2023
DOI: 10.1016/j.atech.2022.100155
|View full text |Cite
|
Sign up to set email alerts
|

Nutrients deficiency diagnosis of rice crop by weighted average ensemble learning

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
10
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
7
2

Relationship

0
9

Authors

Journals

citations
Cited by 41 publications
(10 citation statements)
references
References 28 publications
0
10
0
Order By: Relevance
“…Five-fold crossvalidation is used to avoid model over-fitting to the dataset. Figures 5, 6, and 7 depict the classification performance Ensemble approach [47] 92.00 Modified InceptionResNetV2 [48] 91.66 Modified DensNet-201 [48] 95 2), ( 3), (4), and (5).…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…Five-fold crossvalidation is used to avoid model over-fitting to the dataset. Figures 5, 6, and 7 depict the classification performance Ensemble approach [47] 92.00 Modified InceptionResNetV2 [48] 91.66 Modified DensNet-201 [48] 95 2), ( 3), (4), and (5).…”
Section: Resultsmentioning
confidence: 99%
“…Table VI compares the implemented models with the methods implemented in the literature on the same dataset used in this paper. The methods available in the literature use transfer learning and ensemble averaging methods [47], [48]. All three models implemented in this paper achieve better accuracy results than the methods available in the literature.…”
Section: Precision = T P T P + Fpmentioning
confidence: 95%
“…Finally, it adjusts them with softmax so that they always add up to 1. This mechanism has been used in previous works such as the one recently published in [ 33 ].…”
Section: Materials and Methodsmentioning
confidence: 99%
“…Then the dataset is feeded to the created stacked ensembler for final predictions that provides the highest accuracy and low loss or wrong predictions.The ensembled classifier averages the prediction probability produced for our dataset. In general, a ML/DL model is evaluated for its performance using various parameters and metrics [28]. The metrics is an indicator of the model's efficiency and thereby it helps to select the appropriate model for our task.…”
Section: Proposed Work -Deep Ensemble Learning (Incepv3dense)mentioning
confidence: 99%