ArticlePlant phenomics (Washington, D.C.)2024
Advancements in Imaging Sensors and AI for Plant Stress Detection: A Systematic Literature Review.
Article in Plant phenomics (Washington, D.C.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of multi-mode analytics for enhanced plant stress evaluation.Frontiers in plant science · 2025Pooled it
- Tropical vulnerability and temperate resilience: molecular and physiological insights into cold stress adaptation in oil palm.GM crops & food · 2026Review
- BN-NeRF: A fast 3D reconstruction and phenotyping framework for banana plants using handheld devices.Plant phenomics (Washington, D.C.) · 2026Article
- Accurate 3D recording: Integrating ground-based LiDAR data and 3D segmentation network to extract 3D traits and analyze genetics in wheat populations.Plant phenomics (Washington, D.C.) · 2026Article
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- Application of deep learning in crop research: From genomics to phenomics.The plant genome · 2026Review
- Artificial Intelligence (AI) in Detection of Abiotic Stress in Plants: A Review.Sensors (Basel, Switzerland) · 2026Review
- Detection of Soil-Borne Pathogens Using Fine-Tuned Deep Learning Models: A Case Study on the Soybean Cyst Nematode (Heterodera glycines Ichinohe).The plant pathology journal · 2026Article
- Responses and adaptations of plants to abiotic stress: transcriptional regulation of secondary metabolic pathways, metabolomics, and nanobiological approaches.Frontiers in genetics · 2026Review
- F2DMAS: a smartphone video-based 3D phenotyping workflow for potted plants in complex backgrounds.Frontiers in plant science · 2026Article
- AI-integrated digital breeding for crop improvement.Frontiers in plant science · 2026Review
- MRSliceNet: Multi-Scale Recursive Slice and Context Fusion Network for Instance Segmentation of Leaves from Plant Point Clouds.Plants (Basel, Switzerland) · 2025Article
- Detection of Abiotic Stress in Potato and Sweet Potato Plants Using Hyperspectral Imaging and Machine Learning.Plants (Basel, Switzerland) · 2025Article
- A machine-learning-powered spectral-dominant multimodal soft wearable system for long-term and early-stage diagnosis of plant stresses.Science advances · 2025Article
- Micro- and Nanoengineered Devices for Rapid Chemotaxonomic Profiling of Medicinal Plants.Nanomaterials (Basel, Switzerland) · 2025Review
- Wearable Standalone Sensing Systems for Smart Agriculture.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Epitranscriptomic modifications for enhancing abiotic stress resistance in plants.Frontiers in plant science · 2025Review
- Current methods and future needs for visible and non-visible detection of plant stress responses.Frontiers in plant science · 2025Review
- From text to traits: exploring the role of large language models in plant breeding.Frontiers in plant science · 2025Review
- Terahertz Spectroscopy in Assessing Temperature-Shock Effects on Citrus.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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Integrating imaging sensors and artificial intelligence (AI) have contributed to detecting plant stress symptoms, yet data analysis remains a key challenge. Data challenges include standardized data collection, analysis protocols, selection of imaging sensors and AI algorithms, and finally, data sharing. Here, we present a systematic literature review (SLR) scrutinizing plant imaging and AI for identifying stress responses. We performed a scoping review using specific keywords, namely abiotic and biotic stress, machine learning, plant imaging and deep learning. Next, we used programmable bots to retrieve relevant papers published since 2006. In total, 2,704 papers from 4 databases (Springer, ScienceDirect, PubMed, and Web of Science) were found, accomplished by using a second layer of keywords (e.g., hyperspectral imaging and supervised learning). To bypass the limitations of search engines, we selected OneSearch to unify keywords. We carefully reviewed 262 studies, summarizing key trends in AI algorithms and imaging sensors. We demonstrated that the increased availability of open-source imaging repositories such as PlantVillage or Kaggle has strongly contributed to a widespread shift to deep learning, requiring large datasets to train in stress symptom interpretation. Our review presents current trends in AI-applied algorithms to develop effective methods for plant stress detection using image-based phenotyping. For example, regression algorithms have seen substantial use since 2021. Ultimately, we offer an overview of the course ahead for AI and imaging technologies to predict stress responses. Altogether, this SLR highlights the potential of AI imaging in both biotic and abiotic stress detection to overcome challenges in plant data analysis.
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.