Cargando…

Editorial: Deep learning in crop diseases and insect pests

Detalles Bibliográficos
Autores principales:	Chen, Peng, Wang, Rujing, Yang, Po
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Frontiers Media S.A. 2023
Materias:	Plant Science
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9969843/ https://www.ncbi.nlm.nih.gov/pubmed/36860894 http://dx.doi.org/10.3389/fpls.2023.1145458

Ejemplares similares

Editorial: Disease and pest resistance in legume crops
por: Kaur, Sukhjiwan, et al.
Publicado: (2023)

Estimating insect pest density using the physiological index of crop leaf
por: Chen, Meng, et al.
Publicado: (2023)

Editorial: Advanced technologies of UAV application in crop pest, disease and weed control
por: Zhang, Ruirui, et al.
Publicado: (2023)

Editorial: Crop Traits for Defense against Pests and Disease: Durability, Breakdown and Future Prospects
por: Johnson, Scott N., et al.
Publicado: (2017)

Editorial: Convolutional neural networks and deep learning for crop improvement and production
por: Yang, Wanneng, et al.
Publicado: (2022)

The use of selenium for controlling plant fungal diseases and insect pests
por: Li, Qianru, et al.
Publicado: (2023)

Pesticidal Plant Extracts Improve Yield and Reduce Insect Pests on Legume Crops Without Harming Beneficial Arthropods
por: Tembo, Yolice, et al.
Publicado: (2018)

A lightweight model for efficient identification of plant diseases and pests based on deep learning
por: Guan, Hongliang, et al.
Publicado: (2023)

Management of Pest Insects and Plant Diseases by Non-Transformative RNAi
por: Cagliari, Deise, et al.
Publicado: (2019)

Global Context-Aware-Based Deformable Residual Network Module for Precise Pest Recognition and Detection
por: Jiao, Lin, et al.
Publicado: (2022)

Role of Cytokinins for Interactions of Plants With Microbial Pathogens and Pest Insects
por: Akhtar, Saqib Saleem, et al.
Publicado: (2020)

AgriPest-YOLO: A rapid light-trap agricultural pest detection method based on deep learning
por: Zhang, Wei, et al.
Publicado: (2022)

Crop pest image classification based on improved densely connected convolutional network
por: Peng, Hongxing, et al.
Publicado: (2023)

Editorial: Spectroscopy, imaging and machine learning for crop stress
por: Weng, Shizhuang
Publicado: (2023)

Crop Agnostic Monitoring Driven by Deep Learning
por: Halstead, Michael, et al.
Publicado: (2021)

Editorial: Precision control technology and application in agricultural pest and disease control
por: Tang, Yunchao, et al.
Publicado: (2023)

Automatic pest identification system in the greenhouse based on deep learning and machine vision
por: Zhang, Xiaolei, et al.
Publicado: (2023)

Detecting strawberry diseases and pest infections in the very early stage with an ensemble deep-learning model
por: Lee, Sangyeon, et al.
Publicado: (2022)

Editorial: Insect Physiology Aspects of Environmentally Friendly Strategies for Crop Pests and Insect Vectors Control
por: Melo, Ana Claudia Amaral, et al.
Publicado: (2022)

Pathogens—The Hidden Face of Forest Invasions by Wood-Boring Insect Pests
por: Linnakoski, Riikka, et al.
Publicado: (2019)

ASFL-YOLOX: an adaptive spatial feature fusion and lightweight detection method for insect pests of the Papilionidae family
por: Xu, Lijia, et al.
Publicado: (2023)

Toward Sustainability: Trade-Off Between Data Quality and Quantity in Crop Pest Recognition
por: Li, Yang, et al.
Publicado: (2021)

Deep learning-empowered crop breeding: intelligent, efficient and promising
por: Wang, Xiaoding, et al.
Publicado: (2023)

DIANA: A deep learning-based paprika plant disease and pest phenotyping system with disease severity analysis
por: Ilyas, Talha, et al.
Publicado: (2022)

Lexicon and attention-based named entity recognition for kiwifruit diseases and pests: A Deep learning approach
por: Zhang, Lilin, et al.
Publicado: (2022)

Double-Stranded RNA Technology to Control Insect Pests: Current Status and Challenges
por: Christiaens, Olivier, et al.
Publicado: (2020)

Hoverflies provide pollination and biological pest control in greenhouse-grown horticultural crops
por: Li, Hui, et al.
Publicado: (2023)

Increasing crop field size does not consistently exacerbate insect pest problems
por: Rosenheim, Jay A., et al.
Publicado: (2022)

The Plant Ribosome-Inactivating Proteins Play Important Roles in Defense against Pathogens and Insect Pest Attacks
por: Zhu, Feng, et al.
Publicado: (2018)

Editorial: Crop Response to Waterlogging
por: Arduini, Iduna, et al.
Publicado: (2019)

Editorial: Plant-Pest Interactions Volume II: Hemiptera
por: Ortego, Felix, et al.
Publicado: (2021)

Editorial: Induced resistance and priming against pests and pathogens
por: Perazzolli, Michele, et al.
Publicado: (2022)

Editorial: Improving crop health: Understanding the interaction mechanisms between crops and their pathogens
por: Yang, Yuheng, et al.
Publicado: (2023)

Mitigating Groundwater Depletion in North China Plain with Cropping System that Alternate Deep and Shallow Rooted Crops
por: Yang, Xiao-Lin, et al.
Publicado: (2017)

Importance of Ecological Variables in Explaining Population Dynamics of Three Important Pine Pest Insects
por: Hentschel, Rainer, et al.
Publicado: (2018)

Editorial: Molecular interactions between crops and phytopathogens volume III: Vegetables and other crops
por: Zhang, Meixiang, et al.
Publicado: (2022)

CMRD-Net: a deep learning-based Cnaphalocrocis medinalis damage symptom rotated detection framework for in-field survey
por: Chen, Tianjiao, et al.
Publicado: (2023)

Editorial: Genomics and phenomics of crop wild relatives (CWRs) for crop improvement
por: Rangan, Parimalan, et al.
Publicado: (2023)

Editorial: Increasing crop yield: the interaction of crop plant roots with their environment
por: Tracy, Saoirse, et al.
Publicado: (2023)

Editorial: Plant-Pest Interactions Volume III: Coleoptera and Lepidoptera
por: Suzuki, Takeshi, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_hgaa36iotpksj4k5ufg26qvvvj