Determination of Sugar Apple Ripeness via Image Processing Using Convolutional Neural Network

Sanchez, Rhys B.; Esteves, Jose Angelo C.; Linsangan, Noel B.

doi:10.1109/iccae56788.2023.10111204

Cited by 4 publications

(2 citation statements)

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“…Currently, to address the inefficiency caused by manual labor in fruit ripeness classification, numerous computer-vision-based methods for fruit ripeness detection have been proposed. Although these methods have demonstrated good performance (with average accuracy exceeding 85% for sugar apple [26] and strawberry [24] ripeness tasks using YOLO object detection and semantic segmentation algorithms, respectively), there is still significant room for improvement in performance. Moreover, object detection methods have limitations in expressing information while semantic segmentation methods can provide more detailed and specific information [27].…”

Section: Discussionmentioning

confidence: 99%

“…However, there is currently limited research on using computer vision techniques to detect post-harvest sugar apple ripeness and the performance is still to be further improved. Sanchez et al [26] used the YOLO model to classify sugar apple ripeness and achieved 86.84% in terms of average accuracy performance. On the other hand, the object detection algorithm primarily identifies and locates objects using rectangular bounding boxes.…”

Section: Introductionmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%