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He Wenbo, E-mail: 2023250385020@buu.edu.cn
Outline
Apples, as a high-yield fruit, often suffer significant losses during transportation and storage due to collisions and improper temperature control. Traditional manual inspection methods for apple surface damage are not only costly and inefficient but also heavily reliant on operator experience and subjective factors, leading to limited detection accuracy. To address these issues, this study proposed a cost-effective and efficient apple damage detection method based on laser speckle imaging. The proposed approach integrated the RS_FUJII speckle feature extraction algorithm with the RECONANOM-AE damage detection model to improve detection speed and accuracy. Shandong Fuji apples were used as the experimental samples. Speckle images were first preprocessed, followed by feature extraction. To overcome the limitations of the traditional FUJII algorithm in spatiotemporal feature representation and noise robustness, this study introduces a spatiotemporal weighting mechanism and robustness enhancement strategies to develop an improved RS_FUJII algorithm for high-precision damage feature extraction. In addition, an unsupervised neural network model, RECONANOM-AE, is designed to automatically identify damage regions via reconstruction error. Experimental results demonstrate that the proposed model exhibits favorable convergence and stability during training, with both training and validation errors gradually decreasing with increasing epochs. Comprehensive performance evaluation showed that the model achieved a true positive rate of 86.67% and a false positive rate of 6.67%. The overall recognition Accuracy reached 90%, with a Precision of 92.86%, Recall of 86.67%, Specificity of 93.33%, and an F1 Score of 89.68%. These results demonstrated that the proposed method achieved good training stability and strong generalization ability. The method enabled non-destructive quality assessment of apples and provided a reliable technical solution for intelligent fruit quality inspection.
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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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