ArticlePlant methods2023
A deep learning model for predicting risks of crop pests and diseases from sequential environmental data.
Article in Plant methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 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
7 citing papers in PubMed, 37 citations in OpenAlex.
- Machine learning-enabled chemical ecology for integrated pest management: from volatiles to field applications.Journal of insect science (Online) · 2026Review
- LDDHybridNet: an ROI-aware CNN-LSTM hybrid framework for accurate and early leaf disease detection in precision agriculture.Scientific reports · 2026Article
- Phenological modeling for ecological double-cropping of grapes with rain and pest resilience strategies based on MobileNet developed by Augmented Dream Optimizer.Frontiers in plant science · 2026Article
- An environment-guided visual-temporal deep learning framework for early disease detection in greenhouse horticultural crops.Frontiers in plant science · 2026Article
- A predictive framework using advanced machine learning approaches for measuring and analyzing the impact of synthetic agrochemicals on human health.Scientific reports · 2025Article
- A review of deep learning architectures for plant disease detection.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
- Research Progress on Plant Responses to Stress Combinations in the Context of Climate Change.Plants (Basel, Switzerland) · 2024Review
Corrections and comments
- Erratum issued
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Crop pests reduce productivity, so managing them through early detection and prevention is essential. Data from various modalities are being used to predict crop diseases by applying machine learning methodology. In particular, because growth environment data is relatively easy to obtain, many attempts are made to predict pests and diseases using it. In this paper, we propose a model that predicts diseases through previous growth environment information of crops, including air temperature, relative humidity, dew point, and CO
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.