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 2 publications

(1 citation statement)

References 11 publications

(15 reference statements)

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“…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

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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.

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Section: Introductionmentioning

confidence: 99%