| Autor: |
Kassanuk, Thanwamas, Phasinam, Khongdet |
| Předmět: |
|
| Zdroj: |
AIP Conference Proceedings; 2023, Vol. 2587 Issue 1, p1-6, 6p |
| Abstrakt: |
It is imperative that agricultural productivity keep pace with population growth due to the limited supply of natural resources. The primary goal here is to increase productivity even in the face of adverse environmental conditions. As a result of advances in agricultural technology, precision farming is increasingly being used to increase yields. Machine learning encompasses a wide range of techniques that may be used to learn predictive rules from previous data and develop a model that can anticipate unknown future information. A predictive model may be built using machine learning by analyzing data samples to detect trends and creating decision rules. Smart agriculture is a modern agricultural paradigm that evaluates the farm as a collection of tiny units and identifies irregularities in output and demand for individual units. It is the ultimate objective of smart agriculture to minimize agricultural costs in order to boost profits. Farming strategies that are cutting-edge are used by smart farmers. A machine learning algorithm's ability to forecast yields makes farming more efficient and effective. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
