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Dataset for Smart Precision Agriculture System in Bangladesh
- Citation Author(s):
- Submitted by:
- Fernaz Nur
- Last updated:
- Fri, 06/21/2024 - 13:40
- DOI:
- 10.21227/5j31-8a73
- Data Format:
- License:
- Categories:
- Keywords:
Abstract
The dataset comprises many variables like area, production, season, minimum humidity, maximum humidity, minimum temperature, maximum temperature, district, crop name which impact the agricultural output of different crops in the region of Bangladesh. Surveys were conducted in various areas of Bangladesh to gather data on different types of crops. The primary aim of this collection is to facilitate research in the domain of precision agriculture. The gathered data will assist in developing algorithms and solutions that are more accurate and efficient for making data-driven judgments. This dataset was used to train different machine learning models. The trained models can provide suggestion on most efficient crop yield.
The dataset comprises many variables like area, production, season, minimum humidity, maximum humidity, minimum temperature, maximum temperature, district, crop name which impact the agricultural output of different crops in the region of Bangladesh. Surveys were conducted in various areas of Bangladesh to gather data on different types of crops. The primary aim of this collection is to facilitate research in the domain of precision agriculture. The gathered data will assist in developing algorithms and solutions that are more accurate and efficient for making data-driven judgments. This dataset was used to train different machine learning models. The trained models can provide suggestion on most efficient crop yield.
Comments
Bilgi edinip benzer bir proje geliştirmek için incelemek istedim.
Я намерен изучить запрошенный набор данных, чтобы разработать аналогичный проект.
Я намерен изучить запрошенный набор данных, чтобы разработать аналогичный проект.
Я намерен изучить запрошенный набор данных, чтобы разработать аналогичный проект.
I want to examine the data set to implement it in a machine learning project.