What is the best way to detect visible defects on peaches

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Best Methods for Detecting Visible Defects on Peaches

Detecting visible defects on peaches is crucial for maintaining fruit quality and reducing economic losses. Various advanced imaging techniques have been developed to address this challenge. This article synthesizes the most effective methods based on recent research.

Hyperspectral Imaging for Peach Defect Detection

Multispectral Detection Using Vis-NIR Hyperspectral Imaging

One of the most effective methods for detecting skin defects on bi-colored peaches is multispectral detection using visible-near infrared (vis-NIR) hyperspectral imaging. This technique involves capturing images across a range of wavelengths (400-1000 nm) and using principal component analysis (PCA) to reduce data dimensionality. Specific wavelengths (e.g., 463, 555, 687, 712, 813, 970 nm) are selected to highlight different defect types. A two-band ratio image (Q 781/848) is particularly effective in differentiating defects from normal surfaces, achieving an accuracy of 96.6% in tests.

Multivariate Analysis and Band Math

Another approach combines hyperspectral imaging with multivariate analysis and band math. This method divides defects into artificial and non-artificial categories. For artificial defects, a two-step multivariate analysis method is used to select discriminant wavelengths, followed by image processing using minimum noise fraction (MNF) transform. For non-artificial defects, characteristic wavelengths (925 nm and 726 nm) are used to construct band math equations for defect differentiation. This method achieves an overall classification accuracy of 93.3%.

Structured-Illumination Reflectance Imaging (SIRI)

Structured-illumination reflectance imaging (SIRI) is effective for detecting early fungal infections in peaches. This technique uses patterned spectral images at specific wavelengths (690-810 nm) and spatial frequencies (60, 100, 150 m⁻¹). The alternating component (AC) images and ratio images calculated from AC and direct component (DC) images are used for classification. A pixel-based convolutional neural network (CNN) applied to AC images at 730 nm and 100 m⁻¹ frequency achieves a detection rate of 98.6% for all samples and 97.6% for early decayed peaches.

Machine Vision and Image Analysis

Laboratory Machine Vision System

A laboratory machine vision system can detect and identify surface defects such as scars, cuts, bruises, and brown rot. Image analysis algorithms segment defect regions, and a classifier identifies the defect types. The system's accuracy varies, with correlation coefficients ranging from 0.56 for scale to 0.92 for brown rot. However, the classifier's error rate is relatively high, at 31% for the near-infrared system and 40% for the color system.

Chlorophyll Content-Based Hyperspectral Imaging

Detection Based on Chlorophyll Content

Hyperspectral imaging can also detect decayed peaches by analyzing chlorophyll content. Optimal wavelengths (617 nm, 675 nm, 818 nm) are selected to determine chlorophyll levels, which decrease significantly in diseased peaches. Band ratio values derived from these wavelengths classify diseased peaches with 98.75% accuracy, providing a clear spatial distribution of the affected areas.

Conclusion

In summary, hyperspectral imaging, particularly when combined with multivariate analysis and band math, offers high accuracy in detecting visible defects on peaches. Structured-illumination reflectance imaging is highly effective for early fungal infection detection. While machine vision systems provide a practical solution, their accuracy can be limited. Chlorophyll content-based hyperspectral imaging also shows promise for detecting decay. These advanced imaging techniques significantly enhance the ability to maintain peach quality and reduce post-harvest losses.