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Artificial Intelligence Enabled apple leaf Disease Classification for Precision Agriculture

Computers, Materials & Continua 2022年第3期70卷 6223-6238页

作者： Fahd N.Al-Wesabi Amani Abdulrahman Albraikan Anwer Mustafa Hilal Majdy M.Eltahir Manar Ahmed Hamza Abu Sarwar Zamani Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversityYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Precision agriculture enables the recent technological advancements in farming sector to observe,measure,and analyze the requirements of individual fields and *** recent developments of computer vision and artificial intelligence(AI)techniques find a way for effective detection of plants,diseases,weeds,pests,*** the other hand,the detection of plant diseases,particularly apple leaf diseases using AI techniques can improve productivity and reduce crop ***,earlier and precise apple leaf disease detection can minimize the spread of the *** works make use of traditional image processing techniques which cannot assure high detection rate on apple leaf *** this motivation,this paper introduces a novel AI enabled apple leaf disease classification(AIE-ALDC)technique for precision *** proposed AIE-ALDC technique involves orientation based data augmentation and Gaussian filtering based noise removal *** addition,the AIE-ALDC technique includes a Capsule Network(CapsNet)based feature extractor to generate a helpful set of feature ***,water wave optimization(WWO)technique is employed as a hyperparameter optimizer of the CapsNet ***,bidirectional long short term memory(BiLSTM)model is used as a classifier to determine the appropriate class labels of the apple leaf *** design of AIE-ALDC technique incorporating theWWO based CapsNetmodel with BiLSTM classifier shows the novelty of the *** range of experiments was performed to showcase the supremacy of the AIE-ALDC *** experimental results demonstrate the promising performance of the AIEALDC technique over the recent state of art methods.

关键词： Artificial intelligence apple leaf plant disease precision agriculture deep learning data augmentation

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Estimating the severity of apple mosaic disease with hyperspectral images

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International Journal of Agricultural and Biological Engineering 2019年第4期12卷 148-153页

作者： Songtao Ban Minglu Tian Qingrui Chang College of Natural Resource and Environment Northwest A&F UniversityYangling 712100China Agricultural Information Institute of Science and Technology Shanghai Academy of Agricultural SciencesShanghai 201403China Shanghai Engineering Research Centre for Digital Agriculture Shanghai 201403China

Soil Plant Analysis Development(SPAD)Chlorophyll Meter reading was used to effectively characterize chlorophyll content,which is an important indicator of the health status of plant *** this study,the hyperspectral images of apple leaves infected by apple mosaic virus(ApMV)were captured,and their SPAD values were *** spectral reflectance of leaves with varying degree infection of disease is significantly *** particular,the reflectance in visible wavebands of leaves with a more serious infection was higher than that of leaves with a less severe *** hyperspectral vegetation indices were highly correlated with the SPAD values of apple leaves(correlation coefficient>0.9).Models were established to estimate apple foliar SPAD values based on these vegetation *** the models,the multivariate regression model with partial least square regression(PLSR)method achieved the highest *** SPAD value of a whole apple leaf was calculated from its SPAD distribution image and used as a quantitative index to represent the health status of an apple ***,the SPAD value of a whole apple leaf could also be estimated rapidly and accurately by extracting the spectral average value of the whole leaf using a simple *** can be used as a rapid detection method of SPAD values of apple leaves to monitor and describe the health conditions of apple leaves quantitatively.

关键词： hyperspectral image apple leaf mosaic disease SPAD plant health detection

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苹果斑点落叶病菌与其宿主互作机制的蛋白质组学解析(英文)

苹果斑点落叶病菌与其宿主互作机制的蛋白质组学解析(英文)

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第三届全国植物蛋白质组学大会

作者：张彩霞丛佩华农业部果树种质资源利用重点开放实验室中国农业科学院果树研究所

Previous studies on the interaction between plants and pathogenic microorganisms were focused largely on some model plants and pathogens,while relatively few have been used in apple and its *** that apple alternaria blotch is the usual diseases influencing apple production,and that the previous studies were mostly around its pathogenic mechanism,further studies about the interaction mechanism between the pathogen and its host, particularly some host self-defense response mechanisms which closely related to the anti-disease properties of apple should be *** this project,the interaction system between Alternaria mali and its host will be involved to investigate the host defense mechanism induced by pathogen,which is expected to provide guidance for the future breeding study on new varieties of apple with the *** this aim,we will choose the high throughput proteomic approach,associated with the identification of mass spectrometry(MS),to investigate the interaction system between Alternaria mali and apple *** we hope to identify differentially expressed proteins related to resistance involved in the interaction system,and to aim at understanding the molecular mechanism of plant-pathogen *** project has many virtues to offer,such as providing clues to a comprehensive understanding of the molecular mechanism of apple resist Alternaria mali,accelerating the process of apple molecular breeding, and also providing some theoretical basis or technical reference for future genetic improvement study about high-quality disease-resistant apple varieties.

关键词： Alternaria mali apple leaf Interaction mechanism Proteome analysis

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