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References from Deep convolutional neural network-based automated identification and classification of mungbean foliar diseases. Local targets link to admitted publications; unresolved targets remain external evidence.
Digital image processing techniques for detecting, quantifying and classifying plant diseases
10.1186/2193-1801-2-660 · 2013 · External reference
Unresolved reference
2018 · External reference
From leaves to lab: innovative methods in plant disease diagnosis
10.33545/26633582.2025.v7.i1c.184 · 2025 · External reference
Leaf disease classification in bell pepper plant using VGGNet
10.36548/jiip.2023.1.003 · 2023 · External reference
An efficient VGG16-based deep learning model for automated potato pest detection
10.1016/j.atech.2025.101409 · 2025 · External reference
Agrochemicals, environment, and human health
10.1146/annurev-environ-120920-111015 · 2022 · External reference
Image-based crop disease detection using machine learning
10.1111/ppa.14006 · 2025 · External reference
Unresolved reference
2021 · External reference
Integrated management of major diseases of mungbean by seed treatment and foliar application of insecticide, fungicides and bioagent
10.1016/j.cropro.2012.12.025 · 2013 · External reference
Leveraging inception V3 for precise early and late blight disease classification in potato crops
10.18280/ts.410213 · 2024 · External reference
Deep learning models for plant disease detection and diagnosis
10.1016/j.compag.2018.01.009 · 2018 · External reference
A huanglongbing detection method for orange trees based on deep neural networks and transfer learning
10.1109/access.2022.3219481 · 2022 · External reference
Deep residual learning for image recognition
10.1109/cvpr.2016.90 · 2016 · External reference
A comprehensive evaluation of lightweight deep learning models for tomato disease classification on edge computing environments
10.1038/s41598-026-42439-6 · 2026 · External reference
Densely connected convolutional networks
10.1109/cvpr.2017.243 · 2017 · External reference
Multi-source data fusion for estimating maize leaf area index over the whole growing season under different mulching and irrigation conditions
10.1016/j.fcr.2023.109111 · 2023 · External reference
10.1016/j.jafr.2023.100786
10.1016/j.jafr.2023.100786 · 2023 · External reference
Deep learning in agriculture: a survey
10.1016/j.compag.2018.02.016 · 2018 · External reference
Integrated morphological, biochemical and molecular screening of mungbean (Vigna radiata L.) genotypes for mungbean yellow mosaic virus (MYMV) resistance
10.1016/j.plaphy.2025.110596 · 2025 · External reference
10.18805/lr-5431
10.18805/lr-5431 · 2025 · External reference
Enhancing agriculture through real-time grape leaf disease classification via an edge device with a lightweight CNN architecture and Grad-CAM
10.1038/s41598-024-66989-9 · 2024 · External reference
An automatic fine-tuned inceptionv3 model for groundnut leaf disease multiclass classification
10.1109/incet61516.2024.10593146 · 2024 · External reference
Smart plant disease diagnosis using multiple deep learning and web application integration
10.1016/j.jafr.2025.101948 · 2025 · External reference
Soil and the intensification of agriculture for global food security
10.1016/j.envint.2019.105078 · 2019 · External reference
Machine learning in agriculture: a review
10.3390/s18082674 · 2018 · External reference
Exploring deep learning and explainable AI for precise rice leaf disease detection and classification
10.1109/access.2025.3609671 · 2025 · External reference
Evaluation of state-of-the-art deep learning techniques for plant disease and pest detection
10.32604/cmc.2025.065250 · 2025 · External reference
Detecting sugarcane leaf diseases using inceptionv3 convolutional neural networks
10.1109/icauc68182.2026.11441235 · 2026 · External reference
Sustainable food production: constraints, challenges and choices by 2050
10.1007/s12571-015-0441-1 · 2015 · External reference
Using deep learning for image-based plant disease detection
10.3389/fpls.2016.01419 · 2016 · External reference
Perspectives and challenges for sustainable management of fungal diseases of mungbean [Vigna radiata (L.) R
10.3389/fenvs.2018.00053 · 2018 · External reference
Response of mungbean genotypes for resistance against mungbean yellow mosaic virus
10.59797/jfl.v37.i1.181 · 2024 · External reference
Unresolved reference
2020 · External reference
“Increasing F1 score with VGG16 during plant disease classification over VGG19,”
10.1063/5.0238123 · 2024 · External reference
A systematic review of the current state of transfer learning accelerated CNN-based plant leaf disease classification
10.1109/access.2025.3584404 · 2025 · External reference
Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance
10.1016/j.compag.2010.06.009 · 2010 · External reference
A survey on image data augmentation for deep learning
10.1186/s40537-019-0197-0 · 2019 · External reference
Unresolved reference
2015 · External reference
Rethinking the inception architecture for computer vision
10.1109/cvpr.2016.308 · 2016 · External reference
A comparative study of fine-tuning deep learning models for plant disease identification
10.1016/j.compag.2018.03.032 · 2019 · External reference
Convolutional neural networks in detection of plant leaf diseases: a review
10.3390/agriculture12081192 · 2022 · External reference
Implementing inception v3, VGG-16 and VGG-19 architectures of CNN for medicinal plant leaves identification and disease detection
10.52783/jes.3989 · 2024 · External reference
Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture
10.1007/s10462-024-11100-x · 2025 · External reference
Precision farming for sustainability: an agricultural intelligence model
10.1016/j.compag.2024.109386 · 2024 · External reference
Classification of angular leaf spot, bean rust diseases in haricot bean leaves using CNN and transfer learning
10.1109/ict4da62874.2024.10777237 · 2024 · External reference
