ArticleFrontiers in plant science2024
Detection and identification of plant leaf diseases using YOLOv4.
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed.
- Intersection rotational invariance regression based plant disease detection.Plant molecular biology · 2026Article
- YOLO-AVCA-CBAMNet: Attention-driven framework for detection and classification of green pepper maturity stages.MethodsX · 2026Article
- Lightweight attention enhanced YOLOv11 for accurate multi class detection of brinjal diseases.Scientific reports · 2026Article
- Integrating morphological and deep learning approaches for the identification of economically important nematode genera in vineyards:Helminthologia · 2026Article
- Detection of Soil-Borne Pathogens Using Fine-Tuned Deep Learning Models: A Case Study on the Soybean Cyst Nematode (Heterodera glycines Ichinohe).The plant pathology journal · 2026Article
- An Effective Approach for Recognition of Crop Diseases Using Advanced Image Processing and YOLOv8.Food science & nutrition · 2026Article
- Unveiling the landscape of plant virology in Saudi Arabia: seven decades of progress and future directions toward Vision 2030.Frontiers in plant science · 2026Review
- LSL-YOLO11n: a YOLO11n-based model for maize leaf disease detection in complex field environments.Frontiers in plant science · 2026Article
- A robust cross-crop disease detection framework based on SIS-YOLOv11 with climate-adaptive mechanisms.PloS one · 2026Article
- AgriFusionNet: a context-aware multimodal leaf disease diagnosis and classification system for sustainable plant health monitoring.Frontiers in fungal biology · 2026Article
- A multi-scale small object detection framework for tomato leaf pest and disease detection in complex natural environments.Frontiers in plant science · 2026Article
- Deep vision in agriculture: assessing the function of YOLO in the classification of plant leaf diseases (PLDs).BioData mining · 2025Article
- SCA-MobiPlant: smartphone-deployed multistage attention fusion model for accurate field detection of chili leaf curl complex.Plant methods · 2025Article
- Applying the transfer learning models on the dataset on the effect of diseases on Nagvel-betel (Piper betle) leaves.Data in brief · 2025Article
- A Review on the Detection of Plant Disease Using Machine Learning and Deep Learning Approaches.Journal of imaging · 2025Review
- Molecular Breeding for Abiotic Stress Tolerance in Crops: Recent Developments and Future Prospectives.International journal of molecular sciences · 2025Review
- A multi-module enhanced YOLOv8 framework for accurate AO classification of distal radius fractures: SCFAST-YOLO.Frontiers in medicine · 2025Article
- DSA-net: a lightweight and efficient deep learning-based model for pea leaf disease identification.Frontiers in plant science · 2025Article
- RTCB: an integrated deep learning model for garlic leaf disease identification.Frontiers in plant science · 2025Article
- ViTKAB: an efficient deep learning network for cotton leaf disease identification.Frontiers in plant science · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Detecting plant leaf diseases accurately and promptly is essential for reducing economic consequences and maximizing crop yield. However, farmers' dependence on conventional manual techniques presents a difficulty in accurately pinpointing particular diseases. This research investigates the utilization of the YOLOv4 algorithm for detecting and identifying plant leaf diseases. This study uses the comprehensive Plant Village Dataset, which includes over fifty thousand photos of healthy and diseased plant leaves from fourteen different species, to develop advanced disease prediction systems in agriculture. Data augmentation techniques including histogram equalization and horizontal flip were used to improve the dataset and strengthen the model's resilience. A comprehensive assessment of the YOLOv4 algorithm was conducted, which involved comparing its performance with established target identification methods including Densenet, Alexanet, and neural networks. When YOLOv4 was used on the Plant Village dataset, it achieved an impressive accuracy of 99.99%. The evaluation criteria, including accuracy, precision, recall, and f1-score, consistently showed high performance with a value of 0.99, confirming the effectiveness of the proposed methodology. This study's results demonstrate substantial advancements in plant disease detection and underscore the capabilities of YOLOv4 as a sophisticated tool for accurate disease prediction. These developments have significant significance for everyone involved in agriculture, researchers, and farmers, providing improved capacities for disease control and crop protection.
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.