Computer Vision to recognize maturity level of Peach cv. Punjab Nectarine
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Date
2020
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Punjab Agricultural University, Ludhiana
Abstract
Agriculture plays a key role in India's economic growth. Many new technologies need to be develop to
place the agriculture sector at the frontline. Even now in India, human experts conduct the
maturity inspection of fruits. It is really laborious and time-consuming process. In assessing
agricultural produce, meeting quality standards and increasing market value, grading of fruits is
necessary. Computer vision system and image processing techniques have been found to be highly
useful in the food processing industry in recent years, particularly for quality check and sorting of
agriculture produces. In the proposed work, digital image processing techniques are used to detect
maturity level of Peach cv. Punjab Nectarine fruit using digital images of fruit taken at different stages
of fruit development. Various image pre-processing techniques are applied on images. K-means
clustering technique is used to divide datasets into 5 clusters and it performs color-based segmentation
that is color based ROI. It finds out number of points in an image. After that, SURF algorithm is
implemented to check what features are present in an image and then extracts that features from an
image and store them in database in the form of key points. Its accuracy rate is more as compared to
SIFT (Scale-Invariant Feature Transform) algorithm, takes less time for processing and extracts global
data from an image. It extracts features like color, size, contrast, Mean, SD, Entropy, Homogeneity,
Energy, and Correlation etc. using MATLAB (Matrix Laboratory) software. After that, the machinelearning
algorithm MSVM is applied to classify image according to maturity level of fruit using
various parameter values obtained. Results indicated that color and shape characteristics of fruit are
important for visual inspection. The fruit size and color values calculated from desktop application are
compared with manual readings to check accuracy of software. When the length and breadth computed
from application were compared with the manual readings, the error percentage was less than 2.3% and
in terms of L,a,b values the error percentage between manual and software results was less than
10.82%. Therefore, it is computed that software accurately detects maturity level of fruit with faster
speed and performance. Method used in present software that is SURF+MSVM has high accuracy rate
and fast computing speed. As per the past manual practice, maturity is calculated with a naked eye
whereas this software will help in detecting maturity with quantitative value as compared to manual
process.
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Citation
Pawanpreet Kaur (2020). Computer Vision to recognize maturity level of Peach cv. Punjab Nectarine (Unpublished M.Tech. thesis). Punjab Agricultural University, Ludhiana, Punjab, India.