Rapid Detection of Fertilizer Information Based on Near-Infrared Spectroscopy and Machine Learning and the Design of a Detection Device

Yongzheng Ma, Zhuoyuan Wu, Yingying Cheng, Shihong Chen and Jianian Li ()
Additional contact information
Yongzheng Ma: Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
Zhuoyuan Wu: Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
Yingying Cheng: Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
Shihong Chen: Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
Jianian Li: Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China

Agriculture, 2024, vol. 14, issue 7, 1-19

Abstract: The online detection of fertilizer information is pivotal for precise and intelligent variable-rate fertilizer application. However, traditional methods face challenges such as the complex quantification of multiple components and sensor-induced cross-contamination. This study investigates integrating near-infrared principles with machine learning algorithms to identify fertilizer types and concentrations. We utilized near-infrared transmission spectroscopy and applied Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), and Back-Propagation Neural Network (BPNN) algorithms to analyze full spectrum data. The BPNN model, using S-G smoothing, demonstrated a superior classification performance for the nutrient ions of four fertilizer solutions: HPO 4 2− , NH 4 + , H 2 PO 4 − and K + . Optimization using the competitive adaptive reweighted sampling (CARS) method yielded BPNN model RMSE values of 0.3201, 0.7160, 0.2036, and 0.0177 for HPO 4 2− , NH 4 + , H 2 PO 4 − , and K + , respectively. Building on this foundation, we designed a four-channel fertilizer detection device based on the Lambert–Beer law, enabling the real-time detection of fertilizer types and concentrations. The test results confirmed the device’s robust stability, achieving 93% accuracy in identifying fertilizer types and concentrations, with RMSE values ranging from 1.0034 to 2.4947, all within ±8.0% error margin. This study addresses the practical requirements for online fertilizer detection in agricultural engineering, laying the groundwork for efficient water–fertilizer integration technology aligned with sustainable development goals.

Keywords: near-infrared spectroscopy; Lambert–Beer law; machine learning; fertilizer sensors (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/7/1184/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/7/1184/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:7:p:1184-:d:1438067

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().