ArticleBMC plant biology2025
Potato plant disease detection: leveraging hybrid deep learning models.
Article in BMC plant biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- TRP-Net: A Text-Guided Dual-Modal Network for Potato Leaf Lesion Detection Under Complex Imaging Conditions.Plants (Basel, Switzerland) · 2026Article
- A novel approach for disease and pests detection in potato production system based on deep learning.Scientific reports · 2026Article
- Enhancing plant disease detection through multi-modal integration of visual and textual data.Plant methods · 2026Article
- Deep learning-based seed germination prediction using morphological traits and RGB images.BMC plant biology · 2026Article
- A multimodal feature fusion with deep representation learning approach for polycystic ovary syndrome diagnosis using ultrasound images.Scientific reports · 2026Article
- Feature extraction in sensor plant disease datasets using reformed membership functions independent of class variables.Scientific reports · 2026Article
- A hybrid CNN-transformer model with adaptive activation function for potato leaf disease classification.Scientific reports · 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
- EnviroSpect-guided ConvMixer-ViT framework for environment-robust potato leaf disease detection.Frontiers in plant science · 2026Article
- Explainable deep learning-based comparative study for guava fruit and leaf disease classification: advancing agricultural diagnostics through AI.Frontiers in plant science · 2026Article
- MapReduce-based deep learning framework for potato leaf disease detection in sustainable precision agriculture.Scientific reports · 2025Article
- Image dataset of ten durian diseases captured in real-field conditions from a family orchard in Vinh Long, Vietnam.Data in brief · 2025Article
- Spatial attention-guided pre-trained networks for accurate identification of crop diseases.Scientific reports · 2025Article
- PotatoLeafNet: two-stage convolutional neural networks for effective Potato Leaf disease identification and classification.Frontiers in artificial intelligence · 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
Agriculture, a crucial sector for global economic development and sustainable food production, faces significant challenges in detecting and managing crop diseases. These diseases can greatly impact yield and productivity, making early and accurate detection vital, especially in staple crops like potatoes. Traditional manual methods, as well as some existing machine learning and deep learning techniques, often lack accuracy and generalizability due to factors such as variability in real-world conditions. This study proposes a novel approach to improve potato plant disease detection and identification using a hybrid deep-learning model, EfficientNetV2B3+ViT. This model combines the strengths of a Convolutional Neural Network - EfficientNetV2B3 and a Vision Transformer (ViT). It has been trained on a diverse potato leaf image dataset, the "Potato Leaf Disease Dataset", which reflects real-world agricultural conditions. The proposed model achieved an accuracy of 85.06
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.