ArticleSensors (Basel, Switzerland)2021
Early Detection of Aphid Infestation and Insect-Plant Interaction Assessment in Wheat Using a Low-Cost Electronic Nose (E-Nose), Near-Infrared Spectroscopy and Machine Learning Modeling.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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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.
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Who cites it
16 citing papers in PubMed, 53 citations in OpenAlex.
- Systemic acquired resistance: an emerging role for jasmonates in local signal biogenesis, translocation and distal signal decoding.The New phytologist · 2026Review
- Pest Detection in Edible Crops at the Edge: An Implementation-Focused Review of Vision, Spectroscopy, and Sensors.Sensors (Basel, Switzerland) · 2025Review
- Article
- Article
- Detection of aphid infestation on faba bean (Journal of plant diseases and protection : scientific journal of the German Phytomedical Society (DPG) · 2025Article
- Comparison of Individual Sensors in the Electronic Nose for Stress Detection in Forest Stands.Sensors (Basel, Switzerland) · 2023Article
- Development of a Low-Cost Electronic Nose with an Open Sensor Chamber: Application to Detection ofSensors (Basel, Switzerland) · 2023Article
- Boosting precision crop protection towards agriculture 5.0Frontiers in plant science · 2023Review
- Early Detection ofSensors (Basel, Switzerland) · 2022Article
- Electronic Nose for the Rapid Detection of Deoxynivalenol in Wheat Using Classification and Regression Trees.Toxins · 2022Article
- Diel Periodicity in Males of the Navel Orangeworm (Lepidoptera: Pyralidae) as Revealed by Automated Camera Traps.Journal of insect science (Online) · 2022Article
- Identification of Factors Affecting Environmental Contamination Represented by Post-Hatching Eggshells of a Common Colonial Waterbird with Usage of Artificial Neural Networks.Sensors (Basel, Switzerland) · 2022Article
- Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies.Foods (Basel, Switzerland) · 2022Article
- Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review.Sensors (Basel, Switzerland) · 2022Review
- Editorial: Special Issue "Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems".Sensors (Basel, Switzerland) · 2021Article
- Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Advances in early insect detection have been reported using digital technologies through camera systems, sensor networks, and remote sensing coupled with machine learning (ML) modeling. However, up to date, there is no cost-effective system to monitor insect presence accurately and insect-plant interactions. This paper presents results on the implementation of near-infrared spectroscopy (NIR) and a low-cost electronic nose (e-nose) coupled with machine learning. Several artificial neural network (ANN) models were developed based on classification to detect the level of infestation and regression to predict insect numbers for both e-nose and NIR inputs, and plant physiological response based on e-nose to predict photosynthesis rate (A), transpiration (E) and stomatal conductance (gs). Results showed high accuracy for classification models ranging within 96.5-99.3% for NIR and between 94.2-99.2% using e-nose data as inputs. For regression models, high correlation coefficients were obtained for physiological parameters (gs, E and A) using e-nose data from all samples as inputs (R = 0.86) and R = 0.94 considering only control plants (no insect presence). Finally, R = 0.97 for NIR and R = 0.99 for e-nose data as inputs were obtained to predict number of insects. Performances for all models developed showed no signs of overfitting. In this paper, a field-based system using unmanned aerial vehicles with the e-nose as payload was proposed and described for deployment of ML models to aid growers in pest management practices.
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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.