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Review

Evolution and Application of Precision Fertilizer: A Review

by
Luxi Wang
,
Jianmin Gao
* and
Waqar Ahmed Qureshi
School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(8), 1939; https://doi.org/10.3390/agronomy15081939
Submission received: 10 July 2025 / Revised: 8 August 2025 / Accepted: 8 August 2025 / Published: 12 August 2025

Abstract

This paper reviews technological advances in precision fertilizer application from 2020 to 2025, addressing the need for a systematic synthesis of recent innovations to support agricultural sustainability. With precision fertilization critical for efficient resource use, rapid technological progress in this field has highlighted a gap in consolidated overviews of post-2020 developments. The review focuses on three core areas: device innovation, intelligent control optimization, and simulation-driven parameter refinement. Key advancements include structural improvements in fertilizer applicators (e.g., multi-segment arc and variable-diameter designs) enhancing discharge uniformity and accuracy; integration of algorithms like PSO, fuzzy logic, and RBFNN (e.g., PSO-RBF-PID reducing flow control errors) boosting control precision; and DEM/CFD simulations optimizing device parameters. These technologies, applied in scenarios from drone-based unmanned operations to automatic targeting systems, have shown potential in reducing fertilizer use and increasing crop yields. This synthesis clarifies recent progress, offering insights for green agricultural development. Note that a few pre-2020 references are included for foundational context, ensuring completeness.

1. Introduction

As a fundamental industry of the national economy, agriculture’s sustainable development is directly related to national food security and ecological balance. Cereals and their by-products, as sources of food, fertilizer, fiber, and fuel, occupy a core position in the food system [1]. Population growth has led to increasing intensity of agricultural production to meet food demand; while food output has doubled, the use of chemical fertilizers has also increased accordingly [2]. The Food and Agriculture Organization of the United Nations (FAO) predicts that by 2050, global food production will need to increase by 70% to meet demand [3]. Fertilization is crucial to agricultural productivity and food security [4]. In agricultural production, chemical fertilizers are widely used to achieve higher yields. Reducing the amount of basal fertilizer and increasing the amount of topdressing can usually improve rice yields. As a key input for increasing crop yields, the optimization of various fertilizer applicators directly affects agricultural sustainability in terms of application efficiency [5].
However, the widespread use of inorganic fertilizers has drawn global attention to green practices [6], and excessive application of chemical fertilizers has caused serious non-point source water pollution. Therefore, green development must be considered when applying chemical fertilizers. To reduce environmental pollution, new fertilizer carrier materials such as biochar have been extensively studied [7]. The global food crisis has provided impetus for the green transformation of agriculture by optimizing fertilization methods [8], and in-depth research on soil testing-based precision fertilization methods can reduce the use of chemical fertilizers and environmental pollution [9]. Due to the lack of suitable variable-rate fertilization equipment, blind manual fertilization is still prevalent, resulting in fertilizer waste and environmental pollution [10,11]. Optimal management of crop fertilization is crucial for improving crop yields and reducing fertilizer consumption [12], but a single fertilization method cannot meet the current needs of crop fertilization. The collaborative work of multiple machines in different fields is currently the optimal solution for fertilization methods.
As a key link in agricultural production, the technical level of fertilization directly affects crop yield, quality, and farmland ecosystems. While land use change and intensification contribute to increased food production, they remain one of the main threats to soil biodiversity due to their negative impact on soil health and fertility [13]. The implementation of environmentally friendly Soil Organic Matter Enhancement Technology (SOMET) is essential for addressing soil degradation [14], and soil microorganisms play a key role in maintaining the structure and function of soil ecosystems [15]. To improve the sustainability of agricultural production, full wheat straw returning to the field has been promoted; however, this practice is prone to problems such as straw blockage, poor sowing quality, and severe weeds at the seedling stage, which in turn affect maize growth [16]. Traditional fertilization methods, which rely on empirical judgments and are operated coarsely, lead to low global fertilizer utilization. The dynamics of diazotrophic communities are influenced by soil properties, and the annual economic loss caused by nitrogen fertilizer loss in China exceeds RMB 30 billion. Underground fertilization is a common strategy to improve grain yield and nitrogen utilization [17], and it is also important to understand the relationship between the contents of key nutrients such as nitrogen (N), phosphorus (P), and potassium (K) in the soil and crop yields [18]. Excessive fertilization carries high risks, so precise and appropriate fertilization in the crop root zone is necessary [19]. Rapid, non-destructive, and accurate estimation of crop production performance under different fertilization levels has become an important aspect of precision agriculture technology [20].
Fertilization is one of the most important components of precision agriculture and is crucial for ensuring high and stable crop yields [21]. It promotes the transformation of agricultural production towards intelligence and low-carbonization through precise perception, intelligent decision-making, and precise execution of the fertilization process. Monitoring of crop production has a direct impact on national and global economies and plays an important role in ensuring food security [5]. The application of modern design methods and computer tools is conducive to promoting the development of agricultural machinery and reducing research and development costs [22]. From the perspective of technological development, precision fertilization has gone through three key stages: prescription agriculture in the 1990s, which achieved differentiated fertilization at the field scale through GIS spatial interpolation [23]; real-time monitoring systems based on sensors after 2010, which improved accuracy to the “plot scale”; and intelligent control at the “plant scale” in recent years with the help of artificial intelligence and robotics [24]. These three key stages represent a fundamental shift in the agricultural production paradigm from “experience-driven” to “data-driven”.

2. Materials and Methods

2.1. Research Subject

This section focuses on the core technologies and methods involved in improving fertilization accuracy and efficiency and reducing environmental impacts, including structural innovation and parameter optimization of fertilization. Through the synergistic effect of equipment innovation, algorithm upgrading, and simulation verification, these research objects provide technical support for solving problems such as low efficiency and environmental pollution in agricultural fertilization and promote the development of fertilization technology towards precision, intelligence, greenization devices, intelligent control algorithms and system optimization, and numerical simulation and simulation methods.

2.1.1. Structural Innovation and Parameter Optimization of Fertilization Devices

As a key component of fertilizer application equipment, the fertilizer discharger has an important impact on the accuracy of the amount of fertilizer applied during the fertilizer application process [25,26]. In terms of structural innovation and parameter optimization of fertilization devices, the EDEM-CFD based double-disc fertilizer spreader (Figure 1) is a key fertilization equipment, and its application efficiency directly affects agricultural sustainability [5]. Subsequent optimization through the coupling of the discrete element method and computational fluid dynamics has further clarified the recommended rotational speed parameters.
The multi-segment arc trajectory fertilization device achieves millimeter-level tracking of the opener trajectory through segmented PID control of the electro-hydraulic system. Compared with the traditional trajectory, the fertilizer–root contact coefficient increased by 1.5 times, and the root damage rate was controlled within 8.13% [27].
It is worth mentioning that the pneumatic centralized fertilizer discharge system is an important part of pneumatic fertilizer application machinery [28,29]. In the pneumatic centralized fertilizer discharge system, the pneumatic variable-diameter precise fertilizer discharge system adopts a composite structure of “fertilizer discharge port adjustment + rotation speed control + pneumatic conveying”. Within the rotation speed range of 30–120 r/min, the fertilizer discharge accuracy reached 96.30–98.66%, and the lag time was shortened by 44% [30]. After parameter optimization, the system has a row fertilizer discharge variation coefficient of <5% during high-speed operation at 8–12 km/h [28].
The performance of centrifugal and wheel-type fertilizer discharge devices has been significantly improved after optimization. For example, under the conditions of a fertilization chamber depth of 21.6 mm and a forward speed of 3.6 km/h, the rotary centrifugal granular fertilizer applicator has a hole length variation coefficient of 8.91% and a single-hole fertilization amount error of 1.24% [31].
Through structural improvements, the notched blade spiral fertilizer discharger and the three-headed conical spiral fertilizer discharger reduced the fluctuation coefficient of fertilizer discharge uniformity to 27.01% and controlled the average flow deviation within 3.16% [32,33].
After simulation optimization, the uniformity variation coefficient of the spiral-grooved wheel fertilizer discharger was 8.56% [34]. The improved spiral staggered grooved wheel-type fertilizer discharge device provides a new idea for the optimization of agricultural machinery [35].
In addition, research on other special devices, such as the parameter optimization of the spiral fertilizer discharger in mango orchards [36] and the discrete element analysis of different groove depths and tooth thicknesses of the grooved wheel-type fertilizer discharger [37], provides references for the research and development of fertilization equipment for specific scenarios.
Table 1 and Table 2 below respectively show the three stages of precision fertilization technology and the current optimization of the precision fertilization structure.

2.1.2. Intelligent Control Algorithms and System Optimization

The application of intelligent decision-making and control strategies has achieved remarkable results in terms of intelligent control algorithms and system optimization. The side-deep variable fertilization system based on prescription maps has achieved a fertilization error of less than 5% through neural network decision-making and a dual-variable control strategy [38]. The PSO-RBF-PID algorithm reduced the maximum relative error of liquid fertilizer flow control to 2.50% and shortened the adjustment time to 2.19 s [39]. The self-calibrating variable fertilization system achieved a steady-state error of 0.13 r/min and an overshoot of 7.33% in the rotational speed response through piecewise linear interpolation [40].
The application of optimization algorithms has also improved fertilization performance. The improved BP neural network combined with the dual particle swarm optimization algorithm reduced the soil nutrient prediction error by more than 20% [41]. The Flower Pollination Optimization (FFO) algorithm outperformed 14 mainstream algorithms in 32 benchmark problems [42]. A fertilization system based on the Mariotte siphon principle, combined with a fuzzy algorithm, improved the fertilization accuracy by 15% [43]. The BP-PID system optimized by the bat algorithm achieved a flow control overshoot of 4.78% in complex environments through bionic optimization [44].

2.1.3. Numerical Simulation and Simulation Methods

In terms of numerical simulation and simulation methods, the coupled application of the Discrete Element Method (DEM) and Computational Fluid Dynamics (CFD) has effectively supported the parameter optimization of the double-disc fertilizer applicator, verifying the recommended rotational speeds of 350–400 rpm when moving downwind and 400–500 rpm when moving upwind [45,46]. Meanwhile, the independent application of the Discrete Element Method (DEM), through the analysis of the particle conveying and fertilizer discharge process of the grooved wheel fertilizer discharger [37], has provided an important basis for the optimization of the device structural parameters.

2.1.4. Auxiliary Technologies and Multi-Scenario Adaptation

In terms of auxiliary technologies and multi-scenario adaptation, the combination of tactile sensing systems and Light Detection and Ranging (LiDAR) technology has achieved 97% accuracy in crop obstacle detection, with the error of fertilizer application amount per plant controlled within 5.17% [23,47]. Additionally, the development of multi-fertilizer synchronous spreading machinery [48] has provided equipment support for collaborative fertilization in different scenarios, further enhancing the applicability and efficiency of fertilization technologies in complex agricultural environments. Some researchers have developed machines that can accommodate the simultaneous spreading of three different fertilizers [49].

2.2. Review Methodology

In this section, we will introduce the research methods adopted in this review, including literature search criteria, research keywords, and the inclusion and exclusion criteria for articles.
This review aims to systematically analyze the innovative achievements in precision fertilization control technologies both domestically and internationally, synthesize the current research progress, identify existing limitations, and provide suggestions for future research directions. This study adopts a comprehensive literature evaluation method, covering aspects such as data synthesis, inclusion and exclusion criteria, database selection, and keyword retrieval. To guide the evaluation process, the following research questions are proposed:
  • What are the recent optimizations in fertilization mechanisms?
  • What intelligent control algorithms have been integrated into recent fertilizer applicators?
  • What are the recent new types of fertilizers and specialized fertilization mechanisms?
  • What impact have intelligent fertilizer applicators had on agricultural production?
  • What limitations and challenges exist in achieving precision fertilization?
In addition, the data used in this study were derived from published research literature. A large number of academic research results have been accumulated regarding the optimization of fertilization mechanisms, application of intelligent control algorithms in fertilizer applicators, and precision fertilization technologies. Therefore, this study adopts a systematic and effective method to comprehensively evaluate the current research status of this field. To retrieve the relevant literature, the following keywords were selected for online retrieval: (“precision fertilization” or “precision fertilization mechanism”) AND (“fertilizer applicator” or “intelligent fertilizer applicator”) AND (“optimization of fertilization mechanism”) AND (“intelligent fertilization control system” or “specialized fertilization mechanism”) AND (“fertilization control system” or “variable rate fertilization control system”) AND (“fertilizer discharge optimization algorithm”). Boolean operators (AND, OR) were used to ensure the inclusion of relevant studies while reducing irrelevant results from the search. Figure 2 the retrieval strategy and the selected keywords shows the retrieval strategy and the selected keywords. To ensure the comprehensiveness of the review, an extensive search was conducted across multiple online academic databases, including the China National Knowledge Infrastructure (CNKI), Google Scholar, PubScholar, Web of Science, MDPI, ScienceDirect, and Scopus. A total of 91 studies from different countries were retrieved.

3. Results

3.1. Research Progress of Precision Fertilization Control System at Home and Abroad

3.1.1. Innovative Design of Precision Fertilization Actuators in China

Domestic scholars have made significant progress in the innovative design of precision fertilizer application actuators, which has significantly improved the uniformity and reliability of the fertilizer application device through structural optimization and material improvement. The improved bivariate fertilizer applicator by Dang et al. [50] was excellent in terms of accuracy and regulation time, but there was no significant improvement in uniformity. The authors of [51] investigated the sequence of bivariate fertilizer control and finally established an optimization model for the sequence of fertilizer discharging control based on the Differential Evolution (DE) algorithm, and the fertilizer discharging accuracy and uniformity met the operational requirements. Zhang Jiqin et al. [52] developed a dual-variable fertilizer application control system with independent control of fertilizer discharge monoblocs, constructed a control model of fertilizer discharge monoblocs through quadratic polynomial fitting, and realized the independent adjustment of fertilizer discharge in each row. The experimental results showed that the coefficient of variation of the consistency of the average fertilizer discharge in each row was 3.35%, and the accuracy of the fertilizer discharge control reached 97.6%, which was adaptable to complex fertilizer application boundary conditions. This study provides an innovative technical solution for the variable application of corn-based fertilizers.
Li Zongpeng et al. [53] designed a two-stage spiral fertilizer applicator to address the problem of uneven fertilization and low precision in the application of organic fertilizer in tea gardens, in which the fertilizer is extruded into the cavity of the fertilizer spiral through the fertilizer delivery spiral, and then the fertilizer spiral sends the fertilizer to the fertilizer tube, realizing the smooth application of organic fertilizer. The control system adopted a fuzzy PID algorithm to adjust the DC motor speed, and the bench test showed that the relative error of fertilizer application was 6.20% at most, and the coefficient of variation was 7.39% at most, which provides technical support for the variable application of organic fertilizers in tea plantations.
To meet the special needs of orchard fertilization, Zongze and Liu Gang [54] developed a machine vision-based maize localization and fertilization control system, which used a slice-type fertilizer discharger structure to realize intermittent fertilization, shorten the image processing time, and improve the robustness of the algorithm by improving the plant heart recognition and localization method. The experimental results show that the stability coefficient of variation of the amount of fertilizer applied in the three gears is less than 2%, and the average error of the accuracy of the fertilizer application position is 3.2 cm, which realizes the quantitative fertilizer application of seedling maize according to plant positioning.
Table 3 shows the situation of domestic precision fertilization actuators in recent years.

3.1.2. Innovative Design of Precision Fertilization Actuators Abroad

In the innovative design of precision fertilizer application actuators, foreign scholars have focused on the integration of intelligent sensing technology and improved the spatial and temporal precision of fertilizer application through structural innovations. Shan et al. [31] developed a rotary centrifugal granular fertilizer hole discharge device (RCGF-HDD), designed the key components through theoretical analysis, and simulated the loading and hole-forming characteristics using the discrete element method. The optimization test showed that at a fertilizer cavity depth of 21.6 mm, forward speed of 3.6 km/h, and fertilizer application rate of 5.3 g per hole, the average hole length was 72.4 mm, the coefficient of variation of hole length was 8.91%, and the error of fertilizer application rate per hole was 1.24%, which showed that the device had certain adaptability and good fertilizer agglomeration characteristics.
Dun et al. [32] designed a spiral fertilizer discharger with notched blades for pineapple orchard fertilizer application, analyzed the effects of the number and shape of periodic notched blades on the uniformity of fertilizer discharge based on the discrete element method, and determined the optimal parameter combinations through orthogonal tests. The results show that when the blade notch is fan-shaped and the periodic blade has 3 notches, the fluctuation coefficient of fertilizer discharge uniformity is 27.01%, which is 64.86 percentage points lower than that of the traditional spiral fertilizer discharger, which verifies the effectiveness of the design in improving the uniformity of fertilizer discharge. Based on the principle of half-period superposition of fertilizer discharge curves, a double arc-groove spiral fertilizer discharger was designed [55], and in order to achieve precise control of the fertilizer discharge rate, a spiral push-type fertilizer applicator was designed, which gradually squeezes the fertilizer by adopting a variable-diameter and variable-pitch structure, so as to realize the precise control of the fertilizer discharge volume [56].
Gronewold et al. [23] proposed a novel haptic perception system for autonomous navigation that detects and maps rigid obstacles, such as corn stalks, without remote sensing, while filtering out flexible features, such as leaves and weeds. The system achieved approximately 97% obstacle detection accuracy and global positioning accuracy within 4 cm by combining a mechanical probe with a rotary encoder, enabling an unmanned ground vehicle to autonomously navigate over 100 m (simulated environment) and 30 m (real environment) in a cornfield, providing a vision-independent navigation solution without precision fertilizer application for closely planted row crops.
Table 4 shows the situation of foreign precision fertilization actuators in recent years.

3.2. Domestic and Foreign Intelligent Control System Architecture and Algorithm Optimization Research Progress

3.2.1. Research Progress of Domestic Intelligent Control System Architecture and Algorithm Optimization

In terms of intelligent control system architecture and algorithm optimization, domestic research has presented a significant shift from traditional control to intelligent optimization, and a variety of advanced algorithms have been introduced into the field of fertilizer application control. The authors of [57] proposed a control system for a fertilizer application device based on microcontroller and fuzzy control to address the problems of low degree of automation control of the solid fertilizer applicator and unstable concentration of output fertilizer solution. Zhu et al. [58]. To improve the accuracy of the parameters of the precision fertilizer application control system, the system parameters were optimized based on a particle swarm optimization algorithm and a control system tuner toolbox (Figure 3). Figure 4 shows the air-assisted electric control fertilization test platform. Pan et al. [34] further proposed a liquid fertilizer variable fertilization control algorithm based on particle swarm optimization RBF-PID (PSO-RBF-PID) by establishing a closed-loop transfer function of the liquid fertilizer variable fertilization control system and using a particle swarm algorithm to optimize the key parameters of the RBF neural network. Simulation and experimental results show that the system regulation time and tracking error under this algorithm are minimized, the maximum relative error of the flow rate is 2.50%, and the average regulation time when the target flow rate changes suddenly is 2.19 s, which is significantly better than the traditional PID and RBF-PID control algorithms.
Zhu Fenglei et al. [39] designed a precision fertilizer application control system based on the bat-optimized BP-PID algorithm, using the bat algorithm to optimize the initial weights of the BP neural network, accelerating the self-learning speed of the network, and realizing the fast and precise control of the fertilizer flow rate in the water–fertilizer integration system. The experimental results show that the controller has an average maximum overshoot of 4.78% and an average regulation time of 41.24 s. The controller exhibited the best comprehensive control performance when the fertilizer flow rate was 0.6 m3/h, which effectively reduced the influence of the system time lag and nonlinearity.
To address the distribution uniformity of air-fed fertilizer discharge systems, Lei et al. [59] investigated the influence of the structure type of the distribution device on the performance of fertilizer discharge, constructed an elastic collision model of granular fertilizer and the distribution device based on the Hertz theory, and compared the performance of the flat-topped, flat-topped inverted cone, dome-type, and dome-inverted cone distribution devices through DEM-CFD gas–solid coupling analysis. The test showed that the coefficient of variation of the consistency of the fertilizer discharge of each row of fertilizer particles in the dome-type distribution device was 6.35–7.52%, and the breakage rate was 2.97–3.26%, The performance of the fertilizer discharge was better than that of the other types in general, which provided a theoretical basis for the improvement of the structure of the distribution device.
To address the lack of banded intermittent fertilization devices in standardized orchards, Hao et al. [60] designed an intermittent fertilization control system based on canopy detection of fruit trees to build an intermittent ultrasonic sensor for a fertilization control system to meet the agronomic needs of banded furrow fertilization in standardized orchards. MATLAB (MathWorks, R2020a, USA) Simulink was used to evaluate the dynamic performance of the system under different control strategies, and the results showed that the dynamic performance of the system under the fuzzy PID control strategy was optimal, and the coefficient of variation of fertilizer uniformity of the intermittent fertilization device was less than 7%. The average effective fertilizer application rate was more than 85%, and all the main indexes met the agronomic standards.
To mitigate the effects of time-varying hysteresis and nonlinearity on the fertilization system and achieve precise control of liquid conductivity, Wang et al. [61] proposed a novel hybrid optimized fractional-order proportional–integral–differential (PID) algorithm. This control algorithm has the narrowest steady-state conductivity range, shortest regulation time, and lowest overshooting amount, exhibiting excellent overall dynamic performance and providing a feasible method for the control of nonlinear time-lag systems.
For the current field liquid fertilizer targeting variable deep application of the existence of low-precision, inaccurate fertilizer application, and poor fertilization effect, Zhou et al. [62] designed a fuzzy PID algorithm based on the liquid fertilizer targeting variable fertilizer application control system; the variable fertilizer application control system, compared with the traditional PID control system based on the response time of the system is shortened by an average of nearly 5 s, and the error was controlled to within 10%. In the field test, the compliance rate of the liquid fertilizer precision variable fertilization equipment exceeded 80%, and the control accuracy of the liquid fertilizer application amount was maintained at over 90%. This study provides a feasible solution for combining precision variable fertilization and targeted fertilization.
Table 5 shows the optimization of structure and algorithm of domestic intelligent control systems in recent years.

3.2.2. Research Progress of Foreign Intelligent Control System Architecture and Algorithm Optimization

In terms of intelligent control system architecture and algorithm optimization, foreign research focuses more on multi-sensor fusion and complex system modeling, and various innovative control strategies have emerged. Albedran et al. [37] proposed the Flower Fertilization Optimization Algorithm (FFO), which is a novel bio-inspired optimization technique inspired by the natural fertilization process of flowering plants, by simulating pollen grains navigating in the search space to fertilize the ovule, balancing exploration and development mechanisms. The algorithm outperformed 14 state-of-the-art meta-heuristic algorithms on 32 benchmark optimization problems and was successfully applied to optimize the parameters of a PID controller for the positioning of a magnetic levitation train, verifying its effectiveness in complex nonlinear control problems.
Ou et al. [40] addressed the practical problems of uneven fertilizer distribution and poor particle dispersion in the operation of centrifugal double-disk fertilizer applicators by firstly conducting discrete element simulation, selecting blade inclination angle, fertilizer discharge offset angle, and spreading height as experimental factors and designing quadratic orthogonal simulation experiments with spreading width and coefficient of variation of fertilizer uniformity as evaluating indexes. The results show that the best performance was achieved when the blade inclination angle is −5°, with fertilizer discharge offset angle, the spreading height is 1050 mm, and the disc rotational speeds were—5°, 45°, 1050 mm, and 400 rpm, respectively. Combined with the coupling method of EDEM-CFD to simulate the actual operating conditions, it provides a reference for the design of fertilizer application.
Zhang et al. [63] optimized the structure of Venturi fertilizer applicator based on the head loss calculation method, with the goal of maximizing the fertilizer suction flow under the same inlet and outlet pressures, deduced the formula for calculating the head loss of the Venturi tube inlet and outlet, corrected the formula through regression analysis, and determined that the optimal ranges of the Venturi fertilizer applicator’s contraction angle and expansion angle were 20–28° and 6–10°, respectively. The optimal range of contraction angle and expansion angle of the Venturi fertilizer applicator was determined as 20–28° and 6–10°, respectively, through regression analysis, and the optimal throat diameter was 5–7 mm when the inlet flow rate was in the range of 1.5–2.5 m3/h. The optimal diameter and length of the suction pipe were equal to the diameter of the throat, which provides a theoretical basis for optimizing the design of the Venturi fertilizer applicator.
Table 6 shows the optimization of structure and algorithm of foreign intelligent control systems in recent years.

3.3. Progress in Research on Key Technologies and System Integration for Variable Fertilization at Home and Abroad

3.3.1. Research Progress on Key Technology and System Integration of Variable Fertilizer Application in Domestic Regions

As the core technology of precision fertilization, the key to variable fertilization is to realize the dynamic matching of fertilization amount with crop demand and soil conditions, and domestic scholars have conducted a lot of systematic research in this field. Wang Jinfeng et al. [33] designed a rice side-depth variable fertilization control system based on prescription map, analyzed the controllable factors affecting the amount of fertilizer discharged in the variable fertilization operation by combining with the theory of fertilizer discharging, constructed and trained the intelligent decision-making model based on a neural network, and used the soil nutrient balance method combined with the Kriging spatial interpolation to generate a fertilization prescription map. The fertilizer control accuracy test of the bivariate control model showed that the maximum error of fertilizer discharge was 3.27%, and the total average error was 1.23%, which verified the reliability of the electronic prescription map in guiding the fertilizer application operation. The bivariate control model fertilizer control accuracy test showed that the maximum discharge error was 3.27% and the total average error was 1.23%, which verified the reliability of the electronic prescription map to guide the fertilizer application operation.
Tian Min et al. [64] optimized the control process of the electric proportional valve based on the fuzzy PID of the genetic algorithm for the variable fertilizer application accuracy of the tractor-type liquid fertilizer applicator in large fields, established the negative feedback model of the liquid fertilizer variable fertilizer application control system, and simulated the fuzzy control rules for optimization through the genetic algorithm. The results show that the response time of the fuzzy PID control based on the genetic algorithm is 3.21 s, and the relative error of flow control is 1.14%, which is significantly improved compared with the traditional PID and fuzzy PID control, and it provides a feasible solution for the control of variable fertilizer application.
Gao et al. [40] developed an unmanned variable fertilizer application control system with the function of self-calibration of fertilizer discharge shaft speed (Figure 5), proposed an adaptive control strategy based on segmented linear interpolation, and constructed a model of the relationship between the speed of the fertilizer discharge shaft and the motor control signals in different speed intervals to realize the on-site self-calibration of the speed of the fertilizer discharge shaft and the precise control of the amount of fertilizer applied. The experimental results show that the average response time of the speed of the fertilizer-excluding shaft under the control of the self-calibration model is 0.40 s, the steady-state error is 0.13 rpm, the average overshoot is 7.33%, which is a significant improvement over the performance of the original linear model, and the average fertilizer-application control error is 1.91%, which meets the requirements of variable fertilizer-application operations in the field.
To solve the problems of multiple solid fertilizers not being able to be automatically mixed and blended and irrational fertilizer application amounts, to improve the utilization rate of fertilizers, and to reduce the production cost, a variable formulation fertilizer application control system based on the prescription chart was designed in a study by Niu et al. [65]. The test verified that the control accuracy of the variable formulation fertilizer application system reached more than 95%, which meets the requirements of rapid automatic formulation and precise variable fertilizer application, and has good practicability and economy.
Table 7 shows the key technologies and system integration of domestic variable fertilizer application in recent years.

3.3.2. Research Progress of Key Technology and System Integration of Variable Fertilizer Application in Overseas Countries

Variable fertilizer technology (VRT) reduces input costs, improves crop yield and quality, and helps protect the environment [66]. In terms of key technology and system integration for variable fertilizer application, foreign research focuses more on deep integration with precision agriculture platforms, realizing the intelligence of the whole process from data collection to decision-making execution. Solangi et al. [5] constructed a prediction model of arable land and fertilizer consumption in the inland area of Jiangsu Province using the distributional autoregressive lagging method to analyze its impact on crop production. In 13 years, arable land and fertilizer consumption are projected to decrease by 2.4% and 113%, respectively, while rice and wheat yields are projected to increase by 12.4% and 25.9%, respectively, indicating that increased crop yields can be achieved by reducing arable land and fertilizer inputs, which provides data support for the development of regional variable-fertilizer application strategies.
Bojja et al. [67] evaluated the effectiveness of an automated controller for fertilizing agricultural drones, which was designed to create an autonomous agricultural drone that selectively performs fertilizing, weeding, tilling, planting, planting saplings, watering, and spraying insecticides using a drone guidance system that searches for the optimal solution for the field using a conventional controller. The results show that the automatic controller can effectively regulate UAV movement to achieve more accurate and efficient fertilization, providing technical validation for the application of UAVs in precision fertilization.
Munir et al. [3] reviewed the application of smart nanoparticles in precision fertilization, exploring the design of pH-, water-, and temperature-responsive controlled-release mechanisms by encapsulating nutrients through nanoparticles to improve the efficiency of crop nutrient utilization. This approach is expected to reduce nutrient loss and greenhouse gas emissions while increasing yield and economic efficiency, providing new materials and technological directions for precision fertilization, and demonstrating the potential of nanotechnology applications in agriculture.
Table 8 shows the key technologies and system integration of foreign variable fertilizer application in recent years.

3.4. Research Progress on R&D of New Fertilizers and Specialized Fertilizer Application Systems at Home and Abroad

3.4.1. R&D Progress of New Fertilizers and Special Fertilizer Application Systems in China

Excessive use of chemical fertilizers over a long period of time may lead to soil degradation and manure and straw application is considered to be an effective way to mitigate this problem. Rasoool et al. [68] conducted a study on straw burial fertilization. The widespread use of conventional fertilizers to increase plant productivity can result in nutrient loss and environmental hazards [69]. Consumers consider organic food to be more nutritious and safer than conventional food [70]. Therefore, the research and development of special fertilization system has become another hotspot of domestic research, especially the special fertilization equipment for organic fertilizers and liquid fertilizers. Song et al [71]. designed a fuzzy PID-based control system (Figure 6) for dissolved solidification device. The authors of [71] designed a fuzzy PID-based control system for a solid fertilizer dissolving device, using STM32 as the control core, feedback fertilizer concentration using conductivity detection, and real-time adjustment of the fertilizer and water flow rates. The experiment showed that the system maintained a fertilizer concentration fluctuation range of no more than 1 g/L, and the steady-state error was approximately 0.55 g/L after perturbation, which improved the uniformity of fertilizer application in the solid fertilizer-dissolving device and provided a reference for the precise and intelligent integration of water and fertilizer. Fertilizer application using a drip irrigation system is an important means of saving fertilizer and labor. More than two-thirds of the world’s freshwater is used for irrigation [72], and fertilizer application by drip irrigation systems is an important means of saving fertilizer and labor [73]. The unwise use of water on farms in traditional irrigation methods is a concern in today’s world and can be solved by using advanced irrigation techniques to improve the application efficiency of [74]. Zhang et al. [75] investigated the mixing pattern of liquid fertilizer in irrigation pipes using a pipeline irrigation system (Figure 7) and investigated the mixing pattern of fertilizer and water in the irrigation pipes through numerical simulations and experiments to develop a strategy for the fertilizer to mix evenly with the water in the irrigation system, and the time and location required uniform mixing of fertilizer with water in the irrigation system to develop a fertilizer application strategy. The authors of [39] used the Mariotte siphon principle to improve the mixing accuracy of the nutrient solution while simplifying the structure of the nutrient solution preparation. A mixing model suitable for conductivity (EC) and acidity (pH) adjustment was constructed by combining a fuzzy control algorithm.
Jiang et al. [76] designed an agricultural biogas slurry mixing machine (Figure 8) and its system to address the problems of low precision and difficult dosage control in the application of biogas slurry as a fertilizer and designed a proportional decision-making and feedback control algorithm based on the conductivity of biogas slurry. The experimental results showed that the error in the concentration of each component of the mixed fertilizer was controlled within 10%, and the fluctuation of the conductivity was within 5% after the system was processed. The overall proportioning precision and stability were high, which provides an intelligent equipment solution for the precise application of the biogas slurry. Figure 9 shows common biogas slurry irrigation methods.
To meet the demand for precise fertilization in facility orchards, Bai et al. [77] developed an automatic target-accurate variable fertilization control system, which adopts an external groove wheel structure to realize the continuous adjustment of the groove volume, uses LIDAR sensors to detect the canopy position of fruit trees in real time, and establishes a model of the relationship between the target fertilization amount of a single tree and multivariate variables. The indoor bench test showed that the relative error of the actual fertilization amount of a single citrus tree was 5.17% at most, and the relative error of the greenhouse orchard test was 4.83% at most, realizing the target fertilization according to the canopy size of the fruit trees.
Table 9 shows the research progress of domestic new-type fertilizer development and special fertilizer application systems in recent years.

3.4.2. Research and Development Progress of New Fertilizers and Special Fertilizer Application Systems Abroad

Different fertilizers have different physical properties that affect their efficiency [78]. Therefore, the research and development of new fertilizers and specialized fertilization systems is a frontier area of precision fertilization research in foreign countries, especially the combination of functional fertilizers and intelligent-release systems. The HCl-modified HCRNF has great potential for application as a slow-release nitrogen fertilizer in sustainable green agriculture. Salt stress has become a critical issue affecting agriculture and food security in the context of climate change and a growing population. Nano-fertilizers have promising applications in salt-stress management. Bartkovský et al. [79] studied the effect of foliar spraying of humic acid-containing fertilizers on the mineral and chemical composition of Sauvignon Blanc wines from wine regions in Slovakia and found that spraying of foliar fertilizers containing humic acid and boron significantly affected the quality of the wine samples, with statistically significant increases in the P, K, and B contents of the experimental group during the bloom period, a statistically significant increase in the B and N contents of the post-fermentation wines, and a significant decrease in the Ca content. The B and N contents of the fermented wines were significantly increased, and the Ca content was significantly decreased, providing a chemical basis for the optimization of foliar fertilization technology.
Paut et al. [80] evaluated an innovative dynamic fertilization method at the farm scale that integrates in-season monitoring of wheat N nutrient status and fertilization decision rules to optimize N recovery and minimize N loss. In a 113-site year trial, the innovative method resulted in similar wheat yields, protein content, and partial margins compared to the standard “balance sheet” fertilization method, but a significant 23 kg/ha reduction in N fertilizer application and a 6-percentage-point increase in nitrogen use efficiency, demonstrating the potential of the dynamic fertilization method to improve resource-use efficiency.
Spoorthishankar S K et al. [81] developed a fertilizer prescription equation based on soil test crop response (SCTR) and validated it along with an equation for predicting post-harvest soil test values. The results of this study confirmed the accuracy of the predicted soil test values for fertilizer prescription, suggesting that repeated soil tests may not be necessary between crops. An integrated STCR-based fertilization approach achieves balanced nutrient application for optimal yield and nutrient uptake. This study highlights the effectiveness of the STCR approach for efficient nutrient management and productivity improvement in mung bean
Heidarisoltanabadi et al. [82] used multi-criteria decision making (MCDM) methods, including deterministic hierarchical analysis (AHP), fuzzy hierarchical analysis (FAHP), the ideal solution method (TOPSIS), fuzzy TOPSIS (FTOPSIS), and analytical network process (ANP), to score the appropriate fertilizer application methods for apple trees and to selection. The results showed that tractor-suspended trenchers had the highest priority, followed by fixed centerline tractor-suspended trenchers, motorized trenchers, and orchard openers, providing a scientific decision-making tool for selecting mechanized fertilization methods in orchards.
Ankush Kamb et al. [83] conducted an experiment by adjusting nitrogen and potassium fertilization programs and methods in a split-zone design with three replications. The main zone treatment consisted of two fertilizer application methods (B1 sprinkled and B2 strip), and the secondary zone treatment consisted of four application schemes of recommended nitrogen and potassium application (RDN + RDK), that is, in five (T1), six (T2), and seven splits (T3), and T4 (nitrogen in three splits and full phosphorus and potassium fertilizer in one go at the time of planting). This study aimed to determine the role of precision nutrient management in improving plant nutrient effectiveness in a widely spaced (120 cm) spring-planted sugarcane (Saccharum officinarum L.) crop.
Table 10 shows the research progress of foreign new-type fertilizer development and special fertilizer application systems in recent years.

3.5. Key Technology Analysis of Precision Fertilizer Control System

Precision fertilizer control systems are crucial in modern agricultural development, which covers multiple key levels, such as the perception layer, control algorithms and decision models, actuators, and system integration technologies. In the perception layer, sensors and detection technologies are moving towards miniaturization, integration, and intelligence. Multispectral and hyperspectral sensing technologies are used for soil nutrient detection, which can quickly determine the content of nitrogen, phosphorus, and potassium. However, the development of phosphate quantitative sensor technology has lagged behind. Crop growth state detection using chlorophyll fluorescence sensors, LiDAR, and machine vision technology, such as haptic sensing systems for fertilization robot navigation in dense crop environments, provides a new approach. Multiparameter integrated sensor nodes to achieve synchronous collection of environmental parameters, such as unmanned variable fertilization control systems, integrate a variety of working condition parameter detection and early warning technology to enhance the reliability of the system and the level of intelligence.
The innovation of control algorithms and decision-making models is at the core of precision fertilization. Intelligent optimization algorithms, such as particle swarm optimization, genetic algorithms, and bat algorithms, are widely used in controller parameter setting, which can effectively improve system performance, reduce errors, and shorten adjustment time. Model predictive control and adaptive control can deal with system time variability and uncertainty, such as the adaptive control strategy based on segmented linear interpolation to achieve self-calibration of the fertilizer discharge shaft speed, reducing the steady-state error and overshooting. Data-driven decision-making models combine multi-source data to achieve dynamic optimization of fertilizer application, neural network-based intelligent decision-making models, and soil nutrient balance methods, which can generate fertilizer prescription maps, and ARIMA models can predict the trend of arable land and fertilizer consumption.
Actuator and system integration technology directly affect the fertilizer application effect and the synergistic ability of the various components. Fertilizer discharge mechanisms have developed from the traditional method to more accurate metering, such as the three-head tapering spiral precision fertilizer application device and the rotary centrifugal granular fertilizer hole application and discharge device, which can improve the accuracy of fertilizer discharge. The air-fed fertilizer discharge system improves distribution uniformity through structural optimization, such as the dome-type distribution device and structural optimization study of the Venturi fertilizer applicator. System integration towards intelligent and unmanned development, unmanned variable fertilizer application control systems, and haptic navigation and fertilization actuator integration achieves the whole process of automation to meet the requirements of variable fertilization operation and expands application scenarios

3.5.1. Advances in Sensor and Detection Technology

The perception layer of the precision fertilization control system is the basis for realizing precision, and the progress of sensor and detection technology directly promotes upgrading of the fertilization system. Currently, sensor technology in this field is trending towards miniaturization, integration, and intelligence, which can obtain key information such as soil nutrients, crop growth, and environmental parameters in real time and accurately. Currently, multispectral sensors with hyperspectral, chlorophyll fluorescence, absolute photoelectric, and other various types of sensors are increasingly used in agricultural machinery [84].
In soil nutrient testing, multispectral and hyperspectral sensing technologies are widely used to achieve the rapid determination of nutrient content, such as nitrogen, phosphorus, and potassium, by analyzing the reflective properties of the soil at different wavelengths of light. For example, Silva et al. [4] conducted a critical review of the state-of-the-art technological capabilities of nitrogen, phosphorus, and potassium (NPK) liquid fertilizer sensors, stating that the most common technologies are colorimetry, ion-selective electrodes, photopolar, chemical sensors, and spectroscopy, with nitrate quantification being the most abundant and ion-selective electrodes being the most widely used, while the development of sensors for phosphate quantification has lagged behind. Most techniques are still at a low level of technological development and do not address matrix effects and interference in agricultural samples.
Chlorophyll fluorescence sensors, laser radar (LiDAR), and machine vision technologies have become mainstream for crop growth detection. Gronewold et al. [23] developed a haptic sensing system that does not require vision or ranging sensors and achieves high-accuracy detection and precise positioning of rigid obstacles through a combination of a mechanical probe and a rotary encoder, providing a new solution for fertilizer robotics in a densely planted crop environment and navigation in dense crop environments. The successful application of this technology in a corn field demonstrates the adaptability of non-visual perception technology to complex agricultural environments.
For environmental parameter monitoring, the multiparameter integrated sensor node realizes the simultaneous collection of soil humidity, temperature, conductivity, and other indicators. For example, the unmanned variable fertilizer application control system designed by Gao et al. [40] integrates real-time detection and early warning technology for multiple working condition parameters, including fertilizer tank balance, fertilizer pipe blockage, and operating speed. The collaborative control of unmanned tractors and variable fertilizer applicators was realized through the CAN bus communication protocol, which significantly improved the system’s reliability and intelligence level.

3.5.2. Control Algorithms and Decision-Modeling Innovations

The core of the precision fertilization control system lies in the innovation of control algorithms and decision-making models, which directly determine the regulation accuracy of fertilizer application and the system response speed. In recent years, the application of artificial intelligence algorithms, intelligent optimization algorithms, and advanced control theory in this field has made significant progress.
Intelligent optimization algorithms are widely used in controller parameter tuning, such as particle swarm optimization (PSO), the genetic algorithm (GA), and the bat algorithm (BA). The PSO-RBF-PID algorithm proposed by Chengzhong et al. [35] optimized the key parameters of the RBF neural network through a particle swarm algorithm so that the maximum relative error of the flow rate of the liquid fertilizer variable fertilization control system was reduced to 2.50%, and the regulation time was shortened to 2.19 s, which significantly improved the performance compared with the traditional control algorithm. Zhu Fenglei et al. [40] used the bat algorithm to optimize the initial weights of the BP neural network and designed a precise fertilizer application control system with an average maximum overshoot of 4.78% and an average regulation time of 41.24 s, which showed good robustness in nonlinear and time-lagged systems.
Model predictive control (MPC) and adaptive control show advantages in variable fertilizer application, which can deal with the time-varying and uncertainty of the system. The adaptive control strategy based on segmented linear interpolation proposed by Gao et al. [40] realized the on-site self-calibration of the speed of the fertilizer discharge shaft by constructing the model of the relationship between the speed of the fertilizer discharge shaft and the control signal of the motor in different rotational speed intervals, which reduced the steady state error by 0.23 r/min, and the overshoot was reduced by 1.54 percentage points, which verified the effectiveness of adaptive control in the fertilizer application system.
The data-driven decision model combined soil–crop–environment multi-source data to achieve dynamic optimization of fertilizer application. Wang Jinfeng et al. [85] used an intelligent decision model based on neural network and soil nutrient balance method, combined with Kriging spatial interpolation to generate fertilizer prescription maps, which controlled the fertilizer discharge error of rice side-depth fertilization within 3.27%, proving the value of the data-driven model in spatially varied fertilizer application. Solangi et al. [5] used an ARIMA model to predict the trend of cropland and fertilizer consumption, which provided data support for regional-scale fertilizer application decision-making. This provides data support for fertilizer application decisions at the regional scale.

3.5.3. Actuator and System Integration Technologies

The execution layer of the precision fertilizer control system is key to decision-making. The precision, reliability, and adaptability of the actuator directly affect the fertilizer application effect, whereas system integration technology determines the synergistic working ability of the components. The innovative design of the fertilizer discharge mechanism has always been a research hotspot, from the traditional external grooved wheel type and spiral type to more accurate metering. The authors of [32,86] designed a three-headed tapering spiral precision fertilizer application device, in which the three-headed tapering spiral structure gradually compressed fertilizer particles to achieve a completely filled cavity and evenly discharged particles. The test showed that the average deviation of the discharged flow rate from the preset value was 3.16%, which provides a new method for optimizing spiral-type pumps. Shan et al.’s [31] rotary centrifugal granular fertilizer hole-applied discharge device achieved an error of 1.24% in the amount of fertilizer applied to each hole through discrete element simulation and experimental optimization, which meets the requirement of high accuracy in hole-applied fertilizer application.
The distribution uniformity of air-fed fertilizer discharge systems has received continuous attention, and structural optimization is an effective method for improving performance. Wang Lei et al. [59] compared four distribution device structures through DEM-CFD gas–solid coupling analysis and found that the dome-type distribution device had the lowest coefficient of variation for the uniformity of fertilizer discharge (6.35–7.52%) and the smallest breakage rate (2.97–3.26%), which provided a theoretical basis for the structural design of the air-fed fertilizer discharging system. Zhang et al. [63] conducted structural optimization studies on the Venturi fertilizer applicator. The structural optimization study of the fertilizer applicator determined the optimal contraction angle, expansion angle, and throat diameter to maximize the suction flow rate, which provided a reference for the design of key components of the liquid fertilizer application system.
The development of system integration technology towards intelligent and unmanned directions realizes the whole process automation of sensing–decision–execution. The unmanned variable fertilizer application control system developed by Gao et al. [36] integrates the self-calibration of the fertilizer-discharge axle rotational speed, multiparameter real-time detection, and bus communication protocols. Field tests showed that the average fertilizer application control error was 1.91%, which meets the requirements of variable fertilizer application. The integration of the haptic navigation system and fertilizer application actuator of Gronewold et al. [23] expanded the application scenarios of the system and integrated the haptic navigation system with the fertilizer application actuator, which enables the unmanned ground vehicle to realize precise fertilizer application under visionless conditions and expands the application scenarios of the system.
The following Figure 10 shows the analysis of key technologies of precision fertilizer control systems at home and abroad in recent years.

4. Discussion

The review of research progress in precision fertilization control systems at home and abroad reveals distinct technological pathways, practical applications, and unresolved challenges, which collectively shape the current landscape and future directions of precision agriculture. This section connects the findings to broader agricultural, environmental, and technological implications; highlights critical research gaps; and contextualizes the practical significance of the observed trends.

4.1. Technological Divergence and Convergence in Precision Fertilization

Domestic research has demonstrated a strong focus on scenario-specific innovation, with advancements tailored to staple crops (e.g., corn), specialty agriculture (e.g., tea plantations, orchards), and organic fertilizer application. For instance, dual-variable fertilizer application systems with row-independent control (Zhang Jiqin et al., [52]) and two-stage spiral fertilizer dischargers for tea gardens (Li Zongpeng et al., [53]) address the unique needs of complex agronomic environments, emphasizing structural optimization and algorithmic adaptation (e.g., fuzzy PID) for reliability and uniformity. These technologies prioritize ease of implementation and cost-effectiveness, aligning with China’s diverse agricultural landscapes and smallholder-dominated farming systems.
In contrast, foreign research leans toward intelligent sensing integration and interdisciplinary fusion, such as rotary centrifugal granular fertilizer devices optimized via discrete element simulation (Shan et al., [31]) and haptic perception systems for autonomous navigation in dense crops (Gronewold et al., [23]). These innovations enhance spatiotemporal precision by combining mechanical design with advanced sensing (e.g., machine vision, tactile feedback) and complex system modeling, reflecting a focus on large-scale, high-tech agricultural operations.
Notably, both regions converge on the goal of improving fertilizer use efficiency (FUE) and reducing environmental impact. Domestic studies achieve this through adaptive algorithms (e.g., PSO-RBF-PID, [34]) and targeted fertilization (e.g., orchard systems based on canopy detection, [77]), while foreign research emphasizes dynamic decision-making (e.g., in-season N monitoring, [80]) and nanotechnology-enabled controlled release (Munir et al., [3]). This convergence underscores the global imperative to balance agricultural productivity with sustainability.

4.2. Practical Implications for Agricultural Systems

The reviewed technologies have tangible implications for on-farm management and policy.
  • Increased precision and resource efficiency: Systems like the fuzzy PID-based liquid fertilizer control (Zhou et al., [62]) and bat-optimized BP-PID algorithms (Zhu Fenglei et al., [39]) reduce fertilizer application errors to <10% and <5%, respectively, minimizing nutrient runoff and lowering input costs. For example, Pan et al.’s [34] PSO-RBF-PID system achieves a maximum flow error of 2.50%, translating to significant savings for large-scale farms.
  • Adaptation to diverse cropping systems: Domestic innovations in organic fertilizer application (Li Zongpeng et al., [53]) and orchard-specific devices (Zongze and Liu Gang, [54]) address the needs of high-value, labor-intensive crops, while foreign drone-based automated fertilization (Bojja et al., [67]) and variable-rate technologies (VRT) cater to large-field monocultures. This diversity highlights the importance of context-specific technology transfer.
  • Enabling data-driven agriculture: Prescription map-based systems (Wang Jinfeng et al., [33]) and soil test crop response (STCR) models (Spoorthishankar et al., [81]) facilitate evidence-based decision-making, reducing over-fertilization and improving crop quality. For instance, STCR-derived fertilizer prescriptions achieve balanced nutrient uptake in mung beans, enhancing yields without excess inputs.

4.3. Key Challenges and Research Gaps

Despite progress, critical gaps remain.
  • Sensor limitations: While multispectral and hyperspectral sensors effectively detect nitrogen, phosphorus-sensing technology lags (Silva et al., [4]), hindering comprehensive nutrient management. Additionally, non-visual perception systems (e.g., haptic navigation, [23]) are costly and require further miniaturization for widespread adoption.
  • Complexity in system integration: Domestic unmanned variable fertilization systems (Gao et al., [40]) and foreign multi-sensor fusion platforms face challenges in synchronizing data from diverse sources (e.g., soil, crop, weather), leading to delays or inaccuracies in real-time decision-making.
  • Scalability for smallholder farms: Many advanced technologies (e.g., nanotechnology-enabled fertilizers [3] and drone-based systems [67]) are prohibitively expensive for small-scale farmers, particularly in developing regions. Domestic efforts to prioritize “easy-to-land” technologies offer a model, but broader affordability remains a barrier.
  • Long-term environmental impacts: While controlled-release fertilizers and dynamic fertilization methods reduce immediate runoff, their long-term effects on soil microbial communities and groundwater quality are understudied. For example, the ecological risks of nano-fertilizers (Munir et al., [3]) require further assessment.

5. Conclusions

Precision fertilization control systems have advanced significantly, driven by innovations in actuators, algorithms, and sensor integration, with distinct regional priorities but a shared goal of sustainable productivity. Domestic research excels in scenario-specific, cost-effective solutions for diverse crops, while foreign studies lead in intelligent sensing and interdisciplinary integration. These technologies directly enhance fertilizer use efficiency, reduce environmental impact, and adapt to varied agricultural systems, with proven applications in both large-scale and smallholder contexts.
However, challenges persist: sensor limitations for phosphorus detection, high integration complexity, scalability barriers for small farms, and understudied long-term environmental effects. Addressing these gaps requires the following:
  • Investment in next-gen sensors: Developing low-cost, multi-nutrient sensors (especially for phosphorus) and miniaturized non-visual perception tools.
  • Simplified system integration: Standardizing communication protocols (e.g., CAN bus) to enable seamless data flow between sensors, controllers, and actuators.
  • Inclusive innovation: Designing affordable technologies for smallholders, such as simplified prescription map tools and low-cost adaptive algorithms.
  • Long-term ecological studies: Evaluating the impacts of novel fertilizers (e.g., nanomaterials) and precision systems on soil health and biodiversity.
By addressing these areas, precision fertilization can fulfill its potential as a cornerstone of sustainable agriculture, balancing productivity with environmental stewardship across global farming systems.

Author Contributions

Conceptualization, L.W. and J.G.; Methodology, W.A.Q. and L.W.; Formal Analysis, L.W.; Writing—Original Draft Preparation, L.W.; Writing—Review and Editing, L.W. and W.A.Q.; Visualization, L.W.; Supervision, J.G.; Project Administration, J.G.; Funding Acquisition, J.G. All authors have read and agreed to the published version of the manuscript.

Funding

The Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (Grant No. PAPD-2018-87).

Data Availability Statement

No new data were created or analyzed in this study.

Conflicts of Interest

The authors declare no conflicts of interest, financial or personal, that could have influenced the research or outcomes presented in this paper.

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Figure 1. Prototype of electric-disc fertilizer spreader.
Figure 1. Prototype of electric-disc fertilizer spreader.
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Figure 2. The retrieval strategy and the selected keywords.
Figure 2. The retrieval strategy and the selected keywords.
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Figure 3. Structure diagram of an air-assisted electronic fertilizer discharge system. 1. Conveying fan; 2. wind speed sensor; 3. fertilizer box; 4. support mechanism; 5. Venturi fertilizer apparatus; 6. fertilizer discharge shaft; 7. transmission chain; 8. fertilizer discharge stepper motor; 9. driver; 10. stepper motor controller; 11. terminal controller; 12. furrow opener; 13. fertilizer pipeline.
Figure 3. Structure diagram of an air-assisted electronic fertilizer discharge system. 1. Conveying fan; 2. wind speed sensor; 3. fertilizer box; 4. support mechanism; 5. Venturi fertilizer apparatus; 6. fertilizer discharge shaft; 7. transmission chain; 8. fertilizer discharge stepper motor; 9. driver; 10. stepper motor controller; 11. terminal controller; 12. furrow opener; 13. fertilizer pipeline.
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Figure 4. Air-assisted electric control fertilization test platform. 1. Conveying fan; 2. fertilizer box; 3. support mechanism; 4. Venturi fertilizer apparatus; 5. transmission chain; 6. fertilizer pipeline; 7. fertilizer discharge stepper motor.
Figure 4. Air-assisted electric control fertilization test platform. 1. Conveying fan; 2. fertilizer box; 3. support mechanism; 4. Venturi fertilizer apparatus; 5. transmission chain; 6. fertilizer pipeline; 7. fertilizer discharge stepper motor.
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Figure 5. Unmanned variable-rate fertilization control system.
Figure 5. Unmanned variable-rate fertilization control system.
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Figure 6. Schematic diagram of the device for dissolving and applying solid fertilizer. (1) Hopper; (2) screw rod; (3) stepping motor; (4) direct-current motor; (5) mixer; (6) EC sensor; (7) fertilizer solution outlet pipe; (8) control cabinet; (9) flow sensor; (10) water inlet pipe; (11) direct-current pump; (12) filter; (13) mixing barrel; (14) sewage outlet pipe.
Figure 6. Schematic diagram of the device for dissolving and applying solid fertilizer. (1) Hopper; (2) screw rod; (3) stepping motor; (4) direct-current motor; (5) mixer; (6) EC sensor; (7) fertilizer solution outlet pipe; (8) control cabinet; (9) flow sensor; (10) water inlet pipe; (11) direct-current pump; (12) filter; (13) mixing barrel; (14) sewage outlet pipe.
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Figure 7. Pipeline irrigation system: (1) water tank, (2) centrifugal pump, (3) fertilizer injection pipe, (4) flowmeter, (5) sampling point, (6) PE transparent pipe, (7) plunger pump, and (8) fertilizer.
Figure 7. Pipeline irrigation system: (1) water tank, (2) centrifugal pump, (3) fertilizer injection pipe, (4) flowmeter, (5) sampling point, (6) PE transparent pipe, (7) plunger pump, and (8) fertilizer.
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Figure 8. Physical diagram of the device system.
Figure 8. Physical diagram of the device system.
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Figure 9. Common biogas slurry irrigation methods.
Figure 9. Common biogas slurry irrigation methods.
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Figure 10. Key technology analysis of precision fertilizer control system [4,5,23,31,32,38,39,40,44,59,75].
Figure 10. Key technology analysis of precision fertilizer control system [4,5,23,31,32,38,39,40,44,59,75].
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Table 1. Comparison of different stages of precision fertilization technology in recent years.
Table 1. Comparison of different stages of precision fertilization technology in recent years.
PointTechnical MeansAdvantageDrawbacksTechnological
Advances Reflect
Prescription agriculture in the 1990sSpatial interpolation using GIS to plan field fertilization programs based on historical soil data and crop needs.The first field-scale differential fertilization, breaking the traditional uniform fertilization mode, reducing fertilizer waste and improving fertilization efficiency.Relies on historical data and static interpolation, no real-time monitoring capability, unable to respond to field changes, accuracy limited to field scale.Introducing geographic information technology to promote the initial transformation of agricultural fertilization from “experience-driven” to “data-driven” and to establish a basic framework for spatially differentiated fertilization.
Post-2010 real-time monitoring systemSoil and crop sensors are used to pick up data, and the Internet of Things is used to do plot fertilization and control.With real-time monitoring capability, it can adjust the fertilization strategy according to the dynamic data of soil nutrients, crop growth, etc., with the precision of mu level and faster response.Sensors are costly to deploy, small in coverage, and hardware limits data transmission and processing, making it difficult to seamlessly monitor the entire field.From static planning to dynamic real-time regulation, real-time data collection and analysis through the sensor network, fertilization accuracy and timeliness are significantly improved.
Artificial intelligence and robotics in recent yearsFusion of AI algorithms and robotics (agricultural drones) for precise identification of single plants for fertilizer application.It realizes precise fertilization of single plants, intelligent distribution of fertilizers according to crop status and pest and disease risk and improves resource utilization and automation.The technology is complex, requires a lot of data and arithmetic, robots, AI chips are costly, and promotion is limited by technology and cost.Through the deep integration of AI and robotics, we can realize the leap from “group average” to “individual precision” in fertilizer control and promote the transformation of agricultural production paradigm to full data-driven intelligence.
Table 2. Structural comparison of fertilizer application devices in recent years.
Table 2. Structural comparison of fertilizer application devices in recent years.
Fertilizer Application Unit TypeFertilizer Application Unit TypeAdvantageDrawbacksTechnological
Advances Reflect
Multi-stage arc trajectory fertilizer applicator.Segmented PID control of electro-hydraulic system for millimeter tracking of groove opener trajectory.Significantly higher fertilizer-root contact coefficients and lower rates of root damage. Complex electro-hydraulic system and PID control technology, high device cost.Breaking through traditional fertilizer trajectory limitations to improve fertilizer effectiveness and crop protection.
Pneumatic variable diameter precision fertilizer discharge system.“Fertilizer discharge opening + speed control + pneumatic conveying” combination structure.Fertilizer discharge with high precision, adapted to complex terrain and dynamic operations.Pneumatic conveying structure requires high maintenance and relies heavily on the stability of the gas source.Innovative composite structure design, solving the problem of fertilizer discharge accuracy and response speed.
Rotary centrifugal pellet fertilizer applicator.Optimization of fertilization chamber structure by discrete element simulation.Low coefficient of variation of burrow length and low error of fertilizer application to a single burrow under specific working conditions.Limited adaptability to working conditions, need to be re-optimized for different conditions.Precise optimization of the structure using simulation technology for high-precision control of granular fertilization.
Notched-blade spiral fertilizer discharger with three-headed tapering spiral fertilizer discharger.Improvement of the structure of the fertilizer discharger and optimization of the discharge paths.Improved uniformity of fertilizer discharge and low average deviation of flow rate.Spiral structure prone to clogging.Structural innovation to effectively improve the uniformity and stability of fertilizer discharge.
Table 3. The domestic precision fertilizer application actuator in recent years.
Table 3. The domestic precision fertilizer application actuator in recent years.
EmphasisPrecision Fertilization
Implementing Agency
AdvantageTechnological
Progressiveness
Through structural optimization and algorithmic innovation, it solves the problem of uniformity and reliability of fertilizer application devices, which is applicable to scenarios such as corn, tea plantations, and orchards, and the technology is easy to land.Bi-variable fertilizer application system with independent control of the fertilizer discharge unit.Independent adjustment of each row of fertilizer rows, adapting to complex boundary conditions.Achieving precise control of variable application of corn-based fertilizers.
Two-stage spiral fertilizer discharger.Achieving smooth application of organic fertilizers.Addressing uneven application of organic fertilizers.
Sliced fertilizer discharger structure.Fertilizer rationing on a plant-by-plant basis.Improvement of positioning accuracy of seedling corn fertilization.
Table 4. Precision fertilization implementing agencies in foreign countries in recent years.
Table 4. Precision fertilization implementing agencies in foreign countries in recent years.
EmphasisPrecision Fertilization
Implementing Agency
AdvantageTechnological
Progressiveness
The integration of intelligent perception (discrete element simulation, haptic navigation) and interdisciplinary technologies (machine vision, autonomous navigation) to improve the accuracy of fertilizer application is innovative, but navigation and other scenarios are limited in their application in real environments.Rotary centrifugal granular fertilizer hole discharge deviceGood adaptability and good fertilizer agglomeration characteristicsOptimization of key parameters of the hole application device
Spiral fertilizer discharger with notched bladesReduced coefficient of fluctuation of fertilizer discharge uniformity and improved uniformityOptimization of the structure of the spiral fertilizer discharger
Tactile sensing systemToward autonomous navigation in cornfields without visual dependenceA new navigation solution for precision fertilization of dense crops
Table 5. Optimization of structure and algorithm of domestic intelligent control system in recent years.
Table 5. Optimization of structure and algorithm of domestic intelligent control system in recent years.
EmphasisIntelligent Control System
Architecture and
Algorithm Optimization
AdvantageTechnological
Progressiveness
Domestic research has shown a remarkable shift from traditional control to intelligent optimization, and a variety of advanced algorithms have been introduced into the field of fertilization control.Particle swarm optimization (PSO)-based RBF-PID algorithm.Minimized tracking error in regulation time, superior to traditional PID and RBF-PID control algorithms.Improved dynamic control accuracy.
Bat Algorithm for optimizing initial weights of BP neural networks/BP-PID algorithm.Effectively reduces the effects of system time lag and nonlinearity.Enhanced nonlinear adaptation.
An elastic collision model constructed based on Hertz theory and analyzed by DEM-CFD gas–solid coupling.Dome dispensers outperform other configurations.Provides a theoretical basis for structural improvement of the dispensing device.
Intermittent fertilization control system based on fruit tree canopy detection and construction of intermittent ultrasonic sensors to fertilization control system.The coefficient of variation of fertilizer uniformity is less than 7% for intermittent application units.Addressing the lack of banded intermittent fertilization devices in standardized orchards.
Hybrid optimization of fractional order proportional-integral-derivative (PID) algorithms.Narrowest range of steady-state conductivity with lowest regulation time and overshoot.A feasible method is provided for the control of nonlinear time-lag systems.
Liquid fertilizer targeted variable fertilizer application control system based on fuzzy PID algorithm.Reduced system response time and error control within 10 percent.Provides a viable solution for combining precision variable fertilization with targeted fertilization.
Table 6. Optimization of Structure and Algorithm of Foreign Intelligent Control Systems in Recent Years.
Table 6. Optimization of Structure and Algorithm of Foreign Intelligent Control Systems in Recent Years.
EmphasisIntelligent Control System Architecture and Algorithm OptimizationAdvantageTechnological
Progressiveness
In terms of intelligent control system architecture and algorithm optimization, foreign research focuses more on multi-sensor fusion and complex system modeling, resulting in a variety of innovative control strategies.EDEM-CFD coupling methodQuickly optimize the structure of the fertilizer applicator.Simulating operating conditions with EDEM-CFD coupling for fertilizer applicator design.
Flower Fertilization Optimization Algorithm (FFO)Outperforms 14 state-of-the-art meta-heuristics on 32 benchmark optimization problems.Parameter-optimized PID controller successfully applied to Maglev train positioning.
Venturi fertilizer applicator structure optimizationDetermine the optimal parameter range to maximize suction flow.Provide a quantitative basis for fertilization agencies
Table 7. Key technology and system integration of domestic variable fertilizer application in recent years.
Table 7. Key technology and system integration of domestic variable fertilizer application in recent years.
EmphasizeKey Technology and System Integration of Variable
Fertilization
VantageTechnological
Progressiveness
Domestic scholars have carried out a large number of systematic studies mainly in the field of realizing the dynamic matching between the amount of fertilizer applied and the needs of crops and soil conditions, which is the key to precise fertilizationPrescription map generation and intelligent decision modelingHigh reliability of electronic prescription maps to guide fertilizer applicationEnabling dynamic and intelligent decision-making on fertilizer application rates
Genetic algorithm optimized fuzzy PID controlSignificant improvement in response time over traditional PID and fuzzy PID controlEnhanced system immunity and dynamic response
Fertilizer shaft speed self-calibration and adaptive controlSignificant performance improvement over the original linear modelImprovement of fertilizer application stability under different working conditions
Variable formulation fertilizer control system based on prescription mapsMeets the requirements for rapid automated formulation and precise variable fertilizer applicationPreloaded soil prescription maps based on good utility and economy
Table 8. Key Technology and System Integration of Variable Fertilizer Application in Foreign Countries in Recent Years.
Table 8. Key Technology and System Integration of Variable Fertilizer Application in Foreign Countries in Recent Years.
EmphasizeKey Technology and System Integration of Variable
Fertilization
VantageTechnological
Progressiveness
In terms of key technology and system integration of variable fertilization, foreign research pays more attention to the in-depth integration with the precision agriculture platform to realize the whole process intelligence from data collection to decision-making execution.Using distributed autoregressive lag methodsData support for regional variable fertilization strategy developmentProspective guidance for fertilization strategies
Automated drone fertilization using a drone guidance systemEffectively regulate drone movement for precise and efficient fertilizer applicationAutomation and Intelligence of Fertilizer Application
Nanoparticle controlled release mechanismsReduced nutrient losses and greenhouse gas emissions, increased yields and economic benefitsProviding new controlled-release materials and technology paths for precise fertilization
Table 9. The domestic research and development of new types of fertilizers and special fertilizer application systems in recent years.
Table 9. The domestic research and development of new types of fertilizers and special fertilizer application systems in recent years.
EmphasisResearch and Development of New Fertilizers and
Special Fertilizer
Application Systems
AdvantageTechnological
Progressiveness
Domestic research for organic fertilizers, liquid fertilizers, and other special application equipment; special fertilizer system research and development has become another hotspot for domestic researchBased on fuzzy PID algorithm + conductivity detection feedback methodFertilizer concentration fluctuation range is stable, improving the uniformity of fertilizer applicationPromote intelligent upgrading of water-fertilizer integration system
Biogas slurry mixed farming machines and their systemsMixed fertilizer components concentration error is small, proportioning accuracy and stability is highFilling the technology gap for intelligent application of biogas fertilizer
External grooved wheel structure + LIDAR methodEnabling on-target fertilization on demandInnovative orchard precision fertilization model
Table 10. Research and development of new fertilizers and special fertilizer application systems in foreign countries in recent years.
Table 10. Research and development of new fertilizers and special fertilizer application systems in foreign countries in recent years.
EmphasisResearch and Development of New Fertilizers and
Special Fertilizer
Application Systems
AdvantageTechnological
Progressiveness
The research and development of new fertilizers and special fertilization systems is the frontier of precision fertilization research in foreign countries, mainly in the combination of functional fertilizers and intelligent release systems.Innovative orchard precision fertilization modelFoliar fertilizers containing humic acid and boron significantly optimize wine quality.Providing a theoretical basis for foliar fertilization technology
Monitoring and fertilization decision rules to optimize nitrogen recyclingReduction in nitrogen fertilizer applicationInnovative farm-scale fertilization model
Multi-criteria decision-making (MCDM) approach.Providing scientific selection basis for mechanized fertilization of orchardsProviding new ways to make decisions about fertilizer mechanization
Fertilizer prescription equation based on soil test crop response (SCTR)Balanced nutrient application is achieved for optimal yield and nutrient uptakeNew reference for other crop fertilization methods
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Wang, L.; Gao, J.; Qureshi, W.A. Evolution and Application of Precision Fertilizer: A Review. Agronomy 2025, 15, 1939. https://doi.org/10.3390/agronomy15081939

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Wang L, Gao J, Qureshi WA. Evolution and Application of Precision Fertilizer: A Review. Agronomy. 2025; 15(8):1939. https://doi.org/10.3390/agronomy15081939

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Wang, Luxi, Jianmin Gao, and Waqar Ahmed Qureshi. 2025. "Evolution and Application of Precision Fertilizer: A Review" Agronomy 15, no. 8: 1939. https://doi.org/10.3390/agronomy15081939

APA Style

Wang, L., Gao, J., & Qureshi, W. A. (2025). Evolution and Application of Precision Fertilizer: A Review. Agronomy, 15(8), 1939. https://doi.org/10.3390/agronomy15081939

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