Abstract
ABSTRACT Seed quality is critical to agricultural yield, and traditional testing can be time-consuming and subjective. Therefore, the use of machine learning can provide an efficient approach for predicting germination. The aim of this work was to investigate algorithms that, together with tetrazolium test data, lead to efficient prediction of soybean seed germination. The experiment was based on the collection and transcription of a database of thousand soybean seed analysis samples containing information on germination and tetrazolium tests (vigor and viability). The algorithms tested were REPTree, M5P, random forest, logistic regression, artificial neural networks and support vector machine, and the inputs tested were viability, vigor and vigor + viability (tetrazolium test) data. The data analysis used the correlation coefficient and mean absolute error as accuracy parameters of the algorithms. The results highlighted the support vector machine as the most effective algorithm for predicting germination, with the viability and vigor + viability inputs showing the best results. This study suggests that the integration of computational intelligence techniques with the tetrazolium test can make the assessment of soybean seed quality more efficient and contribute to fast and efficient decision making in agriculture.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
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Claims 3
Claim · model-written summary
Support vector machine was the most effective algorithm for predicting soybean seed germination.
- Taxon
- soybean
Glycine maxt0 - Outcome
- predicting germinatione0
- Direction
- most effectivee0
- Comparator
- support vector machinee0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“soybean seed germination”
e0“The results highlighted the support vector machine as the most effective algorithm for predicting germination”
Claim · model-written summary
Viability and combined vigor plus viability were the best inputs for predicting soybean germination.
- Taxon
- soybeanGlycine maxt0
- Outcome
- best resultse0
- Direction
- beste0
- Comparator
- viability and vigor + viability inputse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“soybean seed germination”
e0“with the viability and vigor + viability inputs showing the best results”
Claim · model-written summary
The authors suggest combining computational intelligence with tetrazolium testing can improve the efficiency of soybean seed quality assessment and agricultural decisions.
- Taxon
- soybeanGlycine maxt0
- Treatment
- integration of computational intelligence techniques with the tetrazolium teste0
- Outcome
- assessment of soybean seed qualitye0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“soybean seed germination”
e0“the integration of computational intelligence techniques with the tetrazolium test can make the assessment of soybean seed quality more efficient and contribute to fast and efficient decision making in agriculture”