ArticleHeliyon2024
Deep learning for mango leaf disease identification: A vision transformer perspective.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- A Visual Question Answering Dataset for Benchmarking Vision-Language Models in Plant Science.Scientific data · 2026Article
- SAR-MLD1-2025-MangoLeaf: A comprehensive high-quality mango leaf dataset for disease classification.Data in brief · 2026Article
- An uncertainty-aware evaluation framework based on hierarchical vision transformers for robust cross-domain plant leaf disease classification.Scientific reports · 2026Article
- A lightweight convolutional neural network for real-time monitoring of smart mango orchard systems.Scientific reports · 2026Article
- Dual-modality fusion for mango disease classification using dynamic attention based ensemble of leaf & fruit images.Scientific reports · 2025Article
- OCRNet a robust deep learning framework for alphanumeric character recognition to assist the visually impaired.Scientific reports · 2025Article
- PlantCareNet: an advanced system to recognize plant diseases with dual-mode recommendations for prevention.Plant methods · 2025Article
- Attention-enhanced hybrid deep learning model for robust mango leaf disease classification via ConvNeXt and vision transformer fusion.Frontiers in plant science · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over the last decade, the use of machine learning in smart agriculture has surged in popularity. Deep learning, particularly Convolutional Neural Networks (CNNs), has been useful in identifying diseases in plants at an early stage. Recently, Vision Transformers (ViTs) have proven to be effective in image classification tasks. These architectures often outperform most state-of-the-art CNN models. However, the adoption of vision transformers in agriculture is still in its infancy. In this paper, we evaluated the performance of vision transformers in identification of mango leaf diseases and compare them with popular CNNs. We proposed an optimized model based on a pretrained Data-efficient Image Transformer (DeiT) architecture that achieves 99.75% accuracy, better than many popular CNNs including SqueezeNet, ShuffleNet, EfficientNet, DenseNet121, and MobileNet. We also demonstrated that vision transformers can have a shorter training time than CNNs, as they require fewer epochs to achieve optimal results. We also proposed a mobile app that uses the model as a backend to identify mango leaf diseases in real-time.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.