ReviewSensors (Basel, Switzerland)2025
A Review of CNN Applications in Smart Agriculture Using Multimodal Data.
Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- SAM-CLIP-Thermal: Leveraging large multimodal models for reliable and scalable annotation in thermal image segmentation for field plant phenotyping.Plant phenomics (Washington, D.C.) · 2026Article
- Harnessing high-throughput phenotyping and artificial intelligence for soybean breeding: From trait assessment to data-driven decisions.Plant phenomics (Washington, D.C.) · 2026Review
- Artificial intelligence-driven advancements in agricultural biotechnology.Journal, genetic engineering & biotechnology · 2026Review
- Sensor-Based Cross-Modal Spatiotemporal Alignment and Causal Profit Modeling for Agricultural Input Optimization.Sensors (Basel, Switzerland) · 2026Article
- An Effective YOLOv11 Grain Detection Model Trained on Intact Barley Spikes Reveals a QTL Containing a Pivotal Regulator of Lateral Spikelet Formation.Plants (Basel, Switzerland) · 2026Article
- Sliding sampling and successive variational mode decomposition CNN-BiLSTM-attention based fault detection and early warning method for DC microgrid.Scientific reports · 2026Article
- GroupCeptionNet: a lightweight model for classifying chili seed germination with macro images.Scientific reports · 2026Article
- A CNN-Based Deep Learning Architecture for Discriminating Botanical Adulteration and Complexities Among Commercial Apiaceae Medicinal Species.Food science & nutrition · 2026Article
- A paradigm-level evaluation of deep learning architectures for plant pest and disease recognition under data-scarce field conditions.Frontiers in plant science · 2026Article
- Frequency-domain attention enhanced YOLOv11-EfficientFormerV2 for Tiny lesion detection in complex field plant images.Frontiers in plant science · 2026Article
- Predicting wheat yield and grain quality with UAV multispectral imagery and deep learning.Frontiers in plant science · 2026Article
- From Traditional Machine Learning to Fine-Tuning Large Language Models: A Review for Sensors-Based Soil Moisture Forecasting.Sensors (Basel, Switzerland) · 2025Review
- Artificial Intelligence in Edible Mushroom Cultivation, Breeding, and Classification: A Comprehensive Review.Journal of fungi (Basel, Switzerland) · 2025Review
- High-Precision Pest Management Based on Multimodal Fusion and Attention-Guided Lightweight Networks.Insects · 2025Article
- Computer Vision in Monitoring Fruit Browning: Neural Networks vs. Stochastic Modelling.Sensors (Basel, Switzerland) · 2025Article
- A Review of CNN Applications in Smart Agriculture Using Multimodal Data.Sensors (Basel, Switzerland) · 2025Review
- A lightweight deep convolutional neural network development for soybean leaf disease recognition.Frontiers in plant science · 2025Article
- Chat Demeter: a multi-agent system for plant disease diagnosis integrating CNN-transformer models.Frontiers in plant science · 2025Article
- Comprehensive Investigation of Machine Learning and Deep Learning Networks for Identifying Multispecies Tomato Insect Images.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
This review explores the applications of Convolutional Neural Networks (CNNs) in smart agriculture, highlighting recent advancements across various applications including weed detection, disease detection, crop classification, water management, and yield prediction. Based on a comprehensive analysis of more than 115 recent studies, coupled with a bibliometric study of the broader literature, this paper contextualizes the use of CNNs within Agriculture 5.0, where technological integration optimizes agricultural efficiency. Key approaches analyzed involve image classification, image segmentation, regression, and object detection methods that use diverse data types ranging from RGB and multispectral images to radar and thermal data. By processing UAV and satellite data with CNNs, real-time and large-scale crop monitoring can be achieved, supporting advanced farm management. A comparative analysis shows how CNNs perform with respect to other techniques that involve traditional machine learning and recent deep learning models in image processing, particularly when applied to high-dimensional or temporal data. Future directions point toward integrating IoT and cloud platforms for real-time data processing and leveraging large language models for regulatory insights. Potential research advancements emphasize improving increased data accessibility and hybrid modeling to meet the agricultural demands of climate variability and food security, positioning CNNs as pivotal tools in sustainable agricultural practices. A related repository that contains the reviewed articles along with their publication links is made available.
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.