| Autor: |
Shaik, Amjan, Ansari, Nishath, Neelakantappa, M., Nimra, Amtul, Purnachand, K., Tara, Saikumar |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2023, Vol. 2477 Issue 1, p1-16, 16p |
| Abstrakt: |
Agricultures play a vital role in our day-to-day life. As the population is emerging day by day the need for agriculture and farming has become a major concern. There is a vast difference between the total population and the number of farmers available. Apart from this, the selection of appropriate seed which will be fruitful is also a major task. Due to the improper growth of few seeds, farmers face a lot of problems, waste a lot of time, and loss in the economy as well. In this project, an MLA to extract relevant features of a seed which results to knob the inconsistent targets, i.e., it decreases cardinality of features and uniqueness is preserved which helps the farmer to select that particular seed. The selected features of the seed are then applied for training and testing. 60% of data is applied for training and 40% of data is applied for testing. A total of three MLA classifiers Decision Tree, SVM, KNN to demonstrate the efficiency of the selected features of a seed. The experiment is performed on an extensive well-liked a bench mark seeds dataset is used to apply these algorithms. The seed used in this is wheat seed dataset which consist of three different varieties of wheat, they are kama, rosa and Canadian. The MLA has been applied on these datasets to knob the relevant features which describes the growth of seed and then a classification algorithm is applied to demonstrate the accuracy of the selected features. By performing this robust method, our intention is to help farmers to select a good seed for the growth of agriculture and economy of the country which saves time, energy, and cost of the farmers. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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