Head rice yield response to low and high drying and tempering conditions

Sadeghi, Morteza; Nasrnia, E.; Masoumi, A A; Hemmat, Abbas

doi:10.2478/v10247-012-0088-5

Cited by 8 publications

(4 citation statements)

References 16 publications

(15 reference statements)

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“…During milling operations, the degree of milling strongly affects head rice yield [11,12]. The degree of milling reflects how many passes are required for polishing [168].…”

Section: Factors Beyond Physiological Maturity Affecting Head Rice Yieldmentioning

confidence: 99%

“…The degree to which the aleurone layer (bran) and embryo are removed in the polishing process has implications for the quality of the milled product and depends on the milling intensity, length of time the rice is milled and grain shape. For these reasons, milling time and intensity are generally optimized for individual rice cultivars [9] to maximize head rice yield while meeting market requirements [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Current Status and Future Prospects of Head Rice Yield

et al. 2023

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Global warming poses a serious threat to food security because of its impacts on thermosensitive food crop production. Rice is of paramount significance due to the world’s three-billion-population dependence on it as a staple food. It is well established that the high temperatures at day or night times during the grain-filling period can reduce rice grain yield, although the intriguing impact of high temperatures on head rice yields (HRY) is poorly discussed. This is because high and stable HRY is vital to meet the demand for rice grain, which is a staple food for many developing and developed nations. Hence, identifying the novel heat-tolerant rice germplasm with higher head rice yields may help mitigate a critical problem threatening global food security resulting from climate change. This review addresses the key factors, including pre-and-post-harvest scenarios related to overall reductions in the HRY and how grain molecular composition can play a significant role in determining head rice yields. Moreover, the underlying genetics of head rice is discussed as and possible mechanism to breach the complexity of HRY before identifying the key alleles and genomic regions related to the reduction in the HRY. Future research should focus on understanding the mechanisms of tolerating heat stress in rice by combining modern statistical, physiological, and molecular techniques to increase HRY. This may include high-throughput phenotyping techniques, mapping quantitative loci affecting HRY loss processes and genomic prediction using a broad wild and cultivated rice germplasm.

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“…During milling operations, the degree of milling strongly affects head rice yield [11,12]. The degree of milling reflects how many passes are required for polishing [168].…”

Section: Factors Beyond Physiological Maturity Affecting Head Rice Yieldmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Current Status and Future Prospects of Head Rice Yield

et al. 2023

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“…Padi (Oryza sativa L.) yang setelah diolah menjadi beras merupakan pangan utama sebagian populasi penduduk dunia. Beras mengandung vitamin, mineral, dan amino acid (Sadeghi et al, 2013). Ada beberapa varietas unggul padi yang dikenal antara lain Batang Piaman, IR42, dan Inpara-9.…”

Section: Pendahuluanunclassified

Mesin Pembelajaran Ensemble Untuk Identifikasi Varietas Padi

Ikhlas¹,

Abdullah

Prasetyo³

2020

Informatika Pertanian

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Setiap varietas padi memiliki karakter tertentu dengan anjuran tanam berbeda. Petani umumnya kesulitan memilih varietas padi yang cocok untuk ditanam di lahan mereka karena kurangnya kemampuan identifikasi. Algoritma klasifikasi merupakan solusi mengatasi masalah ini karena mampu mengidentifikasi varietas padi melalui citra digital. Tujuan penelitian ini adalah menerapkan dan mengevaluasi beberapa algoritma klasifikasi untuk mengidentifikasi varietas padi menggunakan fitur warna dan tekstur. Penelitian dilakukan di kabupaten Indagiri Hilir Riau pada tahun 2018. Mesin pembelajaran dibangun dengan cara menggabungkan beberapa algoritma klasifikasi (classifier), yaitu Support Vector Machine, k-Nearest Neighbors, Logistic Regression, dan Decision Tree. Varietas yang diteliti adalah IR42, Inpara-9. dan Batang Piaman. Berdasarkan tingkat ketelitian masing-masing algoritma, k-Nearest Neighbors memberikan hasil lebih baik dibanding algoritma lainnya, baik dengan maupun tanpa normalisasi data. Terdapat enam sampel Inpara-9 yang diprediksi benar (true positive) dan lima sampel diprediksi salah (false positive). Pada varietas Batang Piaman terdapat delapan sampel yang diprediksi benar (true positive). Pada IR42 terdapat lima sampel yang diprediksi benar.

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“…Rice (Oryza sativa L.) is a primary source of food for energy consumed by almost half of the world population. It is an important source of vitamins, mineral elements and essential amino acids (Sadeghi et al 2013). Because it is a staple food for serving many generations and many centuries in the world, the need for its production and consumption is increasing day by day.…”

Section: Introductionmentioning

confidence: 99%

Qualitative classification of milled rice grains using computer vision and metaheuristic techniques

Zareiforoush

Minaei

Alizadeh³

et al. 2015

J Food Sci Technol

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Qualitative grading of milled rice grains was carried out in this study using a machine vision system combined with some metaheuristic classification approaches. Images of four different classes of milled rice including Low-processed sound grains (LPS), Low-processed broken grains (LPB), High-processed sound grains (HPS), and High-processed broken grains (HPB), representing quality grades of the product, were acquired using a computer vision system. Four different metaheuristic classification techniques including artificial neural networks, support vector machines, decision trees and Bayesian Networks were utilized to classify milled rice samples. Results of validation process indicated that artificial neural network with 12-5*4 topology had the highest classification accuracy (98.72 %). Next, support vector machine with Universal Pearson VII kernel function (98.48 %), decision tree with REP algorithm (97.50 %), and Bayesian Network with Hill Climber search algorithm (96.89 %) had the higher accuracy, respectively. Results presented in this paper can be utilized for developing an efficient system for fully automated classification and sorting of milled rice grains.

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Head rice yield response to low and high drying and tempering conditions

Cited by 8 publications

References 16 publications

Current Status and Future Prospects of Head Rice Yield

Current Status and Future Prospects of Head Rice Yield

Mesin Pembelajaran Ensemble Untuk Identifikasi Varietas Padi

Qualitative classification of milled rice grains using computer vision and metaheuristic techniques

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