Ammonia and ethanol detection via an electronic nose utilizing a bionic chamber and a sparrow search algorithm–optimized backpropagation neural network DOI Creative Commons

Yeping Shi,

Yunbo Shi,

Haodong Niu

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(12), P. e0309228 - e0309228

Published: Dec. 3, 2024

Ammonia is widely acknowledged to be a stressor and one of the most detrimental gases in animal enclosures. In livestock- poultry-breeding facilities, precise, rapid, affordable method for detecting ammonia concentrations essential. We design develop an electronic nose system containing bionic chamber that imitates nasal-cavity structure humans canines. The sensors are positioned based on fluid simulation results. Response data ethanol response/ recovery times sensor under three collected using system. classified regressed sparrow search algorithm (SSA)-optimized backpropagation neural network (BPNN). results show has relative mean deviation 1.45%. sensor’s output voltage 1.3–2.05 V when concentration ranges from 15 300 ppm. gas 1.89–3.15 8 200 average response time 13 s slower than directly exposed being measured, while 19 faster. tests comparing performance SSA-BPNN, support vector machine (SVM), random forest (RF) models, SSA-BPNN achieves 99.1% classification accuracy, better SVM RF models. It also outperforms other models at regression prediction, with smaller absolute, root square errors. Its coefficient determination (R 2 ) greater 0.99, surpassing those theoretical experimental both indicate proposed chamber, used offers promising approach facilities.

Language: Английский

Synthesis of Mg doped ZnO cauli-flower nanostructures using chemical spray and its investigation for ammonia gas sensing at room temperature DOI
Sushilkumar A. Jadhav, S. D. Lokhande,

G. Umadevi

et al.

Talanta, Journal Year: 2024, Volume and Issue: 285, P. 127403 - 127403

Published: Dec. 18, 2024

Language: Английский

Citations

4
Ultrafast Detection of ppb-Level NH3 Gas at Room Temperature Using CuO Nanoparticles Decorated AlN-Based Surface Acoustic Wave Sensor DOI
Na-Hyun Bak, Kedhareswara Sairam Pasupuleti,

Reddeppa Maddaka

et al.

ACS Sensors, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 22, 2025

Rational design of heterostructure (HS)-based surface acoustic wave (SAW) smart gas sensors for efficient and accurate subppm level ammonia (NH3) detection at room temperature (RT) is great significance in environmental protection human safety. This study introduced a novel HS composed an AlN-based SAW resonator CuO nanoparticles (NPs) as chemical interface NH3 RT (∼26 °C). The structural, morphological, compositions were detailly investigated, which demonstrates that the CuO/AlN was successfully formed via interfacial modulation. sensor exhibited significant positive frequency shift 52.60 kHz response to 100 ppm NH3, 4.8 times higher than as-grown AlN sensor. Additionally, ultrafast response/recovery 5/25 s, remarkably low limit (LOD) 24 ppb, excellent long-term stability selectivity. These results are attributed high porosity defect sites NPs, enhanced charge transfer heterointerface, well decreased mass loading conductivity effects. also demonstrated distinct responses under varying relative humidity (RH): RH (5%–10%) due increased conductivity, negative (20%–80%) loading. sensing characteristics validated through X-ray photoelectron spectroscopy band diagram analysis resistive-type measurements. findings highlight potential integrating metal oxide with nitride semiconductors advanced SAW-based technology industrial applications.

Language: Английский

Citations

0
Enhanced CO gas-sensing using zinc oxide decorated with mixed-valence manganese oxide DOI
So-Young Bak, Se-Hyeong Lee,

Jinwoo Lee

et al.

Journal of Materials Science Materials in Electronics, Journal Year: 2025, Volume and Issue: 36(12)

Published: April 1, 2025

Language: Английский

Citations

0
The application of deep learning technology in smart agriculture: Lightweight apple leaf disease detection model DOI Creative Commons
Man Luo

International Journal for Simulation and Multidisciplinary Design Optimization, Journal Year: 2025, Volume and Issue: 16, P. 7 - 7

Published: Jan. 1, 2025

Current models for disease detection in fruit tree leaves suffer from limitations such as low recognition precision, high frequencies of missed and false detections. To address these challenges, an advanced model AppleLite-YoloV8 is proposed this study. Built on the YOLOv8 architecture, incorporates a refined backbone with EdgeNeXt network, enhancing feature extraction improved identification precision. Also novel C2f-SC module integrates SCCONV convolution into C2f module, creating lightweight architecture while reducing computational complexity. Additionally, DySample adaptively modifies up-sampling process, boosting resistance to interference improving small-scale diseases. The MPDIOU refines bounding box regression loss, accuracy robustness objects varying dimensions. Experimental results demonstrate model's effectiveness detecting common apple leaf diseases Alternaria Blotch, Brown Spot, Grey achieving precision,and recall values 97.56%, 94.38%, respectively, speed 124.33 fps. With just 29.3 million parameters 57.6 GFLOPs, computationally suitable resource-constrained devices. These advancements make it robust, efficient, practical real-time intelligent agricultural environments.

Language: Английский

Citations

0
Prediction of tomato plants infected by fungal pathogens at different disease severities using E-nose and GC–MS DOI

Yubing Sun,

Yutong Zheng

Journal of Plant Diseases and Protection, Journal Year: 2024, Volume and Issue: 131(3), P. 835 - 846

Published: Feb. 8, 2024

Language: Английский

Citations

3
AIE paper shred for the detection of evolved amine vapor from putrefaction processes of fish DOI

Abinaya Muthukumar,

Kalaiyar Swarnalatha

Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy, Journal Year: 2024, Volume and Issue: 323, P. 124860 - 124860

Published: July 23, 2024

Language: Английский

Citations

3
A Gas Sensor Scheme for CO Based on Optical-Feedback Linear-Cavity Enhanced Absorption Spectroscopy DOI
rifan Xie,

Shiyu Guan,

Zhongqi Tan

et al.

Optics Communications, Journal Year: 2024, Volume and Issue: unknown, P. 131105 - 131105

Published: Sept. 1, 2024

Language: Английский

Citations

2
Enhanced sensing properties of optical ammonia sensor based on electrospun fibers containing Eosin-Y and silver nanoparticles DOI

Rispandi,

Cheng-Shane Chu,

Manna Septriani Simanjuntak

et al.

Materials Today Communications, Journal Year: 2024, Volume and Issue: 41, P. 110524 - 110524

Published: Sept. 24, 2024

Language: Английский

Citations

2
Structure Optimization and Data Processing Method of Electronic Nose Bionic Chamber for Detecting Ammonia Emissions from Livestock Excrement Fermentation DOI Creative Commons

Yeping Shi,

Yunbo Shi,

Haodong Niu

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(5), P. 1628 - 1628

Published: March 1, 2024

In areas where livestock are bred, there is a demand for accurate, real-time, and stable monitoring of ammonia concentration in the breeding environment. However, existing electronic nose systems have slow response times limited detection accuracy. this study, we introduce novel solution: bionic chamber construction optimized, sensor data analyzed using an intelligent algorithm. We analyze structure biomimetic surface airflow array to determine sensing units system. The system employs detect ethanol gases circulating within closed box. captured signals processed, followed by application classification regression models prediction. Our results suggest that system, leveraging chamber, offers rapid gas times. A high prediction accuracy, with determination coefficient R2 value 0.99 single-output over 0.98 multi-output predictions, achieved incorporating backpropagation (BP) neural network These outcomes demonstrate effectiveness nose, based on optimized combined BP algorithm, accurately detecting emitted during excreta fermentation, satisfying requirements farms.

Language: Английский

Citations

1
Influence of Environmental Pollution on Animal Behavior DOI Creative Commons
Renata Relić, Mirjana Đukić-Stojčić

Contemporary Agriculture, Journal Year: 2023, Volume and Issue: 72(4), P. 216 - 223

Published: Dec. 1, 2023

Summary Animals, like humans, act according to physiological patterns of behavior that change in response various internal and external stimuli. Environmental pollutants represent “negative” stimuli stressors. Air pollution is among sources pose the greatest threat environment all living organisms. Exposure causes behavioral changes animals disruption organ functions structures, which are often identical those humans working or under same conditions as (e.g., on a farm city). Aside from shorter life span possible premature death, there some diseases commonly occur result exposure. Symptoms indicative disease irritation, such coughing, lameness, diarrhea, eye discharge like, cause animal make movements (actions) not part their normal routine considered changes. Behavioral earliest indicator suffering physical mental disorders can negatively affect its health and, case livestock, production results. Various species serve indicators pollution, domestic animals, including also this purpose. resulting exposure include disorientation, problems interacting with other reproductive problems, respiratory, digestive symptoms, etc. This review compiled data number studies after short long environmental pollutants. The focus was effects air particular importance they share space breed them for economic interest.

Language: Английский

Citations

2