Wheat Quality Evaluation

Jayas, Digvir S.; Paliwal, Jitendra; Erkinbaev, Chyngyz; Ghosh, P. K.; Karunakaran, Chithra

doi:10.1016/b978-0-12-802232-0.00016-5

Cited by 6 publications

(4 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The reduction in protein content was thought to be the result of the lesser availability of nutrients. The nutritional quality of the wheat seeds is dependent on the growing conditions, soil fertility, fertilizer practice, water availability, genotype, grain handling, and storage conditions (Jayas et al, 2008;Carson & Edwards, 2009;Popovic, 2015). Rahim et al (2010) and Shahbazi and Nematollahi, (2022) found that with an increase in phosphorus fertilizer, there was an increment in the protein content of wheat grains.…”

Section: Protein Content and Economic Analysis In Relation To Fertili...mentioning

confidence: 99%

A study of wheat-weed response and economical analysis to fertilization and post-emergence herbicides under arid climatic conditions

Sarita¹,

Singh²,

Mehriya

et al. 2022

Front. Agron.

View full text Add to dashboard Cite

A two-year field experiment was conducted in two consecutive rabi seasons under arid climatic conditions to examine the effect of different fertility levels and herbicides on weed dynamics and the performance of wheat (Triticum aestivum L.). Results revealed that a significantly minimum weed dry weight was recorded with 75% RDF (Recommended Dose of Fertilizer) (90-30 kg N-P2O5/ha). Application of 100% RDF (120-40 kg N-P2O5/ha) recorded significantly higher weed dry weight at 35 DAS (Days After Sowing) (16.50 g/m2) and harvest (28.15 g/m2), growth and yield attributes i.e. plant height (89.14 cm), crop dry matter accumulation (300.8 g/meter row length), crop growth rate (17.08 g/m2/day), leaf area index at 50 DAS (3.06, net assimilation rate 50-75 DAS, length of the spike (13.36 cm), number of grains/spike (41.52), grain yield (4083 kg/ha), straw yield (5019 kg/ha) and biological yield (9103 kg/ha) over 75% RDF. This treatment remains at par with 125% RDF (150-50 kg N-P2O5/ha) except for the leaf area index at 75 DAS. Among the herbicidal treatments, application of the ready-to-use herbicides clodinafop-propargyl 15% + metsulfuron methyl 1% @ 64 g/ha provided a superior value of weed indices i.e. higher weed control efficiency (91.30), crop resistance-index, and herbicide efficiency-index, and lower weed-index (1.91) over other herbicides. This treatment also exhibited significantly higher plant height (92.33 cm), crop dry matter accumulation (325.5g/meter row length), crop growth rate (16.49 g/m2/day), leaf area index (3.15), net assimilation rate, length of the spike (14.28 cm), number of spike/meter row length (153.0), number of grains/spike (44.52), grain yield (4374 kg/ha), straw yield (5381 kg/ha) and biological yield (9755 kg/ha) over weedy check plot, which was followed by sulfosulfuron 75% + metsulfuron methyl 5% @ 32 g/ha, both of which remained statistically at par with each other except for the number of grains/spike. The application of 100% RDF and clodinafop-propargyl 15% + metsulfuron methyl 1% @ 64 g/ha recorded higher net returns and maximum B: C ratio.

show abstract

Section: Protein Content and Economic Analysis In Relation To Fertili...mentioning

confidence: 99%

A study of wheat-weed response and economical analysis to fertilization and post-emergence herbicides under arid climatic conditions

Sarita¹,

Singh²,

Mehriya

et al. 2022

Front. Agron.

View full text Add to dashboard Cite

show abstract

“…Wheat quality is an extremely complex comprehensive concept ( Troccoli et al, 2000 ), and can be divided into processing quality (e.g., primary processing quality such as grinding, flour yield, bulk weight, grain hardness, flour whiteness and ash content; secondary processing quality, also known as food production quality, such as flour quality, dough quality, baking quality, cooking rate, dough formation time, stability time, sedimentation value, softening degree, evaluation value, etc.) ( Jayas et al, 2016 , Pagani et al, 2014 ), edible quality (e.g. taste and flavor of products such as bread, noodles, cakes, pastries, etc.…”

Section: Introductionmentioning

confidence: 99%

Advancements, limitations and challenges in hyperspectral imaging for comprehensive assessment of wheat quality: An up-to-date review

Wang,

Ou,

et al. 2024

Food Chemistry: X

View full text Add to dashboard Cite

“…Colour vision sensors have been used for food quality evaluation including fresh produce, and processed foods [18,21]. Jayas, Paliwal [22] utilised RGB imaging, hyperspectral imaging, X-ray, and thermal imaging for wheat grain quality evaluation. Colour sensors were successfully implemented to extract morphological, colour, and texture features to classify barley, oats, rye, red spring wheat, and amber durum wheat and the classification accuracies using neural networks were 66.8-98.2% [23].…”

Section: Introductionmentioning

confidence: 99%

Apricot Stone Classification Using Image Analysis and Machine Learning

2023

View full text Add to dashboard Cite

Apricot stones have high commercial value and can be used for manufacturing functional foods, cosmetic products, active carbon, and biodiesel. The optimal processing of the stones is dependent on the cultivar and there is a need for methods to sort among different cultivars (which are often mixed in processing facilities). This study investigates the effectiveness of two low-cost colour imaging systems coupled with supervised learning to develop classification models to determine the cultivar of different stones. Apricot stones of the cultivars ‘Bella’, ‘Early Orange’, ‘Harcot’, ‘Skierniewicka Słodka’, and ‘Taja’ were used. The RGB images were acquired using a flatbed scanner or a digital camera; and 2172 image texture features were extracted within the R, G, B; L, a, b; X, Y, Z; U, and V colour coordinates. The most influential features were determined and resulted in 103 and 89 selected features for the digital camera and the flatbed scanner, respectively. Linear and nonlinear classifiers were applied including Linear Discriminant Analysis (LDA), Decision Trees (DT), k-Nearest Neighbour (kNN), Support Vector Machines (SVM), and Naive Bayes (NB). The models resulting from the flatbed scanner and using selected features achieved an accuracy of 100% via either quadratic diagonal LDA or kNN classifiers. The models developed using images from the digital camera and all or selected features had an accuracy of up to 96.77% using the SVM classifier. This study presents novel and simple-to-implement at-line (flatbed scanner) and online (digital camera) methodologies for apricot stone sorting. The developed procedure combining colour imaging and machine learning may be used for the authentication of apricot stone cultivars and quality evaluation of apricot from sustainable production.

show abstract

Wheat Quality Evaluation

Cited by 6 publications

References 71 publications

A study of wheat-weed response and economical analysis to fertilization and post-emergence herbicides under arid climatic conditions

A study of wheat-weed response and economical analysis to fertilization and post-emergence herbicides under arid climatic conditions

Advancements, limitations and challenges in hyperspectral imaging for comprehensive assessment of wheat quality: An up-to-date review

Apricot Stone Classification Using Image Analysis and Machine Learning

Contact Info

Product

Resources

About