Full
Outline
Granulation is a common post-harvest disease of citrus fruits, particularly in pomelos. When this disease occurs, it will affect the taste of pomelo fruits and even make them lose their edible value. Pomelos are large, thick-skinned fruits which are difficult to visually detect their internal granulation. The rapid, non-destructive detection of granulation of pomelos was explored by using near-infrared transmission spectroscopy combined with one-dimensional convolutional neural networks. A self-developed visible-near-infrared full-transmission spectroscopy measurement system was used to collect data from red-flashed pomelos. Data were collected in the 562.03~1 110.16 nm range under short integration mode. Without spectral preprocessing, the Kennard-Stones algorithm divided samples into training and testing sets. Subsequently, a one-dimensional convolutional neural network model for pomelo dryness identification was established. The network had a seven-layer structure. Testing showed that the model achieved a classification accuracy of 98.15% for granulation levels. Compared with traditional models such as backpropagation neural network (BPNN), kernel extreme learning machine (K-ELM), and extreme gradient boosting tree (XGBoost), which used raw spectral data and polynomial smoothing processing (SG) and competitive adaptive reweighted sampling (CARS) to extract feature variables. Finally, data augmentation further validated the model's stability. The proposed convolutional neural network model effectively identified internal granulation without complex preprocessing. It can select effective information during the spectral processing, greatly improving the model identification efficiency. The researh provided a reference for the application of deep learning methods to achieve rapid and accurate non-destructive identification of the internal granulation level of pomelo.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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