ReviewPlanta2026
Bridging Artificial Intelligence, Machine Learning with Green Nanotechnology: A Visionary Framework for Smart Fungal Disease Management in Plants.
Review in Planta, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
Fungal diseases in plants pose significant threats to global food security and sustainable agriculture, yet current management methods remain limited by environmental concerns and increasing pathogen resistance. Advanced plant disease detection ML methods include hyperspectral imaging for early disease detection, deep learning (CNNs) for image-based classification, and Internet of Things (IoT) with edge AI for real-time monitoring. Although independent advances have been made in plant disease detection and nanotechnology-based control, there is a clear lack of integrated research that combines intelligent predictive systems with eco-friendly nanobiocontrol agents for comprehensive disease management. We hypothesize that an integrated framework linking advanced disease detection technologies with green-synthesized biowaste-derived nanoparticles will improve early diagnosis and sustainable control of fungal diseases compared to existing approaches. The objectives of this study are to (1) identify limitations in current detection and control strategies, (2) develop a hybrid system that couples real-time monitoring and prediction with targeted application of green nanomaterials, and (3) assess the efficacy of this integrated approach against representative fungal pathogens in model crops. This combined strategy aims to enhance precision, sustainability, and responsiveness in crop protection, offering a forward-looking paradigm for resilient agricultural systems in the face of evolving phytopathological challenges.
Indexed as
Identifiers
41966676What 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.