SweetFruit: A Two-Stage Mobile Sensing System for Real-Time Fruit Sugar Estimation
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866913177573261312 |
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| author | Cardamis, Mark Wang, Yanxiang Chou, Chun Tung Hu, Wen |
| author_facet | Cardamis, Mark Wang, Yanxiang Chou, Chun Tung Hu, Wen |
| contents | Accurate prediction of fruit sugar content is essential for quality control and market valuation in agriculture. Conventional measurement techniques rely on destructive, time-consuming processes (e.g., juicing and refractometry) or direct contact instruments, which hinder high-throughput operations. This paper introduces SweetFruit, a mobile two-stage system that leverages low-cost sensors to estimate fruit sugar content without contact. In Stage 1, we implement a lightweight 3D deep learning model (SF-PointNet) that uses point clouds from a Time-of-Flight (ToF) depth camera to classify fruit as high or low sugar. In Stage 2, a regression network (SF-Net) predicts the fruit's Brix value using measurements from a compact 18-channel near-infrared (NIR) spectrometer. The system uses simple off-the-shelf sensors (AS7265x NIR and Arducam ToF) with efficient processing pipelines for real-time execution on embedded platforms. Experiments on green 'Granny Smith' apples and strawberries demonstrate the system's effectiveness. Stage 1 achieves over 90% classification accuracy, enabling rapid prescreening, while Stage 2 delivers precise sugar estimates, with a root mean square error (RMSE) of 0.57 Brix, reducing error by 22% compared to using NIR sensing alone. SweetFruit offers a scalable, field-ready solution for rapid fruit quality screening, showcasing the benefits of task-specific multimodal sensing in mobile agricultural applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_01231 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SweetFruit: A Two-Stage Mobile Sensing System for Real-Time Fruit Sugar Estimation Cardamis, Mark Wang, Yanxiang Chou, Chun Tung Hu, Wen Signal Processing Emerging Technologies Accurate prediction of fruit sugar content is essential for quality control and market valuation in agriculture. Conventional measurement techniques rely on destructive, time-consuming processes (e.g., juicing and refractometry) or direct contact instruments, which hinder high-throughput operations. This paper introduces SweetFruit, a mobile two-stage system that leverages low-cost sensors to estimate fruit sugar content without contact. In Stage 1, we implement a lightweight 3D deep learning model (SF-PointNet) that uses point clouds from a Time-of-Flight (ToF) depth camera to classify fruit as high or low sugar. In Stage 2, a regression network (SF-Net) predicts the fruit's Brix value using measurements from a compact 18-channel near-infrared (NIR) spectrometer. The system uses simple off-the-shelf sensors (AS7265x NIR and Arducam ToF) with efficient processing pipelines for real-time execution on embedded platforms. Experiments on green 'Granny Smith' apples and strawberries demonstrate the system's effectiveness. Stage 1 achieves over 90% classification accuracy, enabling rapid prescreening, while Stage 2 delivers precise sugar estimates, with a root mean square error (RMSE) of 0.57 Brix, reducing error by 22% compared to using NIR sensing alone. SweetFruit offers a scalable, field-ready solution for rapid fruit quality screening, showcasing the benefits of task-specific multimodal sensing in mobile agricultural applications. |
| title | SweetFruit: A Two-Stage Mobile Sensing System for Real-Time Fruit Sugar Estimation |
| topic | Signal Processing Emerging Technologies |
| url | https://arxiv.org/abs/2606.01231 |