ArticleSensors (Basel, Switzerland)2022
Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 197 citations in OpenAlex.
- Deep Learning for Disease Detection: Building a Leaf Image Classifier for Roses.Sensors (Basel, Switzerland) · 2026Article
- VCNet: Optimized Deep Learning framework with deep feature extraction and genetic algorithm for multiclass rice crop disease detection.MethodsX · 2025Article
- Grapes leaf disease dataset for precision agriculture.Data in brief · 2025Article
- Article
- Optimized sequential model for superior classification of plant disease.Scientific reports · 2025Article
- A review of plant leaf disease identification by deep learning algorithms.Frontiers in plant science · 2025Review
- Corn variety identification based on improved EfficientNet lightweight neural network.Frontiers in plant science · 2025Article
- An ensemble deep learning models approach using image analysis for cotton crop classification in AI-enabled smart agriculture.Plant methods · 2024Article
- Multiclass classification of diseased grape leaf identification using deep convolutional neural network(DCNN) classifier.Scientific reports · 2024Article
- Detection and identification of plant leaf diseases using YOLOv4.Frontiers in plant science · 2024Article
- Automatic identification of pavement cracks in public roads using an optimized deep convolutional neural network model.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Article
- Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis.Foods (Basel, Switzerland) · 2023Review
- BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model.Journal of imaging · 2023Article
- Recent advances in plant disease severity assessment using convolutional neural networks.Scientific reports · 2023Article
- Influence of Insufficient Dataset Augmentation on IoU and Detection Threshold in CNN Training for Object Detection on Aerial Images.Sensors (Basel, Switzerland) · 2022Article
- Smart Strawberry Farming Using Edge Computing and IoT.Sensors (Basel, Switzerland) · 2022Article
- A Hybrid Model for Leaf Diseases Classification Based on the Modified Deep Transfer Learning and Ensemble Approach for Agricultural AIoT-Based Monitoring.Computational intelligence and neuroscience · 2022Article
- Artificial Intelligence-Based Drone System for Multiclass Plant Disease Detection Using an Improved Efficient Convolutional Neural Network.Frontiers in plant science · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Agriculture is crucial to the economic prosperity and development of India. Plant diseases can have a devastating influence towards food safety and a considerable loss in the production of agricultural products. Disease identification on the plant is essential for long-term agriculture sustainability. Manually monitoring plant diseases is difficult due to time limitations and the diversity of diseases. In the realm of agricultural inputs, automatic characterization of plant diseases is widely required. Based on performance out of all image-processing methods, is better suited for solving this task. This work investigates plant diseases in grapevines. Leaf blight, Black rot, stable, and Black measles are the four types of diseases found in grape plants. Several earlier research proposals using machine learning algorithms were created to detect one or two diseases in grape plant leaves; no one offers a complete detection of all four diseases. The photos are taken from the plant village dataset in order to use transfer learning to retrain the EfficientNet B7 deep architecture. Following the transfer learning, the collected features are down-sampled using a Logistic Regression technique. Finally, the most discriminant traits are identified with the highest constant accuracy of 98.7% using state-of-the-art classifiers after 92 epochs. Based on the simulation findings, an appropriate classifier for this application is also suggested. The proposed technique's effectiveness is confirmed by a fair comparison to existing procedures.
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.