Development of a machine vision system for weedy rice seed identification
Weedy rice contamination in certified rice seed has a dramatic impact on the rice seed industry in Malaysia. To ensure the purity of the certified seed, the authorized agency (Department of Agriculture) made a manual inspection of the rice seed samples. The task is laborious and time-consuming as...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/104020/1/RASHIDAH%20BINTI%20RUSLAN%20-%20IR.pdf |
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| Summary: | Weedy rice contamination in certified rice seed has a dramatic impact on the rice seed
industry in Malaysia. To ensure the purity of the certified seed, the authorized agency
(Department of Agriculture) made a manual inspection of the rice seed samples. The task
is laborious and time-consuming as well as very subjective and error-prone as it is
influenced by the skills and experience of the operators in identifying the weedy rice
seeds within the cultivated rice seed samples. High similarities between weedy rice
morphological features and cultivated rice seed make it more challenging to separate the
weedy rice effectively. Therefore, this study was formulated to explore the possibility of
automating the manual process of distinguishing the weedy rice using a machine vision
and machine learning technique. A machine vision prototype (Patent ID: PI2018500018)
works as a platform to replace the human vision in identifying the weedy rice seed was
developed. The hardware structure configuration includes selecting a suitable imaging
system with uniform lighting and designing the seed plate and body case prototype. The
finalized prototype was installed with a moving camera attached to the front light and
equipped with imaging and features extraction software. Five cultivated rice seeds
varieties and weedy rice variants were collected from the Seed Testing Laboratory. The
monochrome and RGB images of the seed kernel were acquired using the prototype for
classification model development. Each images is comprised of 15 rice seeds acquired
on a seed plate. In total, 895 weedy rice and 7350 cultivated rice seed kernels were used.
Ninety-four features were extracted from the morphological, colour and textural
parameters. Features optimisation was done based on Stepwise Discriminant Analysis
(SDA) and Principal Component Analysis (PCA) approaches. The PCA uses features
selected from the correlation loading’s observation and PCs with the explained variances
greater than 10%. The optimised features from the two types of input image were fed to
seven machine learning classifiers and trained using a cross-validation technique using
single-parameter (RGB Morph, RGB Colour, RGB Texture, Mono Morph, Mono Grey,
Mono Texture), and three-parameter-sets (RGB MCT, Mono MGT, RGB Mono MCGT).
The models were trained using ML classifiers such as Decision Trees (DT), Discriminant
Analysis (DA), Naïve Bayes (NB), K-Nearest Neighbour (KNN), Support Vector
Machines (SVM), Ensemble Classifier (EC), and Logistic Regression (LR). The results
revealed SDA has a high percentage of features reduction than the CL plot for the single-parameter-set and a low percentage of features reduction for the three-parameters-set.
Furthermore, the SDA had higher classification performance among other optimisation
methods. For classification performance, RGB MCT dataset (combination of
morphology, colour and textural features from RGB images) modeled by the SVM
classifier had the best classification accuracy and average correct classification of 98.1%
and 93%, respectively. The RGB MCT model used nine morphology, 22 colour, and 12
texture features. The model was proven to achieve high sensitivity (97.4% to 99.8%) and
specificity (97.5% to 100%) when tested using different seeds samples. In conclusion,
this study contributed to the development of a complete laboratory-scaled machine vision
equipped with the classification model using optimised morphology, colour and texture
features. |
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