ReviewPlant methods2022
A survey of few-shot learning in smart agriculture: developments, applications, and challenges.
Review in Plant methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed.
- AgroQuali-FSL: a few-shot deep learning framework with QualiProtoNet for automated quality grading of fruits and vegetables.Scientific reports · 2026Article
- Autonomous driving system based on dual process theory and deliberate practice theory.Nature communications · 2026Article
- Knowledge-enhanced large language model construction for garlic cultivation using GraphRAG.Frontiers in plant science · 2026Article
- YOLO-SAM AgriScan: A Unified Framework for Ripe Strawberry Detection and Segmentation with Few-Shot and Zero-Shot Learning.Sensors (Basel, Switzerland) · 2025Article
- PlantCaFo: An efficient few-shot plant disease recognition method based on foundation models.Plant phenomics (Washington, D.C.) · 2025Article
- Few-shot crop disease recognition using sequence- weighted ensemble model-agnostic meta-learning.Frontiers in plant science · 2025Article
- Few-shot object detection for pest insects via features aggregation and contrastive learning.Frontiers in plant science · 2025Article
- Multi-kernel inception aggregation diffusion network for tomato disease detection.BMC plant biology · 2024Article
- Study on canopy extraction method for narrowband spectral images based on superpixel color gradation skewness distribution features.Plant methods · 2024Article
- A few-shot learning method for tobacco abnormality identification.Frontiers in plant science · 2024Article
- Phenotyping of Drought-Stressed Poplar Saplings Using Exemplar-Based Data Generation and Leaf-Level Structural Analysis.Plant phenomics (Washington, D.C.) · 2024Article
- AI-Assisted Cotton Grading: Active and Semi-Supervised Learning to Reduce the Image-Labelling Burden.Sensors (Basel, Switzerland) · 2023Article
- Plant and Disease Recognition Based on PMF Pipeline Domain Adaptation Method: Using Bark Images as Meta-Dataset.Plants (Basel, Switzerland) · 2023Article
- A Multi-Layer Feature Fusion Method for Few-Shot Image Classification.Sensors (Basel, Switzerland) · 2023Article
- FSVM: A Few-Shot Threat Detection Method for X-ray Security Images.Sensors (Basel, Switzerland) · 2023Article
- A New Acoustical Autonomous Method for Identifying Endangered Whale Calls: A Case Study of Blue Whale and Fin Whale.Sensors (Basel, Switzerland) · 2023Article
- A novel transfer learning framework for sorghum biomass prediction using UAV-based remote sensing data and genetic markers.Frontiers in plant science · 2023Article
- Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits inPlant phenomics (Washington, D.C.) · 2023Article
- A Tiny Model for Fast and Precise Ship Detection via Feature Channel Pruning.Sensors (Basel, Switzerland) · 2022Article
- A conditional GAN-based approach for enhancing transfer learning performance in few-shot HCR tasks.Scientific reports · 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
6 authors.
Funding
Abstract
With the rise of artificial intelligence, deep learning is gradually applied to the field of agriculture and plant science. However, the excellent performance of deep learning needs to be established on massive numbers of samples. In the field of plant science and biology, it is not easy to obtain a large amount of labeled data. The emergence of few-shot learning solves this problem. It imitates the ability of humans' rapid learning and can learn a new task with only a small number of labeled samples, which greatly reduces the time cost and financial resources. At present, the advanced few-shot learning methods are mainly divided into four categories based on: data augmentation, metric learning, external memory, and parameter optimization, solving the over-fitting problem from different viewpoints. This review comprehensively expounds on few-shot learning in smart agriculture, introduces the definition of few-shot learning, four kinds of learning methods, the publicly available datasets for few-shot learning, various applications in smart agriculture, and the challenges in smart agriculture in future development.
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.