An Approach for Soil Fertility Assessment Using Geospatial Data and Environmental Covariates
Article No.: 29, Pages 1 - 4
Abstract
Agriculture is one of the most important industries for the country in terms of sustainability and the economy. Agriculture practises must be improved to preserve a sustainable balance because agriculture’s importance and requirement are increasing along with the population. A vital part of the agricultural system is the soil. The accurate and thorough spatial soil information provided by soil composition detection can assist farmers in making decisions that will promote the growth of their crops. As a result, researchers concentrated on using AI and remote sensing to create models for the prediction of soil nutrient information that will aid farmers in exact fertilisation. A precise and economical solution for soil nutrient mapping is now available due to recent developments in sensor technology and machine learning-based soil analysis methods. Therefore, using machine learning (ML) techniques on remote sensing data can aid in developing a precision farming decision support system to increase crop output. For environmental modelling and risk assessment, complete and accurate geographic soil information is crucial.
References
[1]
Kishore Babu Bandamaravuri, Ashish K Nayak, Anu Sharma Bandamaravuri, and Abdul Samad. 2020. Simultaneous detection of downy mildew and powdery mildew pathogens on Cucumis sativus and other cucurbits using duplex-qPCR and HRM analysis. Amb Express 10 (2020), 1–11.
[2]
Conrad Bielski and Pierre Soille. 2007. Adaptive mosaicing: Principle and Application to the mosaicing of large image data sets. In International Conference on Adaptive and Natural Computing Algorithms. Springer, 500–507.
[3]
Chris Brunsdon, A Stewart Fotheringham, and Martin E Charlton. 1996. Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical analysis 28, 4 (1996), 281–298.
[4]
E Buis, A Veldkamp, B Boeken, and N Van Breemen. 2009. Controls on plant functional surface cover types along a precipitation gradient in the Negev Desert of Israel. Journal of Arid Environments 73, 1 (2009), 82–90.
[5]
Raffaele Casa, Fabio Castaldi, Simone Pascucci, Bruno Basso, and Stefano Pignatti. 2013. Geophysical and hyperspectral data fusion techniques for in-field estimation of soil properties. Vadose Zone Journal 12, 4 (2013).
[6]
Tao Chen, Qingrui Chang, Jing Liu, JGPW Clevers, and L Kooistra. 2016. Identification of soil heavy metal sources and improvement in spatial mapping based on soil spectral information: A case study in northwest China. Science of the total environment 565 (2016), 155–164.
[7]
Freddy A Diaz-Gonzalez, Jose Vuelvas, Carlos A Correa, Victoria E Vallejo, and D Patino. 2022. Machine learning and remote sensing techniques applied to estimate soil indicators–review. Ecological Indicators 135 (2022), 108517.
[8]
Roger Kogge Enang, Bernard Palmer Kfuban Yerima, Georges Kogge Kome, and Eric Van Ranst. 2020. Trace elements in tephra soils of mounts Kupe and Manengouba (Cameroon). Eurasian Soil Science 53, 5 (2020), 595–606.
[9]
Andrew N French, Frédéric Jacob, Martha C Anderson, William P Kustas, W Timmermans, A Gieske, Zhongbo Su, H Su, Matthew F McCabe, Fashuai Li, 2005. Surface energy fluxes with the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) at the Iowa 2002 SMACEX site (USA). Remote sensing of environment 99, 1-2 (2005), 55–65.
[10]
Guotian Hu, Kenneth A Sudduth, Dongjian He, D Brenton Myers, and Manjula V Nathan. 2016. Soil phosphorus and potassium estimation by reflectance spectroscopy. Transactions of the ASABE 59, 1 (2016), 97–105.
[11]
Lianfa Li. 2019. Geographically weighted machine learning and downscaling for high-resolution spatiotemporal estimations of wind speed. Remote Sensing 11, 11 (2019), 1378.
[12]
Huizeng Liu, Tiezhu Shi, Yiyun Chen, Junjie Wang, Teng Fei, and Guofeng Wu. 2017. Improving spectral estimation of soil organic carbon content through semi-supervised regression. Remote Sensing 9, 1 (2017), 29.
[13]
Dorota Makowska, Andrzej Strugała, Faustyna Wierońska, and Martyna Bacior. 2019. Assessment of the content, occurrence, and leachability of arsenic, lead, and thallium in wastes from coal cleaning processes. Environmental Science and Pollution Research 26 (2019), 8418–8428.
[14]
Alex B McBratney, ML Mendonça Santos, and Budiman Minasny. 2003. On digital soil mapping. Geoderma 117, 1-2 (2003), 3–52.
[15]
AM Mouazen, MR Maleki, Josse De Baerdemaeker, and Herman Ramon. 2007. On-line measurement of some selected soil properties using a VIS–NIR sensor. Soil and Tillage Research 93, 1 (2007), 13–27.
[16]
Antonio P Leone, Raphael A Viscarra-Rossel, Pietro Amenta, and Andrea Buondonno. 2012. Prediction of soil properties with PLSR and vis-NIR spectroscopy: Application to mediterranean soils from Southern Italy. Current Analytical Chemistry 8, 2 (2012), 283–299.
[17]
Abel Ramoelo, AK Skidmore, Moses A Cho, R Mathieu, IMA Heitkönig, N Dudeni-Tlhone, M Schlerf, and HHT Prins. 2013. Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data. ISPRS journal of photogrammetry and remote sensing 82 (2013), 27–40.
[18]
David P Roy, Michael A Wulder, Thomas R Loveland, Curtis E Woodcock, Richard G Allen, Martha C Anderson, Dennis Helder, James R Irons, David M Johnson, Robert Kennedy, 2014. Landsat-8: Science and product vision for terrestrial global change research. Remote sensing of Environment 145 (2014), 154–172.
[19]
Sebastian Schmidtlein, P Zimmermann, Ralf Schüpferling, and Carola Weiß. 2007. Mapping the floristic continuum: Ordination space position estimated from imaging spectroscopy. Journal of Vegetation Science 18, 1 (2007), 131–140.
[20]
Dong-Jo Seo, Chong-Hwa Park, Jong-Hyun Park, and Ryutaro Tateishi. 1998. A search for the optimum combination of spatial resolution and vegetation indices. In IGARSS’98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings.(Cat. No. 98CH36174), Vol. 3. IEEE, 1729–1731.
[21]
Vern Singhroy, Patrick Assouad, Peter Barnett, and Katrin Molch. 2003. Terrain interpretation from SAR Techniques. In IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No. 03CH37477), Vol. 1. IEEE, 106–108.
[22]
Rajesh Bahadur Thapa and Ronald C Estoque. 2012. Geographically weighted regression in geospatial analysis. In Progress in geospatial analysis. Springer, 85–96.
[23]
Muhammad Uzair, Stefania Tomasiello, Evelin Loit, and Jerry Chun Wei-Lin. 2022. Predicting the soil organic carbon by recent machine learning algorithms. In 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). IEEE, 1–7.
[24]
M Wood and AM Litterick. 2017. Soil health–What should the doctor order?Soil Use and Management 33, 2 (2017), 339–345.
[25]
Yiming Xu, Scot E Smith, Sabine Grunwald, Amr Abd-Elrahman, Suhas P Wani, and Vimala D Nair. 2018. Estimating soil total nitrogen in smallholder farm settings using remote sensing spectral indices and regression kriging. Catena 163 (2018), 111–122.
[26]
JB Yavitt, KE Harms, MN Garcia, SJ Wright, F He, and MJ Mirabello. 2009. Spatial heterogeneity of soil chemical properties in a lowland tropical moist forest, Panama. Soil Research 47, 7 (2009), 674–687.
[27]
Lei Zhang, Yanyan Cai, Haili Huang, Anqi Li, Lin Yang, and Chenghu Zhou. 2022. A CNN-LSTM model for soil organic carbon content prediction with long time series of MODIS-based phenological variables. Remote Sensing 14, 18 (2022), 4441.
Recommendations
Detection of sandy soil surfaces using ASTER-derived reflectance, emissivity and elevation data: potential for the identification of land degradation
Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) reflectance and emissivity data were used to discriminate nonphotosynthetic vegetation (NPV) from exposed soils, to produce a topsoil texture image, and to relate sand fraction ...
Determination of management zones for a tobacco field based on soil fertility
Present nutrient management recommendations for flue-cured tobacco (Nicotiana tabacum) in Central China are typically uniform for large regions. This results in over-application in areas with high nutrient levels and under-application in areas with low ...
An automated system of soil sensor-based site-specific seeding for silage maize: A proof of concept
Highlights- Automated vis-NIRS sensor-based SSS technology is developed.
- The proposed ...
AbstractDespite recent studies of map-based site-specific seeding (SSS) revealing improved agronomic and economic outcomes over uniform rate seeding (URS), to our best knowledge no visible and near infrared spectroscopy (vis-NIRS) sensor-based ...
Comments
Information & Contributors
Information
Published In
November 2023
1215 pages
ISBN:9798400709418
DOI:10.1145/3647444
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 May 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICIMMI 2023
ICIMMI 2023: International Conference on Information Management & Machine Intelligence
November 23 - 25, 2023
Jaipur, India
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 22Total Downloads
- Downloads (Last 12 months)22
- Downloads (Last 6 weeks)2
Reflects downloads up to 05 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format