Inspection of paddy seed varietal purity using machine vision and multivariate analysis

Ansari, Nadia; Ratri, Sharmin Sultana; Jahan, Afroz; Ashik-E-Rabbani, Muhammad; Rahman, Anisur

doi:10.1016/j.jafr.2021.100109

Cited by 30 publications

(26 citation statements)

References 10 publications

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“…Moreover, we compared the performance achieved by our method with some recent existing works. The accuracy of our model is 99.28% whereas the performance of Ansari et al [7] and Javanmardi et al [30] are 78.21% and 99.36% respectively. These comparisons reveal that our method is superior to the Ansari et al [7] method and almost identical to Javanmardi et al [30] VGG16 CNN method.…”

Section: Discussionmentioning

confidence: 61%

“…The accuracy of our model is 99.28% whereas the performance of Ansari et al [7] and Javanmardi et al [30] are 78.21% and 99.36% respectively. These comparisons reveal that our method is superior to the Ansari et al [7] method and almost identical to Javanmardi et al [30] VGG16 CNN method. Hence, this system can be used in both the industry and farmers' levels efficiently and effectively.…”

Section: Discussionmentioning

confidence: 61%

“…Flatten and rounded paddy shapes can be another distinguishing point. The total surrounding border describes an object's elongation, which also describes the seed is broken or not; hence, perimeter can be an important feature [7,13,34,35]. The area expresses the amount of surface within the border.…”

Section: Feature Extractionmentioning

confidence: 99%

“…We applied their techniques in our BDRICE dataset and com-pared them with our results. At first, we considered Ansari et al [7] work where they trained an SVM model by extracting twenty features: seven color, nine morphological, and four textural features. They converted input RGB image to HSV, and then applied imfill and bwareaopen for removing small objects under 100 pixels.…”

Section: Comparison With Recent Workmentioning

confidence: 99%

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Paddy seed variety identification using T20-HOG and Haralick textural features

Uddin

Islam

Shajalal

et al. 2021

Complex Intell. Syst.

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The seed is an inevitable element for agricultural and industrial production. The non-destructive paddy seed variety identification is essential to assure paddy purity and quality. This research is aimed at developing a computer vision-based system to identify paddy varieties using multiple heterogeneous features, exploiting textural, external, and physical properties. We captured the paddy seed images without any fixed setup to make the system user friendly at both industry and farmer levels, which can lead to illumination problems in the images. To overcome this problem, we introduced a modified histogram oriented gradient (T20-HOG) feature that can describe the illumination, scale, and rotational variations of a paddy image. We also utilized the existing Haralick and traditional features and the dimensionality of the features is reduced by the Lasso feature selection technique. The selected features are used to train the feed-forward neural network (FNN) to predict the paddy variety. The experiments conducted on two different datasets: BDRICE, and VNRICE. Results of our method are shown in terms of four standard evaluation metrics, namely, accuracy, precision, recall, and F_1 score, and achieved 99.28%, 98.64%, 98.48%, and 98.56% score, respectively. We also compared our system efficiency with existing studies. The experimental results demonstrate that our proposed features are effective to identify paddy variety and achieved a new state-of-the-art performance. And we also observed that our newly proposed T20-HOG features have a major impact on overall system performance.

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Section: Discussionmentioning

confidence: 61%

Section: Discussionmentioning

confidence: 61%

Section: Feature Extractionmentioning

confidence: 99%

Section: Comparison With Recent Workmentioning

confidence: 99%

See 2 more Smart Citations

Paddy seed variety identification using T20-HOG and Haralick textural features

Uddin

Islam

Shajalal

et al. 2021

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…In 2017, Huang et al analysed the key details of both ends of rice seeds and found that the identification accuracies of three rice seeds with similar appearances were 92.68%, 97.35%, and 96.57%, respectively [ 20 ]. Furthermore, in 2021, Ansari et al adopted machine vision technology combined with a multivariate analysis method to establish a rapid detection method of rice seeds based on different purities, obtaining the highest accuracy of 93.9% [ 21 ]. However, the phenomena of overlapping and superposition of external physical features are often observed owing to the similar appearances of many rice seeds.…”

Section: Introductionmentioning

confidence: 99%

Rice Seed Purity Identification Technology Using Hyperspectral Image with LASSO Logistic Regression Model

Liu

Zeng

Guiju

et al. 2021

Sensors

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Hyperspectral technology is used to obtain spectral and spatial information of samples simultaneously and demonstrates significant potential for use in seed purity identification. However, it has certain limitations, such as high acquisition cost and massive redundant information. This study integrates the advantages of the sparse feature of the least absolute shrinkage and selection operator (LASSO) algorithm and the classification feature of the logistic regression model (LRM). We propose a hyperspectral rice seed purity identification method based on the LASSO logistic regression model (LLRM). The feasibility of using LLRM for the selection of feature wavelength bands and seed purity identification are discussed using four types of rice seeds as research objects. The results of 13 different adulteration cases revealed that the value of the regularisation parameter was different in each case. The recognition accuracy of LLRM and average recognition accuracy were 91.67–100% and 98.47%, respectively. Furthermore, the recognition accuracy of full-band LRM was 71.60–100%. However, the average recognition accuracy was merely 89.63%. These results indicate that LLRM can select the feature wavelength bands stably and improve the recognition accuracy of rice seeds, demonstrating the feasibility of developing a hyperspectral technology with LLRM for seed purity identification.

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Characterization and classification of selective Indian chickpea cultivars based on physical and hydration properties, using image and multivariate analysis

Kaur

Prasad

2022

Legume Science

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Chickpea (Cicer arietinum) is an important leguminous crop, widely consumed for its high protein content, mainly in Asian countries. India is the largest chickpea‐producing country in the world. Chickpea seeds vary in shape and size depending on the cultivars. Physical properties play an important role in designing the equipment for storage and processing. Thus, the present study was carried out with the objective to characterize selected Indian chickpea cultivars based on their physical properties and hydration characteristics. Hydration is an important preprocessing treatment in most processes like germination, cooking, and fermentation, so the water uptake characteristics (water absorption capacity, swelling index, equilibrium moisture content, and weight gain) vary depending on the size and shape of the seeds. The increase in the length of chickpea seeds after hydration was found to be in the range of 18.18% to 32.08% for different cultivars, whereas the percentage increase in width ranged from 25.62% to 45.45%. The maximum increase in sphericity was 7.39%. Weight gain (%) after hydration was observed to be ranging from 98.87 ± 0.64% to 113.53 ± 0.60%. Comparative characteristics of selective chickpea cultivars based on their physical and hydration properties were studied and classify them using multivariate analysis. Principal component analysis and hierarchical cluster analysis (HCA) for multivariate data may be used to classify the chickpea cultivars. First three principal components explained the 90% variation, with PC1 and PC2 explaining the 73.03% variation. Using HCA, all the cultivars were classified into three clusters based on the similarities in their properties. The three‐dimensional (3‐D) image analysis approach was also employed to study the physical characteristics before and after soaking. It can be a helpful technique for process automation as it is less time consuming.

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Inspection of paddy seed varietal purity using machine vision and multivariate analysis

Cited by 30 publications

References 10 publications

Paddy seed variety identification using T20-HOG and Haralick textural features

Paddy seed variety identification using T20-HOG and Haralick textural features

Rice Seed Purity Identification Technology Using Hyperspectral Image with LASSO Logistic Regression Model

Characterization and classification of selective Indian chickpea cultivars based on physical and hydration properties, using image and multivariate analysis

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