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New perspectives on plant disease characterization based on deep learning

https://doi.org/10.1016/j.compag.2020.105220

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What this work says

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inferred← citeshttps://doi.org/10.1007/978-3-031-11346-8_43openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-981-97-9855-1_51openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/s43674-023-00065-zopenalex-snapshot

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inferred← citeshttps://doi.org/10.1016/b978-0-12-818617-6.00017-2openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/b978-0-323-91880-0.00030-1openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.agrcom.2025.100089openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.agwat.2020.106334openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.aiia.2022.11.002openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.atech.2022.100083openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.atech.2024.100605openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.atech.2024.100662openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.atech.2025.100813openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.atech.2026.101837openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.compag.2021.106644openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.compag.2022.106788openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.compag.2022.107144openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.compag.2022.107163openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.compag.2022.107518openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.ecoinf.2023.102068openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.ecoinf.2024.102548openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.engappai.2024.108260openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.engappai.2026.114771openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.eswa.2022.118117openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.eswa.2022.118905openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.eswa.2025.127743openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.eswa.2025.128365openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.frinr.2026.100033openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.inpa.2021.01.003openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.inpa.2024.03.002openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.iswa.2023.200264openalex-snapshot

Multisource neural network feature map fusion: An efficient strategy to detect plant diseases

inferred← citeshttps://doi.org/10.1016/j.jafr.2022.100389openalex-snapshot

Comparative study on the performance of deep learning implementation in the edge computing: Case study on the plant leaf disease identification

inferred← citeshttps://doi.org/10.1016/j.jksuci.2020.09.006openalex-snapshot

Novel fusion of color balancing and superpixel based approach for detection of tomato plant diseases in natural complex environment

inferred← citeshttps://doi.org/10.1016/j.measen.2022.100581openalex-snapshot

Performance analysis of deep learning architectures for plant leaves disease detection

inferred← citeshttps://doi.org/10.1016/j.neucom.2024.128608openalex-snapshot

Beyond supervision: Harnessing self-supervised learning in unseen plant disease recognition

inferred← citeshttps://doi.org/10.1016/j.patcog.2024.110752openalex-snapshot

Efficient and lightweight convolutional neural network architecture search methods for object classification

inferred← citeshttps://doi.org/10.1016/j.procs.2023.01.225openalex-snapshot

A Systematic Review of Citrus Disease Perceptions and Fruit Grading Using Machine Vision

inferred← citeshttps://doi.org/10.1016/j.rineng.2025.107739openalex-snapshot

Molecular validation integrated reliable deep learning approach for early blight severity detection in tomato crops under protected cultivation environments

inferred← citeshttps://doi.org/10.1016/j.rsase.2024.101390openalex-snapshot

High-resolution mapping of Blueberry scorch virus incidence using RGB and multispectral UAV images and deep learning

inferred← citeshttps://doi.org/10.1016/j.suscom.2020.100443openalex-snapshot

Use of deep learning techniques for identification of plant leaf stresses: A review

inferred← citeshttps://doi.org/10.1038/s41598-025-02271-wopenalex-snapshot

An efficient plant disease detection using transfer learning approach

inferred← citeshttps://doi.org/10.1038/s41598-025-08557-3openalex-snapshot

A Multi-kernel CNN model with attention mechanism for classification of citrus plants diseases

inferred← citeshttps://doi.org/10.1038/s41598-025-13079-zopenalex-snapshot

Deep learning-based automatic diagnosis of rice leaf diseases using ensemble CNN models

inferred← citeshttps://doi.org/10.1038/s41598-025-20386-yopenalex-snapshot

An interpretable machine learning approach based on SHAP, Sobol and LIME values for precise estimation of daily soybean crop coefficients

inferred← citeshttps://doi.org/10.1038/s41598-025-32384-1openalex-snapshot

AI-driven drone technology and computer vision for early detection of crop disease in large agricultural areas

inferred← citeshttps://doi.org/10.1051/e3sconf/202455601028openalex-snapshot

Agri Watch: Precision Plant Health Monitoring using Deep Learning

inferred← citeshttps://doi.org/10.1063/5.0108899openalex-snapshot

Review analysis of content based image retrieval for plant disease identification using deep learning algorithms

inferred← citeshttps://doi.org/10.1063/5.0108930openalex-snapshot

The role of deep learning in agriculture: A review

inferred← citeshttps://doi.org/10.1063/5.0129364openalex-snapshot

Leaf-image based analysis of crop diseases using a deep learning recognition model

inferred← citeshttps://doi.org/10.1063/5.0150097openalex-snapshot

Intelligent plant disease detection system

inferred← citeshttps://doi.org/10.1063/5.0168208openalex-snapshot

Deep dynamic classification (DDC) for plant disease detection

inferred← citeshttps://doi.org/10.1063/5.0172897openalex-snapshot

Multi-sync convolutional neural network based E-Agrobot to identify herbaceous plant diseases

inferred← citeshttps://doi.org/10.1063/5.0234968openalex-snapshot

Performance analysis of plant diseases detection using machine learning algorithms

inferred← citeshttps://doi.org/10.1063/5.0244950openalex-snapshot

A comparative analysis of existing state-of-the-art methods for papaya plant monitoring and disease detection using AI techniques

inferred← citeshttps://doi.org/10.1063/5.0328053openalex-snapshot

A comparative analysis of deep learning for yellow mosaic disease classification in Okra plant

inferred← citeshttps://doi.org/10.1080/09064710.2021.2024874openalex-snapshot

RETRACTED ARTICLE: Computer development based embedded systems in precision agriculture: tools and application

inferred← citeshttps://doi.org/10.1088/1742-6596/1918/4/042137openalex-snapshot

Identification of diseases in tomato leaves using convolutional neural network and transfer learning method

inferred← citeshttps://doi.org/10.1088/1748-9326/acadf3openalex-snapshot

Pl@ntNet Crops: merging citizen science observations and structured survey data to improve crop recognition for agri-food-environment applications

inferred← citeshttps://doi.org/10.1088/1755-1315/614/1/012138openalex-snapshot

From machine learning to deep learning in agriculture – the quantitative review of trends

inferred← citeshttps://doi.org/10.1109/access.2020.3001237openalex-snapshot

MDFC–ResNet: An Agricultural IoT System to Accurately Recognize Crop Diseases

inferred← citeshttps://doi.org/10.1109/access.2021.3069646openalex-snapshot

Plant Disease Detection and Classification by Deep Learning—A Review

inferred← citeshttps://doi.org/10.1109/access.2022.3167061openalex-snapshot

Learning Aided System for Agriculture Monitoring Designed Using Image Processing and IoT-CNN

inferred← citeshttps://doi.org/10.1109/access.2022.3192428openalex-snapshot

Tomato Disease Recognition Using a Compact Convolutional Neural Network

inferred← citeshttps://doi.org/10.1109/access.2022.3219481openalex-snapshot

A Huanglongbing Detection Method for Orange Trees Based on Deep Neural Networks and Transfer Learning

inferred← citeshttps://doi.org/10.1109/access.2023.3240100openalex-snapshot

Toward Generalization of Deep Learning-Based Plant Disease Identification Under Controlled and Field Conditions

inferred← citeshttps://doi.org/10.1109/access.2023.3284760openalex-snapshot

A Systematic Literature Review on Plant Disease Detection: Motivations, Classification Techniques, Datasets, Challenges, and Future Trends

inferred← citeshttps://doi.org/10.1109/access.2023.3324722openalex-snapshot

Machine Learning and Deep Learning for Plant Disease Classification and Detection

inferred← citeshttps://doi.org/10.1109/access.2024.3373001openalex-snapshot

Plant Leaf Disease Detection, Classification, and Diagnosis Using Computer Vision and Artificial Intelligence: A Review

inferred← citeshttps://doi.org/10.1109/access.2024.3444796openalex-snapshot

Identification of Leaf Disease Based on Memristor Convolutional Neural Networks

inferred← citeshttps://doi.org/10.1109/access.2025.3541347openalex-snapshot

Multi-Stage Neural Network-Based Ensemble Learning Approach for Wheat Leaf Disease Classification

inferred← citeshttps://doi.org/10.1109/access.2025.3562832openalex-snapshot

A Low Computational Cost Deep Learning Approach for Localization and Classification of Diseases and Pests in Coffee Leaves

inferred← citeshttps://doi.org/10.1109/access.2025.3597844openalex-snapshot

Harvesting Insights on AI-Driven Technologies in Modern Agriculture: A Systematic Literature Review

inferred← citeshttps://doi.org/10.1109/access.2026.3735347openalex-snapshot

PLUNet: A Vision Sensor-Driven Plant Lesion Segmentation Network Integrating Global Context and Boundary-Ambiguity Refinement

inferred← citeshttps://doi.org/10.1109/acet67282.2025.11430198openalex-snapshot

Automated Identification and Classification of Plant Leaf Diseases Using Advanced Deep Convolutional Models with Attention and Transfer Learning Techniques

inferred← citeshttps://doi.org/10.1109/aece67531.2025.11386706openalex-snapshot

Plant Disease Detection Using ResNet9 Architecture

inferred← citeshttps://doi.org/10.1109/agreta61912.2024.10949009openalex-snapshot

Feature Reduction Based Cotton Leaf Curl Disease Detection

inferred← citeshttps://doi.org/10.1109/aisp61711.2024.10870643openalex-snapshot

Plant Disease Identification Using Efficient Fine-tuning of Deep Learning Models

inferred← citeshttps://doi.org/10.1109/apsipaasc58517.2023.10317550openalex-snapshot

Unveiling Robust Feature Spaces: Image vs. Embedding-Oriented Approaches for Plant Disease Identification

inferred← citeshttps://doi.org/10.1109/bigdata59044.2023.10386301openalex-snapshot

Crop Loss Estimation in Maize Agriculture: A Deep Learning Perspective

inferred← citeshttps://doi.org/10.1109/ccic68129.2026.11486213openalex-snapshot

Deep Learning Based Multi-Crop Disease Classification with Transfer Learning and IoT Integration

inferred← citeshttps://doi.org/10.1109/cicn63059.2024.10847455openalex-snapshot

CNN and Edge Detection Based Novel Leaf Disease Identification from Bush with Irrespective of Plant Types and Drone Footage-Based Real-Time Application

inferred← citeshttps://doi.org/10.1109/ciml69155.2026.11692718openalex-snapshot

A Lightweight Convolutional Neural Network for Crop Leaf Disease Recognition

inferred← citeshttps://doi.org/10.1109/decon67170.2025.11447849openalex-snapshot

Plant Disease Detection Using CNN

inferred← citeshttps://doi.org/10.1109/easct59475.2023.10393160openalex-snapshot

Comparison of Different CNN architectures for Grape Leaf Disease Classification

inferred← citeshttps://doi.org/10.1109/easct59475.2023.10393586openalex-snapshot

Deep Neural Network-Based Approach to Identify the Paddy Leaf Disease using ResNet50-V2

inferred← citeshttps://doi.org/10.1109/ecai54874.2022.9847462openalex-snapshot

Detection of Plant Diseases Based On Convolutional Neural Network Approach: a Review

inferred← citeshttps://doi.org/10.1109/esic60604.2024.10481647openalex-snapshot

A Comprehensive Analysis of Stress Detection and Classification Models for Crop Health Assessment

inferred← citeshttps://doi.org/10.1109/gcat62922.2024.10924089openalex-snapshot

Detecting Plant Disease and Suggesting Sustainable Solutions by using Deep Learning Technique

inferred← citeshttps://doi.org/10.1109/i-smac58438.2023.10290468openalex-snapshot

Deep Learning for Plant Disease Detection and Crop Yield Prediction based on NPP-WPF Analysis in Smart Agriculture

inferred← citeshttps://doi.org/10.1109/i2ct51068.2021.9417910openalex-snapshot

Plant Disease Detection Using Deep Learning

inferred← citeshttps://doi.org/10.1109/i3ceet61722.2024.10994033openalex-snapshot

Investigating the Effectiveness of Deep Learning Models in Detecting Wheat Diseases

inferred← citeshttps://doi.org/10.1109/ic2e357697.2023.10262798openalex-snapshot

Citrus fruit disease prediction using ML on a decentralized and secure smart platform

inferred← citeshttps://doi.org/10.1109/icacfct53978.2021.9837379openalex-snapshot

Tomato leaf disease detection and classification based on deep convolutional neural networks

inferred← citeshttps://doi.org/10.1109/icacke65470.2026.11651286openalex-snapshot

Context-Aware Multimodal Transfer Learning for Multi-Crop Plant Disease Detection

inferred← citeshttps://doi.org/10.1109/icaiccit64383.2024.10912399openalex-snapshot

Leveraging Deep Learning for Comprehensive Plant Disease Classification: A Pathway to Sustainable Agriculture

inferred← citeshttps://doi.org/10.1109/icais53314.2022.9742950openalex-snapshot

A Deep Learning-based study of Crop Diseases Recognition and Classification

inferred← citeshttps://doi.org/10.1109/iccakm58659.2023.10449500openalex-snapshot

Potato Disease Detection Using Deep Learning

inferred← citeshttps://doi.org/10.1109/iccams60113.2023.10525833openalex-snapshot

Apple Diseases Detection: Based On Deep Learning

inferred← citeshttps://doi.org/10.1109/iccci56745.2023.10128306openalex-snapshot

An Image Based Classification and Prediction of Diseases on Cotton Leaves Using Deep Learning Techniques

inferred← citeshttps://doi.org/10.1109/icccnt61001.2024.10724735openalex-snapshot

Growing Agriculture: An All-encompassing Structure for Crop Disease Prediction and Ecological Handling

inferred← citeshttps://doi.org/10.1109/icccnt61001.2024.10726238openalex-snapshot

A Review of Soybean Crop Classification and Disease Identification Using AI Learning and IoT-Based Model

inferred← citeshttps://doi.org/10.1109/iccct53315.2021.9711781openalex-snapshot

Plant Disease Identification on Real-World Data using Deep Learning: A Comparative Study

inferred← citeshttps://doi.org/10.1109/iccins58907.2023.10450112openalex-snapshot

An End-to-End Deep Learning Method for Potato Blight Disease Classification Using CNN

inferred← citeshttps://doi.org/10.1109/iccmc65190.2025.11140939openalex-snapshot

Integrative Approaches in Modern Agriculture: Leveraging IoT and Deep Learning for Plant Disease Forecasting Under Climate Change

inferred← citeshttps://doi.org/10.1109/iccsp60870.2024.10543969openalex-snapshot

Diagnosis of Plant Leaf Disease using Vision Transformer

inferred← citeshttps://doi.org/10.1109/icctdc64446.2025.11158173openalex-snapshot

A Gan-Based Approach for Automated Plant Disease Classification Using Image Processing Techniques

inferred← citeshttps://doi.org/10.1109/iccubea65967.2025.11283826openalex-snapshot

Plant Disease Detection System Using Convolutional Neural Networks: a Comprehensive Approach With Real-Time Deployment

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