Evaluation of Growth Performance of Some Rice Varieties in Relation to Their Economic Yield

Junnat, Towfia; Haque, M. N.; Hossain, Md. Sazzad; Hosain,; Nizam, Rezowana; Islam, Rafiqul

doi:10.9734/arja/2017/37394

Cited by 2 publications

(3 citation statements)

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“…Rice improvement study describes production worldwide. The rice chalkiness process is one of the main indices that valuate kernel rice [21]. The traditional and old rice chalkiness cultivation process only uses different rice as the dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Vision Approach for Identification of Four Variant Pakistani Rice Using Multi-Features Dataset

Aslam,

Muhammad Ijaz,

Ur Rehman

et al. 2023

Research Journal Of Computer Science And Information Technology

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Crops are the most important and beneficial food source in Pakistan. The demand for food has been an increase in Pakistan due to population growth. Pakistan produced 7,410 million tons of rice according to the financial year survey 2020 (FYS-2020). Pakistani rice has been cultivated in 3,304 hectares of the agricultural land zone, and it is also export around the world. Rice is also increased by 0.6% Gross Domestic Product (GDP) of Pakistan (FYS-2020). The old and manual process of rice classification is more expensive and time-consuming. In this study, we describe a machine vision approach for rice identification. We use four different varieties of rice for the experimental process such as Pakei_Kaynat, Kaynat_Kauchei, and Kauchei_Super_Banaspati and Tootaa_Kauchei (P1, P2, P3, and P4). The 100 images dataset have been used for practical work and total calculated of 400 (4 x 100) image of rice. The different process has been deploying on available datasets such as introduction, preprocessing methodology, and result discussion. A quality enhancement technique has been implementing for clarifying between rice color and shape sampling, and it is also converted color image in gray scale level. Every image has been employing six different non-overlapping regions of interest (ROI’s) and calculated a total of 2400 (6 x 400) ROI’s. Binary (B), Histogram (H) and Texture (T) features have been implemented and extract 43 features on each ROI’s and total calculated 103,200 (2400 x 43) machine learning (ML) features. Best First Search (BFS) Algorithm was used for feature optimization. Different ML classifiers are implementing for experimental process namely; Function Multi-Layer-Perception, Function SMO, Random Tree, J48 Tree, Meta Classifier via Regression and Meta Bagging. The Function Multi-Layer-Perception overall accuracy (OA) has describe better accuracy result is 99.8333%.

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Section: Literature Reviewmentioning

confidence: 99%

Machine Vision Approach for Identification of Four Variant Pakistani Rice Using Multi-Features Dataset

Aslam,

Muhammad Ijaz,

Ur Rehman

et al. 2023

Research Journal Of Computer Science And Information Technology

View full text Add to dashboard Cite

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“…The result is shown better paddy yield under RCBD by using KS-282, IR-6, NIAB-IR9, Supper Basmati, PK-5261-1-2-1, Basmati-2000, and 99512 rice varieties. Furthermore [5] , RCBD layered approaches have been implemented on different paddy crop areas such as biological, grains, parameter, straw yield, and harvest index. Data is recorded after 30, 45, 60, and 75 days of the transplanting procedure.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Rice has been cultivating in 33,304 (Hectares) of different agricultural land areas in Pakistan location or zones such as Azad Kashmir, Baluchistan, NWFP/North Area, Sindh, and Punjab [4] . The growths of crops were most important for the increase in food production [5] . The Punjab (Pakistan) produces 90.5% of overall Basmati (Oryza Sativa) rice in Pakistan, and it is very helpful in economic growth [6] .…”

Section: Introductionmentioning

confidence: 99%

Machine Vision Approach for Classification of Rice Varieties Using Texture Features

Qadri

Aslam

Nawaz

et al. 2021

International Journal of Food Properties

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The main objective of this study was to assess the machine vision (MV) techniques to classify six Asian rice varieties commonly named as Kachi-Kainat, Kachi-Toota, Kainat-Pakki, Super-Basmati-Kachi, Super-Basmati-Pakki, and Super-Maryam-Kainat (A1, A2, A3, A4, A5, and A6), mainly cultivated in Pakistan, China, India, Bangladesh, and neighboring countries. The sample of each selected rice variety contained 1800 grains, giving a total of 10800 (1800 × 6) grain samples. A cell phone camera captured the actual field digital images dataset in an open climate. All the captured images were enhanced and converted into the standard 8-bit gray-scale format. Six radius-based nonoverlapping regions of interest (ROI's) were taken on each captured image inducing a total of 3600 (6 × 600) ROI's image dataset. We have extracted Binary (B), Histogram (H), and Texture (T) features from each image. We converted these forty-three features for each image into 154800 (43 × 3600) feature vector (FV) space to discriminate rice varieties. After optimizing the FV, five MV classifiers, namely; LMT Tree (LMT-T), Meta Classifier via Regression (MCR), Meta Bagging (MB), Tree J48 (T-J48), and Meta Attribute Select Classifier (MAS-C), were deployed attaining the classification accuracies as 97.4%, 97.0%, 96.3%, 95.74%, and 95.2%, respectively. The maximum overall accuracy (MOA) observed was 97.4% by LMT-Tree.

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Evaluation of Growth Performance of Some Rice Varieties in Relation to Their Economic Yield

Cited by 2 publications

References 0 publications

Machine Vision Approach for Identification of Four Variant Pakistani Rice Using Multi-Features Dataset

Machine Vision Approach for Identification of Four Variant Pakistani Rice Using Multi-Features Dataset

Machine Vision Approach for Classification of Rice Varieties Using Texture Features

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