Open AccessArtificial intelligence agriculture recommendation model (AIARM)
Author(s) -
G. Jaya Lakshmi,
Shams Amer Najy Hilmi,
Ahmed J. Obaid
Publication year - 2022
Publication title -
international journal of health sciences (ijhs) (en línea)
Language(s) - English
Resource type - Journals
eISSN - 2550-6978
pISSN - 2550-696X
DOI - 10.53730/ijhs.v6ns2.5370
Subject(s) - agriculture , agricultural productivity , urbanization , industrialisation , population , productivity , agricultural economics , business , production (economics) , per capita , agribusiness , economics , geography , economic growth , market economy , demography , archaeology , sociology , macroeconomics
Agricultural production is extremely important to the global economy. Agribusiness not only provides food and raw materials, but also provides employment opportunities to a large portion of the population.Increased agricultural production and per-capita income in rural areas, combined with industrialization and urbanization, resulting in increased demand for industrial goods.According to a analysis conducted by the Food and Agriculture Organization, the world's population is projected to increase by another two billion people by 2050, while cropland is only expected to increase by 5%. As a result, to increase agricultural productivity, smart and capable farming approaches are needed. Agriculture land suitability appraisement is a required tools for agriculture advancement. The fast growth of wireless networks has resulted in the development of low-cost Internet of Things (IoT) devices that are favored as a useful methodology for agricultural autonomy and decision making. The proposed model, called the Artificial Intelligence Agriculture Recommendation Model (AIARM), incorporates sensory networks and artificial intelligence programs like neural networks and multi-layer perceptron to determine crop readiness, crop prediction, and fertilizer recommendations. Instead of a binary split, the proposed system divides agricultural land into four categories of decisions, which are fair, appropriate, fairly equitable, and not appropriate to guide farmers accurately.