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ANALYSIS OF FRUIT IMAGES WITH DEEP LEARNING: A SYSTEMATIC LITERATURE REVIEW AND FUTURE DIRECTIONS

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dc.creatorPEDRO GERÓNIMO CAMPOS SOTO
dc.creatorLUIS ALBERTO ROJAS PINO
dc.creatorCRISTHIAN ALEJANDRO AGUILERA CARRASCO
dc.date2023
dc.date.accessioned2025-01-10T15:47:19Z
dc.date.available2025-01-10T15:47:19Z
dc.date.issued2023
dc.description.abstractTHE APPLICATION OF DEEP LEARNING MODELS IN FRUIT ANALYSIS HAS GARNERED SIGNIFICANT ATTENTION DUE TO ITS POTENTIAL TO REVOLUTIONIZE THE AGRICULTURAL SECTOR AND ENHANCE CROP MONITORING. THIS PAPER PRESENTS A COMPREHENSIVE REVIEW OF RECENT RESEARCH EFFORTS IN FRUIT ANALYSIS USING DEEP LEARNING TECHNIQUES. THE STUDY DELVES INTO MODEL SELECTION, DATASET CHARACTERISTICS, EVALUATION METRICS, CHALLENGES, AND FUTURE DIRECTIONS IN THIS DOMAIN. VARIOUS MODEL ARCHITECTURES, INCLUDING CLASSICAL CONVOLUTIONAL NEURAL NETWORKS (CNNS) AND ADVANCED DETECTION MODELS LIKE R-CNN AND YOLO, HAVE BEEN EXPLORED FOR TASKS RANGING FROM FRUIT CLASSIFICATION TO DETECTION. EVALUATION METRICS SUCH AS PRECISION, RECALL, F1-SCORE, AND MEAN AVERAGE PRECISION (MAP) HAVE BEEN COMMONLY USED TO ASSESS MODEL PERFORMANCE. CHALLENGES, INCLUDING DATA SCARCITY, LABELING COMPLEXITIES, VARIATIONS IN FRUIT CHARACTERISTICS, AND COMPUTATIONAL EFFICIENCY, HAVE BEEN IDENTIFIED AND DISCUSSED. THE PAPER ALSO PRESENTS AN OVERVIEW OF AVAILABLE DATASETS, ENCOMPASSING BOTH PROPRIETARY AND PUBLICLY ACCESSIBLE SOURCES. FUTURE RESEARCH DIRECTIONS INVOLVE EXPLORING ENHANCED DATA AUGMENTATION, MULTI-MODAL INTEGRATION, KNOWLEDGE TRANSFER ACROSS SPECIES, ROBUSTNESS IN DYNAMIC ENVIRONMENTS, IMPROVED COMPUTATIONAL EFFICIENCY, AND PRACTICAL INTEGRATION OF MODELS INTO REAL-WORLD AGRICULTURAL SYSTEMS. THIS REVIEW PROVIDES VALUABLE INSIGHTS FOR RESEARCHERS AND PRACTITIONERS AIMING TO LEVERAGE DEEP LEARNING FOR FRUIT ANALYSIS IN THE PURSUIT OF SUSTAINABLE AGRICULTURE AND FOOD PRODUCTION.
dc.formatapplication/pdf
dc.identifier.doi10.1109/ACCESS.2023.3345789
dc.identifier.issn2169-3536
dc.identifier.issn2169-3536
dc.identifier.urihttps://repositorio.ubiobio.cl/handle/123456789/13654
dc.languagespa
dc.publisherIEEE ACCESS
dc.relation.uri10.1109/ACCESS.2023.3345789
dc.rightsPUBLICADA
dc.subjectvisual transformers.
dc.subjectsystematic literature review
dc.subjectdeep learning models
dc.subjectconvolutional neural networks
dc.subjectAgriculture technology
dc.titleANALYSIS OF FRUIT IMAGES WITH DEEP LEARNING: A SYSTEMATIC LITERATURE REVIEW AND FUTURE DIRECTIONS
dc.typeARTÍCULO
dspace.entity.typePublication
ubb.EstadoPUBLICADA
ubb.Otra ReparticionCOORDINACION UNIDAD DE ASEGURAMIENTO DE LA CALIDAD
ubb.Otra ReparticionDEPARTAMENTO DE CIENCIAS DE LA COMPUTACION Y TECNOLOGIA DE LA INFORMACION.
ubb.Otra ReparticionDEPARTAMENTO DE INGENIERIA ELECTRICA Y ELECTRONICA
ubb.SedeCONCEPCIÓN
ubb.SedeCHILLÁN
ubb.SedeCONCEPCIÓN
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