ReviewSensors (Basel, Switzerland)2021
A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases.
Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 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.
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Who cites it
84 citing papers in PubMed, 353 citations in OpenAlex.
- Prediction of pathological staging and grading of renal clear cell carcinoma based on deep learning algorithms.The Journal of international medical research · 2022Trial
- Multimodal Deep Learning and Foundation Models for Early Detection and Forecasting of Plant Diseases.Plants (Basel, Switzerland) · 2026Review
- Hybrid deep learning framework integrating CNNs, transformers, and adaptive optimization for accurate suspended sediment concentration prediction.Scientific reports · 2026Article
- A resource-efficient framework for plant disease classification: integrating reduced-order modeling with treatment-based label engineering.Frontiers in plant science · 2026Article
- Mobile-assisted deep learning framework for identification of insect pests and diseases of maize from field images.Frontiers in plant science · 2026Article
- An enhanced deep learning-based framework for diagnosing apple leaf diseases.Scientific reports · 2025Article
- A survey: to identify plant leaf diseases by feature extraction methods and classification techniques.Planta · 2025Review
- VGG-EffAttnNet: Hybrid Deep Learning Model for Automated Chili Plant Disease Classification Using VGG16 and EfficientNetB0 With Attention Mechanism.Food science & nutrition · 2025Article
- Prediction of Ultra-High-Performance Concrete (UHPC) Compressive Strength Based on Convolutional Neural Networks.Materials (Basel, Switzerland) · 2025Article
- Optical sorting: past, present and future.Light, science & applications · 2025Review
- CGA-ASNet: an RGB-D amodal segmentation network for restoring occluded tomato regions.Frontiers in plant science · 2025Article
- Leaf disease detection and classification in food crops with efficient feature dimensionality reduction.PloS one · 2025Article
- Article
- Precision cotton disease detection via transformer models applied to leaf imagery.Frontiers in artificial intelligence · 2025Article
- Artificial intelligence-based model for the interpretation and reporting of standard automated perimetry.Arquivos brasileiros de oftalmologia · 2025Observational
- Bayesian optimized multimodal deep hybrid learning approach for tomato leaf disease classification.Scientific reports · 2024Article
- Data Analytics for Predicting Situational Developments in Smart Cities: Assessing User Perceptions.Sensors (Basel, Switzerland) · 2024Article
- Robust diagnosis and meta visualizations of plant diseases through deep neural architecture with explainable AI.Scientific reports · 2024Article
- A novel hierarchical framework for plant leaf disease detection using residual vision transformer.Heliyon · 2024Article
- Automated detection and recognition system for chewable food items using advanced deep learning models.Scientific reports · 2024Article
24 more citing papers are in PubMed but not listed here.
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 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the modern era, deep learning techniques have emerged as powerful tools in image recognition. Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in this area. Applications such as identifying objects, faces, bones, handwritten digits, and traffic signs signify the importance of Convolutional Neural Networks in the real world. The effectiveness of Convolutional Neural Networks in image recognition motivates the researchers to extend its applications in the field of agriculture for recognition of plant species, yield management, weed detection, soil, and water management, fruit counting, diseases, and pest detection, evaluating the nutrient status of plants, and much more. The availability of voluminous research works in applying deep learning models in agriculture leads to difficulty in selecting a suitable model according to the type of dataset and experimental environment. In this manuscript, the authors present a survey of the existing literature in applying deep Convolutional Neural Networks to predict plant diseases from leaf images. This manuscript presents an exemplary comparison of the pre-processing techniques, Convolutional Neural Network models, frameworks, and optimization techniques applied to detect and classify plant diseases using leaf images as a data set. This manuscript also presents a survey of the datasets and performance metrics used to evaluate the efficacy of models. The manuscript highlights the advantages and disadvantages of different techniques and models proposed in the existing literature. This survey will ease the task of researchers working in the field of applying deep learning techniques for the identification and classification of plant leaf diseases.
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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.