ArticlePloS one2025
Citrus diseases detection using innovative deep learning approach and Hybrid Meta-Heuristic.
Article in PloS one, 2025. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Comprehensive plant disease classification and severity estimation for sustainable farming via automatic segmentation and multi-scale feature fusion.BMC plant biology · 2026Article
- A hybrid convolution and attention-based framework with visual explanation for fruit disease identification.Scientific reports · 2026Article
- A lightweight LBM detection transformer with multi-scale feature fusion for citrus-picking robots.Frontiers in plant science · 2026Article
- ApaltAI: a web-based diagnostic system with a sequential voting architecture for detecting anthracnose and scab in avocado fruit.Frontiers in plant science · 2026Article
- Novel dual-input stream-based hybrid approach for wheat leaf disease classification using edge-aware features.Scientific reports · 2025Article
- OptiNet-B3: a lightweight explainable deep learning model for multiclass classification of fruit and leaf diseases.Scientific reports · 2025Article
- Deep transfer learning based feature fusion model with Bonobo optimization algorithm for enhanced brain tumor segmentation and classification through biomedical imaging.Scientific reports · 2025Article
- Precision diagnosis of citrus leaf diseases using image enhancement and nonlinear fuzzy ranking ensemble approach NLFuRBe.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Citrus farming is one of the major agricultural sectors of Pakistan and currently represents almost 30% of total fruit production, with its highest concentration in Punjab. Although economically important, citrus crops like sweet orange, grapefruit, lemon, and mandarins face various diseases like canker, scab, and black spot, which lower fruit quality and yield. Traditional manual disease diagnosis is not only slow, less accurate, and expensive but also relies heavily on expert intervention. To address these issues, this research examines the implementation of an automated disease classification system using deep learning and optimal feature selection. The system incorporates data augmentation and transfer learning with pre-trained models such as DenseNet-201 and AlexNet to improve diagnostic accuracy, efficiency, and cost-effectiveness. Experimental results on a citrus leaves dataset show an impressive 99.6% classification accuracy. The proposed framework outperforms existing methods, offering a robust and scalable solution for disease detection in citrus farming, contributing to more sustainable agricultural practices.
Indexed as
Identifiers
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.