Double-branch deep convolutional neural network-based rice leaf diseases recognition and classification
出版年份 2023 全文链接
标题
Double-branch deep convolutional neural network-based rice leaf diseases recognition and classification
作者
关键词
-
出版物
Journal of Agricultural Engineering
Volume -, Issue -, Pages -
出版商
PAGEPress Publications
发表日期
2023-10-30
DOI
10.4081/jae.2023.1544
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Lightweight dense-scale network (LDSNet) for corn leaf disease identification
- (2022) Weihui Zeng et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- A Five Convolutional Layer Deep Convolutional Neural Network for Plant Leaf Disease Detection
- (2022) J. Arun Pandian et al. Electronics
- LS-Net: a convolutional neural network for leaf segmentation of rosette plants
- (2022) Mainak Deb et al. NEURAL COMPUTING & APPLICATIONS
- A novel transfer deep learning method for detection and classification of plant leaf disease
- (2022) Prabhjot Kaur et al. Journal of Ambient Intelligence and Humanized Computing
- An Intelligent System for Cucumber Leaf Disease Diagnosis Based on the Tuned Convolutional Neural Network Algorithm
- (2022) Saman M. Omer et al. Mobile Information Systems
- Tomato Leaf Disease Diagnosis Based on Improved Convolution Neural Network by Attention Module
- (2021) Shengyi Zhao et al. Agriculture-Basel
- Paddy Disease Classification Study: A Deep Convolutional Neural Network Approach
- (2021) Mainak Deb et al. Optical Memory and Neural Networks (Information Optics)
- Crop leaf disease recognition based on Self-Attention convolutional neural network
- (2020) Weihui Zeng et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Identification and recognition of rice diseases and pests using convolutional neural networks
- (2020) Chowdhury R. Rahman et al. BIOSYSTEMS ENGINEERING
- An optimized dense convolutional neural network model for disease recognition and classification in corn leaf
- (2020) Abdul Waheed et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Deep feature based rice leaf disease identification using support vector machine
- (2020) Prabira Kumar Sethy et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Using deep transfer learning for image-based plant disease identification
- (2020) Junde Chen et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Image recognition of four rice leaf diseases based on deep learning and support vector machine
- (2020) Feng Jiang et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- The global burden of pathogens and pests on major food crops
- (2019) Serge Savary et al. Nature Ecology & Evolution
- Cucumber leaf disease identification with global pooling dilated convolutional neural network
- (2019) Shanwen Zhang et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Squeeze-and-Excitation Networks
- (2019) Jie Hu et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
- (2018) Stefan Elfwing et al. NEURAL NETWORKS
- Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks
- (2017) Bin Liu et al. Symmetry-Basel
- Digital image processing techniques for detecting, quantifying and classifying plant diseases
- (2013) Jayme Garcia Arnal Barbedo SpringerPlus
- Recent patterns of crop yield growth and stagnation
- (2012) Deepak K. Ray et al. Nature Communications
- Climate change, plant diseases and food security: an overview
- (2011) S. Chakraborty et al. PLANT PATHOLOGY
- Recent advances in rice blast effector research
- (2010) Barbara Valent et al. CURRENT OPINION IN PLANT BIOLOGY
- Against the grain: safeguarding rice from rice blast disease
- (2009) Pari Skamnioti et al. TRENDS IN BIOTECHNOLOGY
- Current Status and Challenges of Rice Production in China
- (2008) Shaobing Peng et al. PLANT PRODUCTION SCIENCE
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