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http://krishi.icar.gov.in/jspui/handle/123456789/39466
Title: | Artificial Intelligence and Digital Application in Soil Science : Potential Options for Sustainable Soil Management in Future |
Other Titles: | Not Available |
Authors: | Priyabrata Santra Mahesh Kumar Bajrang Lal Dhaka |
ICAR Data Use Licennce: | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf |
Author's Affiliated institute: | ICAR::Central Arid Zone Research Institute |
Published/ Complete Date: | 2020-01-01 |
Project Code: | CAZRI/T-01/37 |
Keywords: | Artificial Intelligence; Machine Learning; Digital Soil Mapping; Soil Information System; Handheld Device; Digital Camera |
Publisher: | SATSA, West Bengal |
Citation: | Santra, P., Kumar, M., Dhaka, B.L., 2020. Artificial intelligence and digital application in soil science: Potential options for sustainable soil management in future. SATSA Mukhapatra - Annual Technical Issue, 24, pp. 1-23. |
Series/Report no.: | Not Available; |
Abstract/Description: | Soil supports plant growth by supplying nutrients and water and thus plays a key role in agricultural production system. Therefore, sustainable management of soil resources is very important to meet the food production targets. Soil nutrient statuses are generally monitored on regular basis to apply additional nutrient requirement for plant growth through manures and fertilizers. Similarly, soil hydraulic properties e.g. hydraulic conductivity, water retention etc are to be characterized in order to apply right amount of irrigation water at right time. Conventionally, soils are characterized through field sampling followed by their laboratory analysis. However, considering the spatial variation of soil properties and time required to measure these properties in laboratory, it if often found difficult to collect multiple samples from field and then to determine soil properties in laboratory. With the advancement in digital technology specifically the artificial intelligence and machine learning tools, there is huge scope to apply these technologies to assess soil properties in field in a quick time. Here, we discusses few potential options of artificial intelligence and digital technology to apply in soil science. Digital camera can be used to prepare digital soil library and then applying machine learning tools on the large database on digital photographs may be possible to relate soil properties with colour. Machine learning tools e.g. random forest regression, support vector machines, regression tree etc. can be applied to prepare digital soil maps using legacy soil data after considering the ‘scorpan’ factors of soil formation. The available information of soil resources as well as the information generated through machine learning tools can be made available to stakeholders through soil information system in different platforms e.g. android application in smart phones, web GIS in desktops etc. Further, handheld devices may be developed to quickly measure soil properties in field. Therefore these technologies have huge potentials in agriculture and coworking robots (cobots) is a futuristic option. |
Description: | Not Available |
ISSN: | 0971-975X |
Type(s) of content: | Research Paper |
Sponsors: | ICAR |
Language: | English |
Name of Journal: | SATSA Mukhapatra - Annual Technical Issue |
Volume No.: | 24 |
Page Number: | 1-23 |
Name of the Division/Regional Station: | Division of Natural Resources |
Source, DOI or any other URL: | Not Available |
URI: | http://krishi.icar.gov.in/jspui/handle/123456789/39466 |
Appears in Collections: | NRM-CAZRI-Publication |
Files in This Item:
File | Description | Size | Format | |
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Santra et al 2020_SATSA Mukhapatra_artificial intelligence.pdf | 1.96 MB | Adobe PDF | View/Open |
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