Edge Detection Techniques for Rice Grain Quality Analysis using Image Processing Techniques

Bernardo, Myrtel

doi:10.52631/jeet.v1i1.78

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…The quality of rice grain is categorized by the combination of physical and chemical characteristics. Grain appearance, color, size, and shape, chalkiness, whiteness, degree of milling, bulk density, foreign matter content, and moisture content are some physical characteristics, while amylose content of the endosperm, gelatinization temperature of the endosperm starch, and Na content are chemical characteristics [64]. The hst1 gene may promote grain size and perfect grain percentage in the YNU genotype under multiple HT+DS conditions.…”

Section: Discussionmentioning

confidence: 99%

The Promising B−Type Response Regulator hst1 Gene Provides Multiple High Temperature and Drought Stress Tolerance in Rice

Lopes Hornai,

Aycan,

Mitsui

2024

IJMS

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High temperatures, drought, and salt stresses severely inhibit plant growth and production due to the effects of climate change. The Arabidopsis ARR1, ARR10, and ARR12 genes were identified as negative salt and drought stress regulators. However, in rice, the tolerance capacity of the hst1 gene, which is orthologous to the ARR1, ARR10, and ARR12 genes, to drought and multiple high temperature and drought stresses remains unknown. At the seedling and reproductive stages, we investigated the drought (DS) high temperature (HT) and multiple high temperature and drought stress (HT+DS) tolerance capacity of the YNU31−2−4 (YNU) genotype, which carries the hst1 gene, and its nearest genomic relative Sister Line (SL), which has a 99% identical genome without the hst1 gene. At the seedling stage, YNU demonstrated greater growth, photosynthesis, antioxidant enzyme activity, and decreased ROS accumulation under multiple HT+DS conditions. The YNU genotype also demonstrated improved yield potential and grain quality due to higher antioxidant enzyme activity and lower ROS generation throughout the reproductive stage under multiple HT+DS settings. Furthermore, for the first time, we discovered that the B−type response regulator hst1 gene controls ROS generation and antioxidant enzyme activities by regulating upstream and downstream genes to overcome yield reduction under multiple high temperatures and drought stress. This insight will help us to better understand the mechanisms of high temperature and drought stress tolerance in rice, as well as the evolution of tolerant crops that can survive increased salinity to provide food security during climate change.

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Section: Discussionmentioning

confidence: 99%

The Promising B−Type Response Regulator hst1 Gene Provides Multiple High Temperature and Drought Stress Tolerance in Rice

Lopes Hornai,

Aycan,

Mitsui

2024

IJMS

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“…Rice grain quality and their varieties is an important factor in cultivation and import/export [16]. Manual labor and some machines work to solve these rice grain problems.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Intelligent DarkNet-SqueezeNet Architecture-Based Framework for Multiclass Rice Grain Variety Identification

Fatima

Khan

Sharif

et al. 2022

Computational Intelligence and Neuroscience

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Image processing is an important domain for identifying various crop varieties. Due to the large amount of rice and its varieties, manually detecting its qualities is a very tedious and time-consuming task. In this work, we propose a two-stage deep learning framework for detecting and classifying multiclass rice grain varieties. A series of steps is included in the proposed framework. The first step is to perform preprocessing on the selected dataset. The second step involves selecting and fine-tuning pretrained deep models from Darknet19 and SqueezeNet. Transfer learning is used to train the fine-tuned models on the selected dataset. The 50% sample images are employed for the training and rest 50% are used for the testing. Features are extracted and fused using a maximum correlation-based approach. This approach improved the classification performance; however, redundant information has also been included. An improved butterfly optimization algorithm (BOA) is proposed, in the next step, for the selection of the best features that are finally classified using several machine learning classifiers. The experimental process was conducted on selected rice datasets that include five types of rice varieties and achieves a maximum accuracy of 100% that was improved than the recent method. The average accuracy of the proposed method is obtained at 99.2%, through confidence interval-based analysis that shows the significance of this work.

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An Efficient Image Analysis Approach to Evaluate the Quality of Cotton Seeds

Keerthi¹,

Kumari²,

Chinnaiah³

et al. 2023

2023 4th International Conference for Emerging Technology (INCET)

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Edge Detection Techniques for Rice Grain Quality Analysis using Image Processing Techniques

Cited by 5 publications

References 11 publications

The Promising B−Type Response Regulator hst1 Gene Provides Multiple High Temperature and Drought Stress Tolerance in Rice

The Promising B−Type Response Regulator hst1 Gene Provides Multiple High Temperature and Drought Stress Tolerance in Rice

Two-Stage Intelligent DarkNet-SqueezeNet Architecture-Based Framework for Multiclass Rice Grain Variety Identification

An Efficient Image Analysis Approach to Evaluate the Quality of Cotton Seeds

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