Detection of early bruises on apples using hyperspectral imaging combining with YOLOv3 deep learning algorithm

Detection of early bruises on apples using hyperspectral reflectance imaging coupled with optimal wavelengths selection and improved watershed segmentation algorithm

BACKGROUND

Bruises caused by mechanical collision during the harvesting and storage and transportation period are difficult to detect using traditional machine vision technologies because there is no obvious difference in appearance between bruised and sound tissues. As a result of its fast and non-destructive characteristics, hyperspectral imaging technology is a potential tool for non-destructive detection of fruit surface defects.

RESULTS

In the present study, visible near infrared hyperspectral reflectance images of healthy apples and bruised apples at 6, 12 and 24 h were obtained. To reduce hyperspectral data dimension, optimal wavelength selection algorithms including principal component analysis (PCA) and band ratio methods were utilized to select the effective wavelengths and enhance the contrast between bruised and sound tissues. Then pseudo-color image transformation technology combining with improved watershed segmentation algorithm (IWSA) were employed to recognize the bruise spots. The result obtained showed that band ratio images obtained better detection performance than that of PCA. The G component derived from pseudo-color image ofλ 821 - λ 752 / λ 821 + λ 752 followed by IWSA obtained the best segmentation performance for bruise spots. Finally, a multispectral imaging system for the detection of bruised apple was developed to verify the effectiveness of the proposed two-band ratio algorithm, obtaining recognition rates of 93.3%, 92.2% and 92.5% for healthy, bruised and overall apples, respectively.

CONCLUSION

The bruise detection algorithm proposed in the present study has potential to detect bruised apple in online practical applications and hyperspectral reflectance imaging offers a useful reference for the detection of surficial defects of fruit. © 2023 Society of Chemical Industry.

Discrimination of Inner Injury of Korla Fragrant Pear Based on Multi-Electrical Parameters

Inner-injury fragrant pears are easily prone to rot during storage. Discriminating inner injury in the Korla fragrant pear from the normal pear is difficult as the flesh may be injured while the peel of the fruit remains intact. This study demonstrated the recognition of inner-injury pears based on their electric characteristics to pick out the inner-injury pears before storage. The electrical parameters parallel equivalent capacitance, quality factor, parallel equivalent inductance, parallel equivalent resistance, complex impedance, and phase angle were measured using the fruit electrical characteristic detection instrument. Principal component analysis and correlation analysis were used to determine the characteristic parameters, connected with the qualitative value of the fragrant pear to establish three discrimination models. When the measurement frequency was 100 kHz, compared with the Naïve Bayes and K-nearest neighbor models, the Support Vector Machine model with the characteristic parameters of quality factor, parallel equivalent resistance, and phase angle performed best. The recognition accuracy of the test set was 92.00%, the precision was 92.41%, the recall was 97.33%, and the F₁ score was 0.95. Therefore, the electrical characteristic technique effectively detected the inner injury of fragrant pears and provided a new way to distinguish the inner injury of fruits.

The knowledge domain and emerging trends in apple detection based on NIRS: A scientometric analysis with CiteSpace (1989-2021)

In this paper, 317 literature in the Web of Science (WoS) related to research on apple by near-infrared spectroscopy (NIRS) were drawn on the knowledge map of the number of literature, the co-occurrence network of authors and institutions, the co-occurrence and clustering of keywords based on CiteSpace. And a related analysis was carried out. Combined with the results of visual analysis and related literature, the research hotspots were sorted out and discussed. This paper provides a certain reference for relevant researchers to study in this field and provides a new method for macroscopically grasping the current status of apple quality detection research, which helps new researchers to quickly integrate into this field and obtain more valuable scientific information.