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Impact of dataset on the study of crop disease image recognition

International Journal of Agricultural and Biological Engineering 2022年第5期15卷 181-186页

作者： Yuan Yuan Lei Chen Yuchen Ren Shimei Wang Yin Li Institute of Intelligent Machines Hefei Institutes of Physical ScienceChinese Academy of SciencesHefei 230031China Rice Research Institute Anhui Academy of Agricultural SciencesHefei 230001China Guangzhou Institute of Software Application Technology Guangzhou 511458China

Datasets are very important in image recognition research based on machine learning *** particular,advanced methods such as deep learning and transfer learning are more dependent on datasets used for training *** quality of datasets directly affects the final effect of these *** the research of crop disease image recognition,due to the complication of the agricultural environment and the variety of crops,datasets are scarce at ***,more and more researches adopt methods based on transfer learning,which can make up for the lack of data in the target domain with the help of other *** these methods,the selection of auxiliary domain datasets has great impact on the modeling effect of target *** order to clarify the impact of datasets on the research of crop disease image recognition,this study used different deep neural network frameworks to study and compare the effects of different datasets in crop disease image recognition based on transfer *** selected datasets include PlantVillage and Image Database for Agricultural diseases and Pests Research(IDADP),which have been widely used in recent *** the selected deep neural network frameworks include ResNet50,InceptionV3,and *** the method of this study,the datasets are preprocessed first,such as data *** dividing the auxiliary domain and the target domain,the selected deep neural network frameworks are used to pre-train the model on the auxiliary domain ***,the parameter-based transfer learning method was used to construct the corresponding crop disease recognition model in the *** the experiments,multiple different datasets and different models were tested and *** results show that when the test set samples and training sample scenarios are consistent,the recognition accuracy of different network frameworks on multiple test sets is generally *** the scenarios of test set samples and training samp

关键词： crop diseases datasets transfer learning deep learning image recognition

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Roles of small RNAs in crop disease resistance

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Stress Biology 2021年第1期1卷 65-79页

作者： Jun Tang Xueting Gu Junzhong Liu Zuhua He National Key Laboratory of Plant Molecular Genetics CAS Center for Excellence in Molecular Plant SciencesInstitute of Plant Physiology&EcologyChinese Academy of SciencesShanghai 200032China University of the Chinese Academy of Sciences Beijing 100049China State Key Laboratory of Conservation and Utilization of Bio-Resources in Yunnan and Center for Life Sciences School of Life SciencesYunnan UniversityKunmingChina.

Small RNAs(sRNAs)are a class of short,non-coding regulatory RNAs that have emerged as critical components of defense regulatory networks across plant *** sRNA-based technologies,such as host-induced gene silencing(HIGS),spray-induced gene silencing(SIGS),virus-induced gene silencing(VIGS),artificial microRNA(amiRNA)and synthetic trans-acting siRNA(syn-tasiRNA)-mediated RNA interference(RNAi),have been developed as disease control strategies in both monocot and dicot plants,particularly in *** review aims to highlight our current understanding of the roles of sRNAs including miRNAs,heterochromatic siRNAs(hc-siRNAs),phased,secondary siRNAs(phasiRNAs)and natural antisense siRNAs(nat-siRNAs)in disease resistance,and sRNAs-mediated trade-offs between defense and growth in *** particular,we focus on the diverse functions of sRNAs in defense responses to bacterial and fungal pathogens,oomycete and virus in ***,we highlight the application of sRNA-based technologies in protecting crops from *** research perspectives are proposed to develop new sRNAs-based efficient strategies to breed non-genetically modified(GMO),diseasetolerant crops for sustainable agriculture.

关键词： Small RNAs HIGS crop diseases RNAi RNAi-based technology

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Deep Learning-Based Trees Disease Recognition and Classification Using Hyperspectral Data

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Computers, Materials & Continua 2023年第10期77卷 681-697页

作者： Uzair Aslam Bhatti Sibghat Ullah Bazai Shumaila Hussain Shariqa Fakhar Chin Soon Ku Shah Marjan Por Lip Yee Liu Jing College of Information and Communication Engineering Hainan UniversityHaikou570228China Department of Computer Engineering Balochistan University of Information TechnologyEngineeringand Management Sciences(BUITEMS)QuettaPakistan Department of Computer Science Sardar Bahadur Khan Women’s UniversityQuettaPakistan Department of Computer Science Universiti Tunku Abdul RahmanKampar31900Malaysia Department of Software Engineering Balochistan University of Information TechnologyEngineeringand Management Sciences(BUITEMS)QuettaPakistan Faculty of Computer Science and Information Technology University of MalayaKuala Lumpur50603Malaysia

crop diseases have a significant impact on plant growth and can lead to reduced *** methods of disease detection rely on the expertise of plant protection experts,which can be subjective and dependent on individual experience and *** address this,the use of digital image recognition technology and deep learning algorithms has emerged as a promising approach for automating plant disease *** this paper,we propose a novel approach that utilizes a convolutional neural network(CNN)model in conjunction with Inception v3 to identify plant leaf *** research focuses on developing a mobile application that leverages this mechanism to identify diseases in plants and provide recommendations for overcoming specific *** models were trained using a dataset consisting of 80,848 images representing 21 different plant leaves categorized into 60 distinct *** rigorous training and evaluation,the proposed system achieved an impressive accuracy rate of 99%.This mobile application serves as a convenient and valuable advisory tool,providing early detection and guidance in real agricultural *** significance of this research lies in its potential to revolutionize plant disease detection and management *** automating the identification process through deep learning algorithms,the proposed system eliminates the subjective nature of expert-based diagnosis and reduces dependence on individual *** integration of mobile technology further enhances accessibility and enables farmers and agricultural practitioners to swiftly and accurately identify diseases in their crops.

关键词： Plant disease Inception v3 CNN crop diseases

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