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Open AccessArticle
Rapid Detection of Fertilizer Information Based on Near-Infrared Spectroscopy and Machine Learning and the Design of a Detection Device
by
Yongzheng Ma
Yongzheng Ma ,
Zhuoyuan Wu
Zhuoyuan Wu ,
Yingying Cheng
Yingying Cheng ,
Shihong Chen
Shihong Chen and
Jianian Li
Jianian Li *
Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
*
Author to whom correspondence should be addressed.
Agriculture 2024, 14(7), 1184; https://doi.org/10.3390/agriculture14071184 (registering DOI)
Submission received: 16 June 2024
/
Revised: 4 July 2024
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Accepted: 17 July 2024
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Published: 18 July 2024
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: HPO42−, NH4+, H2PO4− 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 HPO42−, NH4+, H2PO4−, 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.
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MDPI and ACS Style
Ma, Y.; Wu, Z.; Cheng, Y.; Chen, S.; Li, J.
Rapid Detection of Fertilizer Information Based on Near-Infrared Spectroscopy and Machine Learning and the Design of a Detection Device. Agriculture 2024, 14, 1184.
https://doi.org/10.3390/agriculture14071184
AMA Style
Ma Y, Wu Z, Cheng Y, Chen S, Li J.
Rapid Detection of Fertilizer Information Based on Near-Infrared Spectroscopy and Machine Learning and the Design of a Detection Device. Agriculture. 2024; 14(7):1184.
https://doi.org/10.3390/agriculture14071184
Chicago/Turabian Style
Ma, Yongzheng, Zhuoyuan Wu, Yingying Cheng, Shihong Chen, and Jianian Li.
2024. "Rapid Detection of Fertilizer Information Based on Near-Infrared Spectroscopy and Machine Learning and the Design of a Detection Device" Agriculture 14, no. 7: 1184.
https://doi.org/10.3390/agriculture14071184
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