Design and Development of ZnO Based Gas Sensor for Fruit Ripening Detection

Hasanuddin, N.H.; Wahid, M. H. A.; Shahimin, M. M.; Hambali, N. A. M. Ahmad; Nazir, Nighat; Khairuddin, N.Z.; Ramli, Muhammad M.; Isa, Salmi Mohd

doi:10.1051/matecconf/20167801109

Cited by 3 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The gathered data underwent statistical analysis for the purpose of grading and assessing the ripeness of watermelons. Hasanuddin et al [14] designed a 0.5 µm thick zinc oxide sensitive layer on a LiNbO 3 piezoelectric substrate specifically for sensing ethylene (C 2 H 4 ) gas, aiming to discern the ripeness of fruits. ArrÁZola et al [15] evaluated five maturity levels of Tainong papaya fruits through the examination of mechanical resistance and the application of finite element analysis (FEA); an in-depth analysis was conducted.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Deep Learning Semantic Segmentation Model for Optical-Image-Based Post-Harvest Fruit Ripeness Analysis of Sugar Apples (Annona squamosa)

Xie,

Ke,

Chen

et al. 2024

Agriculture

View full text Add to dashboard Cite

The sugar apple (Annona squamosa) is valued for its taste, nutritional richness, and versatility, making it suitable for fresh consumption and medicinal use with significant commercial potential. Widely found in the tropical Americas and Asia’s tropical or subtropical regions, it faces challenges in post-harvest ripeness assessment, predominantly reliant on manual inspection, leading to inefficiency and high labor costs. This paper explores the application of computer vision techniques in detecting ripeness levels of harvested sugar apples and proposes an improved deep learning model (ECD-DeepLabv3+) specifically designed for ripeness detection tasks. Firstly, the proposed model adopts a lightweight backbone (MobileNetV2), reducing complexity while maintaining performance through MobileNetV2′s unique design. Secondly, it incorporates the efficient channel attention (ECA) module to enhance focus on the input image and capture crucial feature information. Additionally, a Dense ASPP module is introduced, which enhances the model’s perceptual ability and expands the receptive field by stacking feature maps processed with different dilation rates. Lastly, the proposed model emphasizes the spatial information of sugar apples at different ripeness levels by the coordinate attention (CA) module. Model performance is validated using a self-made dataset of harvested optical images categorized into three ripeness levels. The proposed model (ECD-DeepLabv3+) achieves values of 89.95% for MIoU, 94.58% for MPA, 96.60% for PA, and 94.61% for MF1, respectively. Compared to the original DeepLabv3+, it greatly reduces the number of model parameters (Params) and floating-point operations (Flops) by 89.20% and 69.09%, respectively. Moreover, the proposed method could be directly applied to optical images obtained from the surface of the sugar apple, which provides a potential solution for the detection of post-harvest fruit ripeness.

show abstract

Section: Introductionmentioning

confidence: 99%

A Lightweight Deep Learning Semantic Segmentation Model for Optical-Image-Based Post-Harvest Fruit Ripeness Analysis of Sugar Apples (Annona squamosa)

Xie,

Ke,

Chen

et al. 2024

Agriculture

View full text Add to dashboard Cite

show abstract

Design and Simulation of SAW Sensor in COMSOL Multiphysics for Detection of Hexanal in Exhaled Breath

Kondapalli,

Korapati,

Ettikota

et al. 2024

2024 2nd International Conference on Device Intelligence, Computing and Communication Technologies (DICCT)

View full text Add to dashboard Cite

A Novel Impedimetric Ethylene Gas Sensor Based on Copper Foam/CuO/SnO₂ Nanocomposite

Yusefi,

Jannatdoust,

Kheiri

et al. 2024

J. Electrochem. Soc.

View full text Add to dashboard Cite

Ethylene gas plays a key role in the natural ripening of fruits and vegetables. However, high concentrations of ethylene can reduce the shelf life of the product and exacerbate destructive reactions. Measuring the concentration of ethylene is a powerful method to control the ripening and spoilage of agricultural products. The conventional ethylene detection tools are large and expensive or do not offer sufficient sensitivity and selectivity. Therefore, it is important to build small, energy-efficient, low-cost, high-sensitivity ethylene sensors. In this work, CF/CuO/SnO2 nanocomposite was synthesized based on copper oxide nanoclusters by converting copper foam (CF) into tin dioxide/copper oxide (CuO/SnO2) dual-core nano-hybrid using thermal and hydrothermal methods. Energy-dispersive X-ray analysis, field-emission scanning electron microscopy, X-ray diffraction (XRD), grazing XRD, Brunnauer-Emmett-Teller analysis, and UV–vis spectroscopy techniques were used to characterize CF/CuO/SnO2 nanocomposites. Parameters affecting sensor performance such as temperature, gas concentration, sensor stability, and sensor selectivity were also explored. The results showed that CF/CuO/SnO2 nanocomposite with a specific surface area of 1.4480 m2 g−1, a sensitivity of 83%, and ethylene concentration of 80 ppm at 150 °C, as an n-p hybrid, can be a suitable sensor for ethylene detection in air.

show abstract

Design and Development of ZnO Based Gas Sensor for Fruit Ripening Detection

Cited by 3 publications

References 11 publications

A Lightweight Deep Learning Semantic Segmentation Model for Optical-Image-Based Post-Harvest Fruit Ripeness Analysis of Sugar Apples (Annona squamosa)

A Lightweight Deep Learning Semantic Segmentation Model for Optical-Image-Based Post-Harvest Fruit Ripeness Analysis of Sugar Apples (Annona squamosa)

Design and Simulation of SAW Sensor in COMSOL Multiphysics for Detection of Hexanal in Exhaled Breath

A Novel Impedimetric Ethylene Gas Sensor Based on Copper Foam/CuO/SnO₂ Nanocomposite

Contact Info

Product

Resources

About

Design and Development of ZnO Based Gas Sensor for Fruit Ripening Detection

Cited by 3 publications

References 11 publications

A Lightweight Deep Learning Semantic Segmentation Model for Optical-Image-Based Post-Harvest Fruit Ripeness Analysis of Sugar Apples (Annona squamosa)

A Lightweight Deep Learning Semantic Segmentation Model for Optical-Image-Based Post-Harvest Fruit Ripeness Analysis of Sugar Apples (Annona squamosa)

Design and Simulation of SAW Sensor in COMSOL Multiphysics for Detection of Hexanal in Exhaled Breath

A Novel Impedimetric Ethylene Gas Sensor Based on Copper Foam/CuO/SnO2 Nanocomposite

Contact Info

Product

Resources

About

A Novel Impedimetric Ethylene Gas Sensor Based on Copper Foam/CuO/SnO₂ Nanocomposite