Open AccessAgriculture Land Classification Based on Climate Data Using Big Data Analysis
Author(s) -
M. Kumar,
S. Jyothi,
B. C. Kavitha
Publication year - 2019
Publication title -
asian journal of computer science and technology
Language(s) - English
Resource type - Journals
eISSN - 2583-7907
pISSN - 2249-0701
DOI - 10.51983/ajcst-2019.8.s3.2076
Subject(s) - grading (engineering) , agriculture , environmental science , agricultural land , climate change , soil texture , hydrology (agriculture) , physical geography , geography , soil water , soil science , geology , geotechnical engineering , civil engineering , oceanography , archaeology , engineering
The Agricultural Land Classification (ALC) provides a frame work for classifying land according to the extent at which it’s physical or chemical characteristics impose long-term limitations on agricultural use. The major physical factors that influence agricultural criteria for grading are based on their physical margins of land for agricultural use, such as climate (temperature, rainfall, aspect, exposure and frost risk), site (gradient, micro-relief and flood risk) and soil (texture, structure, depth and stoniness and chemical properties which cannot be corrected) and exchanges these factors as soil wetness, draughtiness and erosion. These factors together interact with the basis for classifying land into one of five grades, the grade or sub-grade of land being determined by the most limiting factors that can be classified into grades from 1 (excellent) to 5 (very poor). These grades are classified by using temperature and average rain fall. In this we classified Agriculture Land Classification (ALC) by using Big Data Analysis based on climatic conditions of England and Wales data.Here we analyzed England and Wales data because it has the accurate climatic grades data. These grades data is huge so we analyses the data in Big DATA analysis.