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

Author:

Ma Yongzheng1,Wu Zhuoyuan1,Cheng Yingying1,Chen Shihong1,Li Jianian1

Affiliation:

1. Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China

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.

Funder

National Natural Science Foundation of China

Yunnan Major Science and Technology Special Plan

Yunnan Fundamental Research Projects

Yunnan Revitalization Talent Support Program

Publisher

MDPI AG

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