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disease detection,severity prediction,and crop loss estimation in MaizeCrop using deep learning

Artificial Intelligence in Agriculture 2022年第1期6卷 276-291页

作者： Nidhi Kundu Geeta Rani Vijaypal Singh Dhaka Kalpit Gupta Siddaiah Chandra Nayaka Eugenio Vocaturo Ester Zumpano Department of Computer and Communication Engineering Manipal University JaipurJaipurIndia ICAR DOS in Biotechnology University of Mysore ManasagangotriMysore 570005India DIMES(Department of Computer Engineering ModelingElectronics and Systems)University of CalabriaItaly Nanotec Italian National Research Council87036 RendeCSItaly

The increasing gap between the demand and productivity of maize crop is a point of concern for the food industry,and ***'susceptibility to diseases such as Turcicum Leaf Blight,and Rust is a major cause for reducing its *** detection,and classification of these diseases,calculation of disease severity,and crop loss estimation is a time-consuming ***,it requires expertise in disease ***,there is a need to find an alternative for automatic disease detection,severity prediction,and crop loss *** promising results of machine learning,and deep learning algorithms in pattern recognition,object detection,and data analysis motivate researchers to employ these techniques for disease detection,classification,and crop loss estimation in maize *** research works available in literature,have proven their potential in automatic disease detection using machine learning,and deep learning ***,there is a lack none of these works a reliable and real-life labelled dataset for training these ***,none of the existing works focus on severity prediction,and crop loss *** authors in this manuscript collect the real-life dataset labelled by plant *** propose a deep learning-based framework for pre-processing of dataset,automatic disease detection,severity prediction,and crop loss *** uses the K-Means clustering algorithm for extracting the region of ***,they employ the customized deep learning model‘MaizeNet’for disease detection,severity prediction,and crop loss *** model reports the highest accuracy of 98.50%.Also,the authors perform the feature visualization using the ***,the proposed model is integrated with a web application to provide a userfriendly *** efficacy of the model in extracting the relevant features,a smaller number of parameters,low training time,high accuracy favors its importance as an assisting tool for plant pathology experts.T

关键词： disease detection Crop loss Severity Deep learning Maize

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Grape leaf disease detection based on attention mechanisms

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International Journal of Agricultural and Biological Engineering 2022年第5期15卷 205-212页

作者： Wenjuan Guo Quan Feng Xiangzhou Li Sen Yang Junqi Yang College of Mechanical and Electrical Engineering Gansu Agricultural UniversityLanzhou 730070China School of Cyber Security Gansu University of Political Science and LawLanzhou 730070China

Prevention and control of grape diseases is the key measure to ensure grape *** order to improve the precision of grape leaf disease detection,in this study,Squeeze-and-Excitation Networks(SE),Efficient Channel Attention(ECA),and Convolutional Block Attention Module(CBAM)attention mechanisms were introduced into Faster Region-based Convolutional Neural Networks(R-CNN),YOLOx,and single shot multibox detector(SSD),to enhance important features and weaken unrelated features and ensure the real-time performance of the model in improving its detection *** study showed that Faster R-CNN,YOLOx,and SSD models based on different attention mechanisms effectively enhanced the detection precision and operation speed of the models by slightly enhancing *** models among the three types of models were selected for comparison,and results showed that Faster R-CNN+SE had lower detection precision,YOLOx+ECA required the least parameters with the highest detection precision,and SSD+SE showed optimal real-time performance with relatively high detection *** study solved the problem of difficulty in grape leaf disease detection and provided a reference for the analysis of grape diseases and symptoms in automated agricultural production.

关键词： disease detection Faster R-CNN YOLOx SSD attention mechanism

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Crop diagnostic system:A robust disease detection and management system for leafy green crops grown in an aquaponics facility

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Artificial Intelligence in Agriculture 2023年第4期10卷 1-12页

作者： R.Abbasi P.Martinez R.Ahmad Aquaponics 4.0 Learning Factory(AllFactory) Department of Mechanical EngineeringUniversity of Alberta9211116 St.EdmontonAB T6G 2G8Canada Mechanical and Construction Engineering Department Northumbria UniversityNewcastle Upon Tyne NE77YTUK

Crops grown on aquaponics farms are susceptible to various diseases or biotic stresses during their growth cycle,just like traditional *** early detection of diseases is crucial to witnessing the efficiency and progress of the aquaponics *** combines recirculating aquaculture and soilless hydroponics methods and promises to ensure food security,reduce water scarcity,and eliminate carbon *** the large-scale imple-mentation of this farming technique,a unified system is needed that can detect crop diseases and support re-searchers and farmers in identifying potential causes and treatments at early *** study proposes an automatic crop diagnostic system for detecting biotic stresses and managing diseases in four leafy green crops,lettuce,basil,spinach,and parsley,grown in an aquaponics ***,a dataset comprising 2640 images is ***,a disease detection system is developed that works in three *** first phase is a crop clas-sification system that identifies the type of *** second phase is a disease identification system that determines the crop's health *** final phase is a disease detection system that localizes and detects the diseased and healthy spots in leaves and categorizes the *** proposed approach has shown promising results with accuracy in each of the three phases,reaching 95.83%,94.13%,and 82.13%,*** final dis-ease detection system is then integrated with an ontology model through a cloud-based *** ontol-ogy model contains domain knowledge related to crop pathology,particularly causes and treatments of different diseases of the studied leafy green crops,which can be automatically extracted upon disease detection allowing agricultural practitioners to take precautionary *** proposed application finds its significance as a de-cision support system that can automate aquaponics facility health monitoring and assist agricultural practi-tione

关键词： Computer vision Deep learning disease detection Leafy crops Aquaponics Digital farming

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LeafSpotNet:A deep learning framework for detecting leaf spot disease in jasmine plants

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Artificial Intelligence in Agriculture 2024年第2期12卷 1-18页

作者： Shwetha V Arnav Bhagwat Vijaya Laxmi Department of Electrical and Electronics Engineering Manipal Institute of TechnologyManipalManipal Academy of Higher EducationManipal 576104KarnatakaIndia

Leaf blight spot disease,caused by bacteria and fungi,poses a threat to plant health,leading to leaf discoloration and diminished agricultural *** response,we present a MobileNetV3 based classifier designed for the Jasmine plant,leveraging lightweight Convolutional Neural Networks(CNNs)to accurately identify disease *** model integrates depth wise convolution layers and max pool layers for enhanced feature extraction,focusing on crucial low level features indicative of the *** preprocessing techniques,including data augmentation with Conditional GAN and Particle Swarm Optimization for feature selection,the classifier achieves robust *** on curated datasets demonstrates an outstanding 97%training accuracy,highlighting its *** world testing with diverse conditions,such as extreme camera angles and varied lighting,attests to the model's resilience,yielding test accuracies between 94%and 96%.The dataset's tailored design for CNN based classification ensures result ***,the model's lightweight classification,marked by fast computation time and reduced size,positions it as an efficient solution for real time *** comprehensive approach underscores the proposed classifier's significance in addressing leaf blight spot dis-ease challenges in commercial crops.

关键词： CNN detection Leaf CGAN Augmentation Classification Swarm optimization disease detection Tomato leaf Casava leaf

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Maternal and infant microbiome:next-generation indicators and targets for intergenerational health and nutrition care

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Protein & Cell 2023年第11期14卷 807-823页

作者： Shengtao Gao Jinfeng Wang College of Food Science&Nutritional Engineering China Agricultural UniversityBeijing 100083China

Microbes are commonly sensitive to shifts in the physiological and pathological state of their hosts,including mothers and *** this perspective,the microbiome may be a good indicator for diseases during pregnancy and has the potential to be used for perinatal health *** is embodied in the application of microbiome from multi body sites for auxiliary diagnosis,early prediction,prolonged monitoring,and retrospective diagnosis of pregnancy and infant complications,as well as nutrition management and health products developments of mothers and *** we summarized the progress in these areas and explained that the microbiome of different body sites is sensitive to different diseases and their microbial biomarkers may overlap between each other,thus we need to make a diagnosis prudently for those *** on the microbiome variances and additional anthropometric and physical data,individualized responses of mothers and neonates to meals and probiotics/prebiotics were predictable,which is of importance for precise nutrition and probiotics/prebiotics managements and *** a great deal of encouraging performance was manifested in previous studies,the efficacy could be further improved by combining multi-aspect data such as multi-omics and time series analysis in the *** review reconceptualizes maternal and infant health from a microbiome perspective,and the knowledge in it may inspire the development of new options for the prevention and treatment of adverse pregnancy outcomes and bring a leap forward in perinatal health care.

关键词： newborn microbiome disease detection pregnancy management health care products

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Mango Leaf disease Identification Using Fully Resolution Convolutional Network

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Computers, Materials & Continua 2021年第12期69卷 3581-3601页

作者： Rabia Saleem Jamal Hussain Shah Muhammad Sharif Ghulam Jillani Ansari COMSATS University Islamabad Wah Campus47040Pakistan University of Education Lahore54000Pakistan

Due to the high demand for mango and being the king of all fruits,it is the need of the hour to curb its diseases to fetch high *** leaf disease segmentation and identification are still a challenge due to variations in *** segmentation of the disease is the key prerequisite for any computer-aided system to recognize the diseases,i.e.,Anthracnose,apicalnecrosis,etc.,of a mango plant *** solve this issue,we proposed a CNN based Fully-convolutional-network(FrCNnet)model for the segmentation of the diseased part of the mango *** proposed FrCNnet directly learns the features of each pixel of the input data after applying some preprocessing *** evaluated the proposed FrCNnet on the real-time dataset provided by the mango research institute,Multan,*** evaluate the proposed model results,we compared the segmentation performance with the available state-of-the-art models,i.e.,Vgg16,Vgg-19,and ***,the proposed model’s segmentation accuracy is 99.2%with a false negative rate(FNR)of 0.8%,which is much higher than the other *** have concluded that by using a FrCNnet,the input image could learn better features that are more prominent and much specific,resulting in an improved and better segmentation performance and diseases’***,an automated approach helps pathologists and mango growers detect and identify those diseases.

关键词： Mango plant leaf disease detection CNN FrCNnet segmentation

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Rice disease Diagnosis System (RDDS)

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Computers, Materials & Continua 2022年第10期73卷 1895-1914页

作者： Sandhya Venu Vasantha Shirina Samreen Yelganamoni Lakshmi Aparna Maturi Venkata Subba Rao(MVSR)Engineering College HyderabadTelanganaIndia College of Computer and Information Sciences Majmaah UniversityKSA Indian Institute of Technology Indian School of Mines(ISM)DhanbadJharkhandIndia

Hitherto,Rice(Oryza Sativa)has been one of the most demanding food crops in the world,cultivated in larger quantities,but loss in both quality and quantity of yield due to abiotic and biotic stresses has become a major *** cultivation,the crops are most prone to biotic stresses such as bacterial,viral,fungal diseases and *** stresses can drastically damage the *** and erroneously recognized crop diseases can increase fertilizers costs and major yield loss which results in high financial loss and adverse impact on nation’s *** proven methods of molecular biology can provide accurate detection of pathogenic factors,but these methods are not accessible to the majority of the farmers,needs high costs or resources,and require domain knowledge to ***’s field inspection report provides precise crop diagnosis but continuous field inspection over the remotely placed agriculture fields is not ***,cost effective approach for early detection of diseases can help farmers to take necessary steps in time to boost up the crop *** agriculture makes use of decision support systems built using Machine Learning(ML)or Deep Learning(DL)approaches to cut down heavy *** crop diagnosis process can be automated with the involvement of Computer Vision,Image Processing and Deep Learning(DL)based methods for more precise prediction in less cost and *** research shows that more accurate image classification can be implemented using Deep Learning based Convolutional Neural Network(CNN)*** this paper,we have proposed an automated Rice disease Diagnosis System(RDDS)for timely,more accurate and detailed crop disease diagnosis,which consisting of two modules,they are Leaf disease Identification(LDI)module for disease detection and Infection Intensity Estimation(IIE)module for disease severity *** LDI module is based on the proposed novel RDD_CNN model that classified the eight most harmful a

关键词： Rice disease detection CNN Machine Learning Deep Learning image processing

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Recent advances in the use of the CRISPR-Cas system for the detection of infectious pathogens

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Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2022年第11期23卷 881-898页

作者： Hongdan GAO Zifang SHANG Siew Yin CHAN Dongli MA Institute of Pediatrics Shenzhen Children's HospitalShenzhen 518026China CAS Key Laboratory of Pathogenic Microbiology and Immunology Institute of MicrobiologyChinese Academy of SciencesBeijing 100101China Frontiers Science Center for Flexible Electronics(FSCFE) Xi'an Institute of Flexible Electronics(IFE)&Xi'an Institute of Biomedical Materials and Engineering(IBME)Northwestern Polytechnical University(NPU)Xi'an 710072China

Infectious diseases cause great economic loss and individual and even social *** detection methods lack sensitivity and specificity,have a poor turnaround time,and are dependent on expensive *** recent years,the clustered regularly interspaced short palindromic repeats(CRISPR)-CRISPR-associated protein(Cas)system has been widely used in the detection of pathogens that cause infectious diseases owing to its high specificity,sensitivity,and speed,and good *** this review,we discuss the discovery and development of the CRISPR-Cas system,summarize related analysis and interpretation methods,and discuss the existing applications of CRISPR-based detection of infectious pathogens using Cas *** conclude the challenges and prospects of the CRISPR-Cas system in the detection of pathogens.

关键词： CRISPR-Cas Diagnosis disease detection Infectious disease Pathogen

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Estimation of citrus canker lesion size using hyperspectral reflectance imaging

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International Journal of Agricultural and Biological Engineering 2013年第3期6卷 41-51页

作者： Nikhil P.Niphadkar Thomas F.Burks Jianwei Qin Mark A.Ritenour ATW Automation Inc. DaytonOH 45402USA Department of Agricultural and Biological Engineering University of FloridaGainesvilleFL 32611USA U.S.Department of Agriculture BeltsvilleMD 20705USA Department of Horticultural Sciences University of FloridaGainesvilleFL 34945USA

The Citrus industry has need for effective approaches to remove fruit with canker before they are shipped to selective international market such as the European *** research aims to determine the detectable size limit for cankerous lesions using hyperspectral imaging *** developed multispectral algorithms using visible to near-infrared wavelengths,were used to segregate cankerous citrus fruits from other peel conditions(normal,greasy spot,insect damage,melanose,scab and wind scar).However,this previous work did not consider lesion size.A two-band ratio method with a simple threshold based classifier(ratio of reflectance at wavelengths 834 nm and 729 nm),which gave maximum overall classification accuracy of 95.7%,was selected for lesion size estimation in this *** smallest size of cankerous lesion detected in terms of equivalent diameter was 1.66 *** effect of variation of threshold values and number of erosion cycles(applying morphological erosion multiple times to the image)on estimation of smallest detectable lesion was *** was found that small threshold values gave better canker classification accuracies,while exhibiting a lower overall classification ***,higher threshold values portrayed the opposite *** threshold value of 1.275 gave the optimum tradeoff between canker classification accuracy,overall classification accuracy and minimal lesion size *** the number of erosion cycles reduced detection rates of smaller canker lesions,leading to the conclusion that a single erosion cycle gave the best size estimation *** erosion kernel of the size 3 mm×3 mm was used during the exploration.

关键词： citrus canker lesion size disease detection hyperspectral reflectance imaging image classification multispectral algorithm size detection limit

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Hybrid Convolutional Neural Network for Plant diseases Prediction

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Intelligent Automation & Soft Computing 2023年第5期36卷 2393-2409页

作者： S.Poornima N.Sripriya Adel Fahad Alrasheedi S.S.Askar Mohamed Abouhawwash Department of Information Technology Sri Sivasubramaniya Nadar College of EngineeringChennai603110India Department of Statistics and Operations Research College of ScienceKing Saud UniversityRiyadh11451Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Plant diseases prediction is the essential technique to prevent the yield loss and gain high production of agricultural *** monitoring of plant health continuously and detecting the diseases is a signiﬁcant for sustainable *** system to monitor the diseases in plant is time consuming and report a lot of *** is high demand for technology to detect the plant dis-eases *** image processing approach and deep learning approach are highly invited in detection of plant *** diseases like late blight,bacterial spots,spots on Septoria leaf and yellow leaf curved are widely found in *** are the main reasons to affects the plants life and *** identify the diseases earliest,our research presents the hybrid method by com-bining the region based convolutional neural network(RCNN)and region based fully convolutional networks(RFCN)for classifying the *** the leaf images of plants are collected and preprocessed to remove noisy data in *** data normalization,augmentation and removal of background noises are *** images are divided as testing and training,training images are fed as input to deep learning ***,we identify the region of interest(RoI)by using selective *** every region,feature of convolutional neural network(CNN)is extracted independently for further classiﬁ*** plants such as tomato,potato and bell pepper are taken for this *** plant input image is analyzed and classify as healthy plant or unhealthy *** the image is detected as unhealthy,then type of diseases the plant is affected will be *** proposed technique achieves 98.5%of accuracy in predicting the plant diseases.

关键词： disease detection people detection image classiﬁcation deep learning region based convolutional neural network

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