An Alternative Approach to Managing the Nitrogen Content of Cereal Crops

Fertilizers are actively used in agriculture, and nitrogen is a main introduced agrochemical for cereal crops. It's necessary to study crops nitrogen status managing approaches by determining reasonable doses of fertilizer applied. The crop production process dynamic modeling method is difficult to apply in practice. In this connection, an alternative approach to control the crops nitrogen content based on the remote sensing processing is proposed. Cereal crops aerial photographs are used as the initial data of the problem under consideration. In addition, ground-based measurements can be used. Two approaches to solve this problem based on image processing are proposed. The first method is based on the analysis of plant color characteristics. In the case when in addition to the aerial photograph of crops there is a set of ground-based measurements of nitrogen content in plants, it is advisable to carry out spatial interpolation using the kriging method. It should be noted that in spite of the high accuracy of the plant nitrogen content prediction by both methods, the approach using test sites seems to be more effective.

[1]  Markus Gandorfer,et al.  A conceptual framework for judging the precision agriculture hypothesis with regard to site-specific nitrogen application , 2009, Precision Agriculture.

[2]  Ku Wang,et al.  Rapid mapping of winter wheat yield, protein, and nitrogen uptake using remote and proximal sensing , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[3]  Girish Chowdhary,et al.  Detecting In-Season Crop Nitrogen Stress of Corn for Field Trials Using UAV- and CubeSat-Based Multispectral Sensing , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[4]  A. Bhattacharyya,et al.  Monitoring of Market Fish Samples for Endosulfan and Hexachlorocyclohexane Residues in and Around Calcutta , 2001, Bulletin of Environmental Contamination and Toxicology.

[5]  G. Heuvelink,et al.  Space-time statistical analysis and modelling of nitrogen use efficiency indicators at provincial scale in China , 2020 .

[6]  J. Adinarayana,et al.  Identification of Water and Nitrogen Stress Indicative Spectral Bands Using Hyperspectral Remote Sensing in Maize During Post-Monsoon Season , 2020, Journal of the Indian Society of Remote Sensing.

[7]  W. Mirschel,et al.  Models of Hysteresis Water Retention Capacity and Their Comparative Analysis on the Example of Sandy Soil , 2018, Advances in Intelligent Systems and Computing.

[8]  Kuzyutin Denis 2017 Consrtuctive nonsmooth analysis and related topics (dedicated to the memory of V.F.Demyanov) (CNSA) , 2017 .

[9]  E. Abakumov,et al.  Predicting the scanning branches of hysteretic soil water-retention capacity with use of the method of mathematical modeling , 2017 .

[10]  Md. Wasim Aktar,et al.  Impact of pesticides use in agriculture: their benefits and hazards , 2009, Interdisciplinary toxicology.

[11]  Aleksandr Nikonorov,et al.  Modelling the Hysteretic Water Retention Capacity of Soil for Reclamation Research as a Part of Underground Development , 2016 .

[12]  V. K. Gupta,et al.  Monitoring nitrogen, phosphorus and sulphur in hybrid rice (Oryza sativa L.) using hyperspectral remote sensing , 2017, Precision Agriculture.

[13]  Eduardo Godoy de Souza,et al.  Computational routines for the automatic selection of the best parameters used by interpolation methods to create thematic maps , 2019, Comput. Electron. Agric..

[14]  G. Robertson,et al.  Nitrogen in Agriculture: Balancing the Cost of an Essential Resource , 2009 .

[15]  B. Iticha,et al.  Digital soil mapping for site-specific management of soils , 2019, Geoderma.

[16]  Guijun Yang,et al.  Quantitative analysis and hyperspectral remote sensing of the nitrogen nutrition index in winter wheat , 2020, International Journal of Remote Sensing.

[17]  S. Orlandini,et al.  Normalized Difference Vegetation Index versus Dark Green Colour Index to estimate nitrogen status on bermudagrass hybrid and tall fescue , 2019, International Journal of Remote Sensing.

[18]  Linda S. Birnbaum,et al.  Characterization of potential endocrine-related health effects at low-dose levels of exposure to PCBs. , 1999 .

[19]  W. Mirschel,et al.  Improved Hydrophysical Functions of the Soil and Their Comparison with Analogues by the Williams-Kloot Test , 2018, Advances in Intelligent Systems and Computing.

[20]  Wenshan Guo,et al.  Estimation of leaf nitrogen concentration in wheat using the MK-SVR algorithm and satellite remote sensing data , 2017, Comput. Electron. Agric..

[21]  Salah Sukkarieh,et al.  Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review , 2018, Comput. Electron. Agric..

[22]  Alireza Sharifi,et al.  Using Sentinel-2 Data to Predict Nitrogen Uptake in Maize Crop , 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[23]  V. Yakushev,et al.  Prospects for “Smart Agriculture” in Russia , 2018, Herald of the Russian Academy of Sciences.

[24]  M. Pakparvar,et al.  An approach for land suitability evaluation using geostatistics, remote sensing, and geographic information system in arid and semiarid ecosystems , 2010, Environmental monitoring and assessment.

[25]  V. Terleev,et al.  Spatial distribution prediction of agro-ecological parameter using kriging , 2020, E3S Web of Conferences.

[26]  Y. Lan,et al.  UAV-based partially sampling system for rapid NDVI mapping in the evaluation of rice nitrogen use efficiency , 2021 .

[27]  W. Mirschel,et al.  Five models of hysteretic water-retention capacity and their comparison for sandy soil , 2018 .

[28]  D. V. Rusakov,et al.  Specific and non-specific changes in optical characteristics of spring wheat leaves under nitrogen and water deficiency , 2017 .

[29]  Ruiliang Pu,et al.  An overview of crop nitrogen status assessment using hyperspectral remote sensing: Current status and perspectives , 2021 .