ArticleInsects2023
A Deep-Learning-Based Detection Approach for the Identification of Insect Species of Economic Importance.
Article in Insects, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of artificial intelligence use in behavioral analysis of invertebrate and larval model organisms: methods, applications and future recommendations.Frontiers in artificial intelligence · 2026Pooled it
- Article
- A structured literature review of computer vision methods for insect identification.Journal of insect science (Online) · 2026Review
- Tephritid26: A standardized, multi-angle image dataset of quarantine-significant true fruit flies for deep learning-based identification.Scientific data · 2026Article
- Arthropod Natural Enemies in Biological Control: A Systematic Bibliometric Analysis 2016-2025.Insects · 2026Article
- Article
- Article
- A Systematic Review of Species Classification Using Deep Learning Algorithms and Gender Identification ofZoological studies · 2025Article
- Molecular insights and diagnostic advances in strawberry-infecting viruses.Frontiers in microbiology · 2025Review
- Swin Attention Augmented Residual Network: a fine-grained pest image recognition method.Frontiers in plant science · 2025Article
- Enhancing mosquito classification through self-supervised learning.Scientific reports · 2024Article
- Vision Measurement System for Gender-Based Counting ofSensors (Basel, Switzerland) · 2024Article
- Lizard Body Temperature Acquisition and Lizard Recognition Using Artificial Intelligence.Sensors (Basel, Switzerland) · 2024Article
- An update forBiodiversity data journal · 2024Article
- Learning algorithms estimate pose and detect motor anomalies in flies exposed to minimal doses of a toxicant.iScience · 2023Article
- Wing Interferential Patterns (WIPs) and machine learning for the classification of some Aedes species of medical interest.Scientific reports · 2023Article
- Feature Refinement Method Based on the Two-Stage Detection Framework for Similar Pest Detection in the Field.Insects · 2023Article
- A Novel Deep Learning Model for Accurate Pest Detection and Edge Computing Deployment.Insects · 2023Article
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
Artificial Intelligence (AI) and automation are fostering more sustainable and effective solutions for a wide spectrum of agricultural problems. Pest management is a major challenge for crop production that can benefit from machine learning techniques to detect and monitor specific pests and diseases. Traditional monitoring is labor intensive, time demanding, and expensive, while machine learning paradigms may support cost-effective crop protection decisions. However, previous studies mainly relied on morphological images of stationary or immobilized animals. Other features related to living animals behaving in the environment (e.g., walking trajectories, different postures, etc.) have been overlooked so far. In this study, we developed a detection method based on convolutional neural network (CNN) that can accurately classify in real-time two tephritid species (
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.