Soil utilisation prediction for farmers using machine learning. (26th February 2021)

• Record Type: Journal Article
• Authors: Zakir, Abdul Qadir; Singhal, Anushka; Singh, Gurkirat; Pandey, Pracheesh; Sankaranarayanan, Suresh
• Is Part Of: International journal of sustainable agricultural management and informatics. Volume 7:Number 1(2021)
• Page Start: 67
• Page End: 75
• Publication Date: 2021-02-26
• Subjects: soil sample -- soil analysis -- soil utility -- machine learning -- deep learning; Sustainable agriculture -- Management -- Periodicals; Sustainable agriculture -- Data processing -- Periodicals; Agricultural productivity -- Management -- Periodicals; Agricultural productivity -- Data processing -- Periodicals; Agricultural resources -- Data processing -- Periodicals; Agricultural systems -- Management -- Periodicals; Agricultural systems -- Data processing -- Periodicals; 338.1
• Languages: English
• ISSNs: 2054-5819

Abstract

Soil is necessary for the growth of the plant and there is a need to know the plant that can be grown. Methods are used for soil analysis by taking soil samples in the lab. Soil indicators analyse the soil remotely from the field. The literature review indicates that no work has been done using machine learning for analysing the soil features for soil utility prediction. We have done an in-depth soil utility analysis by employing deep learning and comparing it with other machine learning models for soil utility prediction resulting in the best prediction model.