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
Reference31 articles.
1. Water Fertiliser Integration Development Status and Prospects;Gao;China Agric. Inform.,2015
2. Research on Water-Fertilizer Integrated Technology Based On Neural Network Prediction and Fuzzy Control;Sun;IOP Conf. Ser. Earth Environ. Sci.,2018
3. Integrated Monitor System of Water and Fertilizer of Greenhouse Intelligent Irrigation;Cai;Jiangsu Agric. Sci.,2017
4. Internet of Things in Irrigated Agriculture: From Irrigation Automation to Smart Irrigation;Lu;J. Irrig. Drain.,2023
5. Estimating EC and Ionic EC Contribution Percentage of Nutrient Solution Based on Ionic Activity;Song;Int. J. Agric. Biol. Eng.,2019