Crop Health Analysis with the Help of Soil Parameters by Using ASDFieldspec4 Spectroradiometer

DOI

10.2991/978-94-6463-136-4_35How to use a DOI?

Keywords

Precision Agriculture; Crop Health Analysis; Spectral signature; Vegetation Indices; NPK; pH value; ASD FieldSpec4 Spectroradiometer; Supervised Machine Learning

Abstract

Crop health information represented through hyperspectral data is of great importance for precision agriculture. Because of the similarity in the spectral signatures of crops, discrimination of crop health using non-imaging spectral signatures is still a very challenging task for researchers. In this research work, spectral signatures are developed for soil, cotton, and maize crops from study area. Crop health is analyzed by considering of soil parameters and discriminated against Cotton and Maize crops. These all objectives prove to be essential in precision agriculture. ASD Field Spec4 for spectral signature collection has used, which has the capacity to discriminate objects in the range of 350-2500 nm. The study has carried out on various wavelength ranges or values. We have applied NDVI and CRI2 spectral vegetation indices for the analysis of spectral signature crops. Soil spectral signatures have been created and observed the N(Nitrogen), P(Phosphorus), K(potassium) and pH value of soil. Effects of various indices are studied and developed threshold values for health analysis of crops and found the relationship between soil health and crop health. Through the investigational study of results we found that NDVI and CRI2 performs well for crop health analysis. Finally Supervised machine learning algorithms SVM and KNN applied for classification of healthy and unhealthy crops in which SVM gives the result for health analysis of Maize is 90% and 87.5 for Cotton. KNN gives the accuracy of 85% for Maize and 92.5 for Cotton.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Sulochana Shejul
AU  - Pravin Dhole
AU  - Vijay Dhangar
AU  - Bharti Gawali
PY  - 2023
DA  - 2023/05/01
TI  - Crop Health Analysis with the Help of Soil Parameters by Using ASDFieldspec4 Spectroradiometer
BT  - Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
PB  - Atlantis Press
SP  - 415
EP  - 430
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-136-4_35
DO  - 10.2991/978-94-6463-136-4_35
ID  - Shejul2023
ER  -