2022
DOI: 10.1016/j.rsci.2022.02.003
|View full text |Cite
|
Sign up to set email alerts
|

Computer-Assisted Real-Time Rice Variety Learning Using Deep Learning Network

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2

Citation Types

0
9
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
9

Relationship

0
9

Authors

Journals

citations
Cited by 22 publications
(9 citation statements)
references
References 30 publications
0
9
0
Order By: Relevance
“…Machine learning is also instrumental in constructing pan-genomes (Golicz et al, 2016;Song et al, 2020;Bayer et al, 2021;Danilevicz et al, 2020;Torkamaneh et al, 2021;Jha et al, 2022;Ebler et al, 2022), enabling the identification of core, dispensable, and specific genes that expedite functional validation and reveal regulatory roles in genomics (Khan et al, 2020;Tay Fernandez et al, 2022;Zanini et al, 2022;Shi et al, 2023). Furthermore, ML algorithms have found applications in crop yield and complex traits prediction, crop growth monitoring, precision agriculture, and automated irrigation (Yoosefzadeh- Najafabadi et al, 2021;Yoosefzadeh-Najafabadi et al, 2022b;Jeyaraj et al, 2022;Li et al, 2022c;Croci et al, 2023). Machine learning is also used to identify genomic regions associated with specific traits (Yoosefzadeh- Najafabadi et al, 2022a) and select superior genotypes through genomic selection (Tong and Nikoloski, 2021;Yoosefzadeh-Najafabadi et al, 2022a;Jubair and Domaratzki, 2023).…”
Section: Contribution Of Machine Learning To Fast-track Breeding Effortsmentioning
confidence: 99%
See 1 more Smart Citation
“…Machine learning is also instrumental in constructing pan-genomes (Golicz et al, 2016;Song et al, 2020;Bayer et al, 2021;Danilevicz et al, 2020;Torkamaneh et al, 2021;Jha et al, 2022;Ebler et al, 2022), enabling the identification of core, dispensable, and specific genes that expedite functional validation and reveal regulatory roles in genomics (Khan et al, 2020;Tay Fernandez et al, 2022;Zanini et al, 2022;Shi et al, 2023). Furthermore, ML algorithms have found applications in crop yield and complex traits prediction, crop growth monitoring, precision agriculture, and automated irrigation (Yoosefzadeh- Najafabadi et al, 2021;Yoosefzadeh-Najafabadi et al, 2022b;Jeyaraj et al, 2022;Li et al, 2022c;Croci et al, 2023). Machine learning is also used to identify genomic regions associated with specific traits (Yoosefzadeh- Najafabadi et al, 2022a) and select superior genotypes through genomic selection (Tong and Nikoloski, 2021;Yoosefzadeh-Najafabadi et al, 2022a;Jubair and Domaratzki, 2023).…”
Section: Contribution Of Machine Learning To Fast-track Breeding Effortsmentioning
confidence: 99%
“…Furthermore, ML algorithms have found applications in crop yield and complex traits prediction, crop growth monitoring, precision agriculture, and automated irrigation (Yoosefzadeh‐Najafabadi et al, 2021; Yoosefzadeh‐Najafabadi et al, 2022b; Jeyaraj et al, 2022; Li et al, 2022c; Croci et al, 2023). Machine learning is also used to identify genomic regions associated with specific traits (Yoosefzadeh‐Najafabadi et al, 2022a) and select superior genotypes through genomic selection (Tong and Nikoloski, 2021; Yoosefzadeh‐Najafabadi et al, 2022a; Jubair and Domaratzki, 2023).…”
Section: Contribution Of Machine Learning To Fast‐track Breeding Effortsmentioning
confidence: 99%
“…The GoogleNet algorithm achieved a classification accuracy of up to 95%. Jeyaraj PR et al [17] developed a non-contact and cost-effective rice grading system based on accurate deep learning according to the appearance and characteristics of rice. Using AlexNet architecture, they obtained an average accuracy of 98.2% with 97.6% sensitivity and 96.4% specificity.…”
Section: Introductionmentioning
confidence: 99%
“…Jeyaraj et al (2022) developed a real-time scanning system to identify rice varieties. AlexNet was used to classify the images of different varieties of rice, and the classification accuracy was above 98% [ 14 ].…”
Section: Introductionmentioning
confidence: 99%