a result,early detection of infections is critical as well as cost-effective for farmers.To diagnose the disease at an earlier stage, appropriate segmentation of thediseased component from the leaf in an accurate manner is critical. However,due to the existence of noise in the digitally captured image, as well asvariations in backdrop, shape, and brightness in sick photographs, effectiverecognition has become a difficult task. Leaf smut, Bacterial blight andBrown spot diseases are segmented and classified using diseased Apple (20),Cercospora (60), Rice (100), Grape (140), and wheat (180) leaf photos in thesuggested work. In addition, a superior segmentation technique for the ROIfrom sick leaves with living backdrop is presented here. Textural features of thesegmented ROI, such as 1st and 2nd order WPCA Features, are discoveredafter segmentation. This comprises 1st order textural features like kurtosis,skewness, mean and variance as well as 2nd procedure textural features likesmoothness, energy, correlation, homogeneity, contrast, and entropy. Finally,the segmented region of interest’s textural features is fed into four differentclassifiers, with the Enhanced Deep Convolutional Neural Network provingto be the most precise, with a 96.1% accuracy.

Advances in Measures of Reducing Chemical Pesticides to Control Plant Diseases

In order to provide the technological support for further implementing measures of reducing chemical pesticide to control plant diseases,the research progress on non-chemical pesticide measures to control plant diseases are reviewed from the aspects of agricultural control,botanical pesticide control and microbial pesticide control,and the development prospects are proposed,including accelerating innovative research on botani-cal pesticide control such as Chinese herb extracts,and screening microbial pesticides from valuable bio-control bacteria or plant endophyte metabolites for commercial production and utilization.

Towards Sustainable Agricultural Systems:A Lightweight Deep Learning Model for Plant Disease Detection

A country’s economy heavily depends on agricultural development.However,due to several plant diseases,crop growth rate and quality are highly suffered.Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information.Therefore,the agricultural management system is searching for an automatic early disease detection technique.To this end,an efficient and lightweight Deep Learning(DL)-based framework(E-GreenNet)is proposed to overcome these problems and precisely classify the various diseases.In the end-to-end architecture,a MobileNetV3Smallmodel is utilized as a backbone that generates refined,discriminative,and prominent features.Moreover,the proposed model is trained over the PlantVillage(PV),Data Repository of Leaf Images(DRLI),and a new Plant Composite(PC)dataset individually,and later on test samples,its actual performance is evaluated.After extensive experimental analysis,the proposed model obtained 1.00%,0.96%and 0.99%accuracies on all three included datasets.Moreover,the proposed method achieves better inference speed when compared with other State-Of-The-Art(SOTA)approaches.In addition,a comparative analysis is conducted where the proposed strategy shows tremendous discriminative scores as compared to the various pretrained models and other Machine Learning(ML)and DL methods.

The recent disciplinal progresses of agricultural entomology in China

In this paper, four recent advances and achievements of China in agricultural insect research, namely, on the genome of silkworm (Bombyx mori Linnaeus), on the geographical differentiation and regional migration of cotton bollworm (Helicoverpa armigera (Hübner)), on the standardized monitoring techniques for safety of honey bee (Apis mellifera Linnaeus) products, and on the virus transmission property of small brown planthopper (Laodelphax striatellus (Fallén)) as well as the interactions between vector and rice stripe virus (RSV), were reported. All of these researches are very important for controlling agricultural insect pests and the diseases they transmit, accelerating the molecular biological research of silkworm, and promoting the international trade of honey bee products. Most of these achievements mentioned above have got the national, provincial, ministerial or municipal awards on science and technology.

计算机视觉技术在农作物病虫害检测中的运用研究

现代农业发展更趋集约化、信息化,这为计算机视觉技术的运用提供了空间。以计算机视觉技术在农作物病虫害检测中的运用优势为切入点,在此基础上分析其具体运用方法,包括建设信息作业系统、强调信息采集、运用可视化技术、重视信息复用等内容。最后就该技术在农作物病虫害检测中的未来运用进行展望,一方面客观呈现技术优势、特点,另一方面为其发挥价值提供必要参考。

基于连续提示注入与指针网络的农业病害命名实体识别

针对农业病害领域命名实体识别过程中存在的预训练语言模型利用不充分、外部知识注入利用率低、嵌套命名实体识别率低的问题,本文提出基于连续提示注入和指针网络的命名实体识别模型CP-MRC(Continuous prompts for machine reading comprehension)。该模型引入BERT(Bidirectional encoder representation from transformers)预训练模型,通过冻结BERT模型原有参数,保留其在预训练阶段获取到的文本表征能力;为了增强模型对领域数据的适用性,在每层Transformer中插入连续可训练提示向量;为提高嵌套命名实体识别的准确性,采用指针网络抽取实体序列。在自建农业病害数据集上开展了对比实验,该数据集包含2933条文本语料,8个实体类型,共10414个实体。实验结果显示,CP-MRC模型的精确率、召回率、F1值达到83.55%、81.4%、82.4%,优于其他模型;在病原、作物两类嵌套实体的识别率较其他模型F1值提升3个百分点和13个百分点,嵌套实体识别率明显提升。本文提出的模型仅采用少量可训练参数仍然具备良好识别性能,为较大规模预训练模型在信息抽取任务上的应用提供了思路。

基于中医农业理论创制新型生物农药的实践探索

中医农业是利用中医药基本理论和方法解决现代农业生产过程中遇到的诸多实际问题,是实现生态农业与有机农业的重要途径.本文分析了4个中医农业探索案例.首先论述了从“清热解毒”类传统中药复方及其单味药材中挖掘苦参碱、小檗碱和大黄素甲醚等植物源农药,提出了传统“清热解毒”类中药复方及其单味中药是新型植物源农药创制的重要来源;接着对中药天仙子中挖掘具有新颖结构的天仙子新碱以创制新型植物免疫诱抗剂和植物生长调节剂进行了阐述,从药效物质基础的角度对铁线莲属植物发挥“整体”药效以挖掘新颖结构农用活性分子的潜力进行了归纳;最后对猕猴桃溃疡病防治技术中的“治未病”策略、“两前两后”关键防控技术以及复方生物菌剂创制进行了阐述.以上中医农业实践探索为更好地利用中医药理论和资源创制新型生物农药提供了思路和借鉴.

农业生态网在软枣猕猴桃生产中的应用研究

为了减少软枣猕猴桃栽培中的病、虫、鸟、草害及自然灾害,促进植株生长发育,减少药剂使用量,达到绿色安全生产、提高鲜果质量的目的,将3种类型的农业生态网应用于软枣猕猴桃品种栽培中。试验共设4个处理,每个处理3次重复。通过田间调查和果实内在指标测定,确定农业生态网在提高软枣猕猴桃鲜果果实品相、质量、产量中的效果,从而为农业生态网在丹东地区实现软枣猕猴桃大面积生产提供方法。

基于改进YOLOv5s的苹果病害检测技术研究

[目的]面对大规模的苹果园种植和管理,传统的果园巡检容易出现误检、少检的现象,并且传统的苹果病害检测模型参数量庞大难以实现移动端的轻量化部署,因此,设计一个高效并且轻量的苹果病害检测模型可以实现对苹果病害的有效预防与精准管理,从而改善苹果品质与增加果园经济收入。[方法]针对以上需要,提出了一种基于YOLOv5s的改进算法。以常见的炭疽病和褐斑病为主要研究对象,采集苹果表皮病害图像构建果园苹果病害数据集,通过Labelimg工具对图像进行标注与分类;引入GhostNet轻量化模块对主干特征提取网络进行替换,使用更小的参数量来捕获更多的特征信息,达到模型轻量化的效果,便于后期移动端的部署;引入SimAM无参注意力机制加强模型对通道和空间信息的同时关注,在不添加任何参数量的基础上对重要的病害特征赋予更高的优先级,提高模型的准确性;引入SIoU边界框回归损失函数来优化预测框对于目标病害的准确定位,通过重新定义角度惩罚度量帮助预测框快速定位到准确的轴,同时借助遗传算法优化θ的取值,实现提升模型训练和推理能力的效果。[结果]改进后的模型参数量和浮点运算数(FLOPs)比原始模型减少了30.2%和33.8%,在达到轻量化的基础上,mAP@0.5达到93.7%,mAP@0.5:0.95达到63.3%,分别优于YOLOv5s原始算法1.6%和0.7%。[结论]改进后的模型在实现轻量化的同时也达到了较好的检测性能,不仅实现了苹果病害的高效识别,也为其他农作物的病害检测提供了技术支持与参考依据。

融合RoBERTa-WWM和全局指针网络的农业病害实体关系联合抽取研究

针对实体和关系抽取过程中存在的一词多义、实体嵌套、三元组重叠的问题,本文提出了1种融合RoBERTa-WWM和全局指针网络的联合抽取模型RBGPL。该模型引入RoBERTa-WWM预训练模型,利用上下文的语境信息融合克服了不同语境下一词多义问题;采用全局指针网络Global pointer标注方式解决了实体嵌套问题;通过全局指针联合解码模型将三重抽取转变为五重提取,解决了三元组重叠问题。在自建农业病害数据集上,模型RBGPL的精确率、召回率、F1值达到76.23%,91.18%,83.04%,与其他联合抽取模型相对比F1值均取最优,有效地克服了一词多义问题和三元组重叠问题。此外,在病原(Pathogeny)和作物名称(Crop)2种易嵌套实体的F1值上提升了3%和18%,实体嵌套得到了显著缓解。本文方法提高了中文农业病害领域实体关系抽取性能,可为农业病害领域知识图谱的构建提供技术支持。

基于层级多标签的农业病虫害问句分类方法

随着信息化技术的快速发展,农户通过线上智能农业问答系统解决线下农业病虫害问题已成为趋势。问句分类在问答系统中发挥着至关重要的作用,其准确性直接决定了最终返回答案的正确性。传统的单标签文本分类模型难以直接准确捕捉到农业病虫害问句的确切意图,而且由于缺乏大规模公开的农业病虫害问句语料,使得现有研究具有一定的难度。为此,本文基于树状结构构建了一个农业病虫害问句层级分类体系,由问句模糊性向精确性逐层细化分类,旨在克服农业问句的语义复杂性;此外,引入对抗训练方法,通过构建对抗样本并将其与原始样本一同用于大规模语言模型的训练,以提高模型泛化能力,同时缓解了因语料不足而产生的问题。通过对真实问答语料库的实验验证,本文提出的方法能够提升农业病虫害问句的分类性能,可为农业病虫害自动问答系统提供有效的问句意图识别。

天然醛类化合物在农产品采后真菌病害防治中的应用进展

农产品种类众多、营养价值丰富,在人类日常饮食中不可或缺。然而,水果、蔬菜、食用菌和甘薯等农产品在采后易受病原真菌感染而发生腐烂变质,导致品质下降,贮藏期缩短。目前,主要通过化学杀菌剂来控制农产品采后腐烂,但化学杀菌剂的长期重复使用会导致病原菌抗药性增加、农药残留及环境污染等问题。天然醛类化合物具有广谱杀菌作用,尤其是针对农产品采后真菌病害的防治,具有绿色安全、快速有效等优点,在农产品保鲜中具有巨大的应用潜力。本文综述了天然醛类化合物的化学组成,对农产品采后病原真菌的抑制活性与抑菌机制,以及在农产品采后真菌病害防治中应用的研究进展,并对其在农产品采后保鲜中应用的未来趋势进行展望,以期为天然醛类化合物在农产品保鲜中更深层次的应用提供理论依据和新思路。

基于RoFormer预训练模型的指针网络农业病害命名实体识别

[目的/意义]针对实体嵌套、实体类型混淆等问题导致的农业病害命名实体识别(Named Entities Recognition,NER)准确率不高的情况,以PointerNet为基准模型,提出一种基于RoFormer预训练模型的指针网络农业病害NER方法RoFormer-PointerNet。[方法]采用RoFormer预训练模型对输入的文本进行向量化,利用其独特的旋转位置嵌入方法来捕捉位置信息,丰富字词特征信息,从而解决一词多义导致的类型易混淆的问题。使用指针网络进行解码,利用指针网络的首尾指针标注方式抽取句子中的所有实体,首尾指针标注方式可以解决实体抽取中存在的嵌套问题。[结果和讨论]自建农业病害数据集,数据集中包含2867条标注语料,共10282个实体。为验证RoFormer预训练模型在实体抽取上的优越性,采用Word2Vec、BERT、RoBERTa等多种向量化模型进行对比试验,RoFormer-PointerNet与其他模型相比,模型精确率、召回率、F1值均为最优,分别为87.49%,85.76%和86.62%。为验证RoFormer-PointerNet在缓解实体嵌套的优势,与使用最为广泛的双向长短期记忆神经网络(Bidirectional Long Short-Term Memory,BiLSTM)和条件随机场(Conditional Random Field,CRF)模型进行对比试验,RoFormer-PointerNet比RoFormer-BiLSTM模型、RoFormer-CRF模型和RoFormer-BiLSTM-CRF模型分别高出4.8%、5.67%和3.87%,证明用指针网络模型可以很好解决实体嵌套问题。最后验证RoFormer-PointerNet方法在农业病害数据集中的识别性能,针对病害症状、病害名称、防治方法等8类实体进行了识别实验,本方法识别的精确率、召回率和F1值分别为87.49%、85.76%和86.62%,为同类最优。[结论]本研究提出的方法能有效识别中文农业病害文本中的实体,识别效果优于其他模型。在解决实体抽取过程中的实体嵌套和类型混淆等问题方面具有一定优势。

融合注意力及多重知识迁移的茶叶病害轻量化检测方法

茶树病虫害是影响茶叶产量及品质的主要原因,精准检测茶叶病虫害种类是当前国内的热点问题之一。针对传统目标检测网络模型参数量大、精确率低导致工业部署困难的问题,建立茶叶病虫害表型图像数据集;对网络模型进行轻量化处理,优裁基于知识蒸馏的多重知识迁移训练模型;构建基于视觉注意力模块(CSA)的YOLOv5目标检测网络模型,优化茶叶病虫害检测方法。结果表明,添加视觉注意力模块(CSA)的YOLOv5目标检测模型与YOLOv5网络模型、添加传统注意力模块SE、CBAM模块的YOLOv5网络模型相比较,其平均准确率分别提高3.1%,1.1%,1%。对比蒸馏前学生模型,构建的模型最佳准确率提升4.1%,对比教师模型,模型容量降低5.4 MB,单帧图片推理时间下降35%。设计的网络模型在不损失准确率的情况下,降低网络计算的开销,可为资源受限的农业信息化领域边缘计算系统提供植入可能。

玉米主要病虫害防治技术

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