Sensor-Based Monitoring and Control of Precision Seed Metering Systems in Digital Agriculture

• Systematic Review

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27 October 2026

34 Pages

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1

Department of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea

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Department of Smart Agricultural Systems, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea

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Hwang Geum Seeders, Pyeongtaek 2042, Republic of Korea

Author to whom correspondence should be addressed.

Abstract

Precision seed metering has evolved from mechanically optimized devices to sensor-based, digitally connected systems. However, research remains fragmented across metering mechanisms, sensing, data processing, communication, and control. This review examined monitoring and closed-loop control throughout the seed-delivery process, from pickup and release to tube passage, furrow placement, and crop emergence. This structured review synthesized 49 primary studies identified through Scopus and the Web of Science Core Collection, with supplementary searching using Google Scholar and literature coverage through 1 September 2026. Optical and photoelectric sensors provide the most direct evidence for seed-passage detection, whereas encoders, pressure, vacuum, motor-current, and vibration measurements primarily indicate machine or actuator conditions. Machine vision provides spatial information and can support independent reference measurements, but requires suitable imaging conditions and computational resources. Capacitive, acoustic, impact-based, and hybrid approaches remain less extensively validated. Reported performance cannot be directly compared across studies due to differences in seed characteristics, mechanisms, operating speeds, event definitions, disturbances, and reference methods. Most studies demonstrate monitoring, alarms, or open-loop optimization rather than complete sensor-to-actuator correction. An engineering framework is therefore proposed to connect measurement targets, evidence maturity, latency, actuator authority, and independent verification. Future research should prioritize standardized testing, disturbance robustness, synchronized multi-row field validation, and measured end-to-end delay. Transparent reporting of correction accuracy, stability, cost, power demand, and interoperability is also required to support deployment and defensible assessment of closed-loop seeding systems.

1. Introduction

Precision seeding establishes the initial spatial distribution of a crop and consequently influences plant density, competition among neighboring plants, and the efficiency of subsequent field operations. In digital agriculture, this process is enhanced through the integration of sensors, embedded processors, communication networks, and controllable actuators with the seeding mechanism [1,2]. The engineering objective extends beyond recording machine operation to determining whether each seed is delivered at the intended time and location and whether deviations can be detected and corrected within the available response window.

Seed-metering performance and seed monitoring are closely related but distinct concepts. Studies of mechanical, pneumatic, vacuum, and electrically driven metering systems commonly evaluate seed singulation, misses, multiples, and spacing after bench or field experiments [3,4]. In contrast, monitoring systems are intended to detect individual seed events or identify abnormal machine states during operation. A favorable post-operation quality-of-feed index does not confirm that failures were detected in real time. Similarly, high seed-counting accuracy does not necessarily demonstrate correct seed placement within the furrow or successful crop emergence.

Terminology is applied consistently throughout this review. Seeder denotes equipment that meters and places seeds; precision planter is retained where the cited source uses it for single-seed row-crop machinery. Metering denotes singulation and delivery, whereas monitoring denotes observation and classification. A feedback-loop claim must specify the controlled variable, measured feedback, decision, and actuator response. Actuator speed regulation and seed-event compensation are different feedback targets. A claim of verified seed-placement correction additionally requires an independent reference showing that the placement deviation was reduced.

Previous reviews have already addressed adjustable precision seeders [5], in-row seed-spacing measurement and seed mapping [6], and edge-intelligent monitoring and compensation [7]. These contributions overlap with the present scope and should not be treated as reviews of mechanics alone. The present synthesis therefore focuses on a narrower assessment question: what does each signal directly establish, what can its actuator physically correct, and what independent evidence verifies the outcome? Mechanism design and modeling [4,8] provide the physical context, while monitoring and control studies are evaluated against these separate evidence boundaries.

A higher aggregate counting accuracy does not necessarily imply reliable identification of individual misses or multiples. For example, pulse-width monitoring [9] and the long-belt corn monitoring system [10] report distinct counting and missed-event outcomes. Studies of speed and spacing effects [11], flow reconstruction [12], and line-laser detection [13] are cataloged, but incompletely verified outcome fields are not used to establish a universal speed limit, accuracy trend, or technology ranking. This restriction keeps the synthesis traceable to the evidence actually confirmed.

Across the reviewed studies, evidence is distributed among metering mechanics, direct seed sensing, machine state sensing, embedded interpretation, communication, and actuation. These functions are often evaluated at different physical locations and against different reference targets. The present review brings those evidence boundaries together rather than claiming that earlier reviews omitted digital monitoring. A favorable result for one component does not establish reliable detection, timely correction, or improved placement for an integrated multi-row machine.

Accordingly, the novelty of this review lies in integrating sensing evidence and control maturity across the complete seed-delivery chain, while explicitly distinguishing direct seed-event evidence from auxiliary machine-state evidence and separating monitoring functions from independently verified closed-loop correction. This review aimed to (1) compare technologies for seed-event monitoring and machine-state sensing, (2) evaluate their real-time processing, communication, and control capabilities, and (3) identify validation requirements and research priorities for multi-row closed-loop field operation.

This review provides a target-based synthesis of measurement targets, corrective capabilities, and independent verification of downstream outcomes. Figure 1 illustrates these relationships. Seed metering comprises the physical singulation and delivery of seeds. Seed monitoring involves measuring and classifying seed-related observations, while placement verification independently establishes the seed’s final position. A seed-tube detection event confirms passage at the sensing plane; verification of in-furrow spacing requires an independent placement measurement.

Figure 1. Conceptual framework of sensor-based closed-loop control for precision seed metering, showing the relationships among seed metering, real-time seed monitoring, actuator correction, and independent placement verification.

2. Literature Search and Review Methodology

A structured critical review was conducted across seed pickup, release, tube passage, furrow placement, and emergence. PRISMA 2020 reporting principles [14] were used to organize the recorded identification and screening totals. The work is a qualitative synthesis rather than a meta-analysis or a fully reproducible systematic review: the archived database-specific search logs and screening-process details are not documented completely. The limitations of those records are distinguished from the engineering conclusions drawn from verified study content.

Scopus and the Web of Science Core Collection were used as the primary bibliographic databases, and Google Scholar supported supplementary searching and backward and forward citation tracking. The search included literature available through 1 September 2026. No lower publication-year limit was imposed because foundational standards and seed-spacing metrics were required to interpret recent developments in real-time sensing, embedded processing, communication, and control. The principal technological progression was evaluated for the period from 2000 to 2026, while earlier publications were retained when they provided essential definitions, standards, or evaluation methods.

The search concepts comprised machinery, sensing, digital systems, and control. A reproducible formulation links machinery to the union of the other three groups: M AND (S OR D OR C). Requiring sensing, digital systems, and control simultaneously would exclude relevant single-component studies. Database-specific formulations are provided in Appendix B as reconstructed queries for an auditable rerun; they are not presented as verbatim historical search logs. Exact historical queries, search dates, source-specific yields, supplementary-source counts, screening personnel, and exclusion-reason counts require confirmation from the original review records.

Appendix B now separates the documented search scope, reconstructed rerun expressions, and missing historical fields (Table A2 and Table A3). The literature cutoff is not a search-execution date. No database interface version, collection selection, document-type limit, language filter, or exact execution date is inferred where the original log is unavailable. This reporting distinction follows PRISMA-S [15]; the reconstructed queries improve auditability but cannot establish full historical reproducibility.

Primary studies were organized into two evidence strata. Monitoring and control studies addressed direct seed sensing, machine-state sensing, acquisition, communication, drive regulation, or compensation. Mechanism studies supplied background on release timing, seed orientation, tube interactions, and failure mechanisms that determine the signal a monitor must resolve. The latter studies inform physical interpretation and test design, but do not establish monitoring accuracy or control maturity. Secondary reviews, standards, and manufacturer documents are contextual sources and are excluded from the primary-study count. No inference of complete non-English coverage is made; a Chinese-language review [7] is added for contextual comparison.

Eligibility was based on relevance to the seed delivery chain and the study’s measurement or control target. Only mechanism-only primary studies were retained as physical background, whereas sensing and control studies informed the monitoring synthesis. Contextual reviews, standards, and manufacturer materials were not counted as primary studies. The records do not establish all historical language, document type, access, or database filters, or the detailed exclusion reason for each rejected full text; these remain explicit audit gaps rather than assumed screening rules.

The original screening record lists 301 identified records, 65 duplicates, 236 screened records, 148 title/abstract exclusions, 88 full texts assessed, and 38 full text exclusions, leaving 50 cataloged entries. An audit of Table A1 identified one secondary review [5] among those entries. It has been removed from the primary-study catalog, leaving 49 primary studies; the original screening totals are retained as recorded rather than retroactively reassigned. Within the catalog, 19 mechanism or machine design studies provide physical background and 30 address monitoring, processing, communication, or control. Figure 2 distinguishes this classification correction from the historical screening pathway. Supplemental references added for technical context and commercial illustration are not represented as newly screened primary studies.

Figure 2. Recorded screening pathway and corrected primary study classification. NR means not recoverable from the supplied search record.

The evidence matrix records measurement target, crop, sampling unit, sensing or control principle, validation setting, reference method, repetition, outcome metric, and verification status. A source-confirmed omission is distinguished from an item not yet verified in the extraction. Unverified outcomes do not support maturity grades. Table A1 retains these limitations transparently and separates crop identity from sample size. Four appraisal dimensions guide interpretation: realistic validation environment, a reference independent of the evaluated sensor, reported replication, and event-level error measures. These are qualitative appraisal dimensions, not a numerical quality score.

No formal risk-of-bias instrument or numerical quality score was applied. The appraisal was a qualitative engineering assessment of the four domains summarized in Table A4. Table A1 records available study information and extraction gaps; it is not a completed study-by-study risk of bias assessment. Independent duplicate appraisal, assessor calibration, and a documented disagreement procedure cannot be confirmed from the supplied records. Accordingly, an unverified field is treated as uncertainty, not as evidence of poor study conduct. These limitations restrict comparative confidence and whole system readiness claims.

Evidence was synthesized by measurement target and validation boundary rather than pooled accuracy. The common comparison dimensions were sensing principle, location, temporal resolution, reference method, validation environment, disturbances, processing needs, actuator authority, latency, cost, and power. Missing cost, power, or timing values were recorded as unavailable rather than inferred from hardware reputation. The five maturity grades in Section 4 describe the demonstrated target within the reviewed evidence; they do not certify whole-machine readiness or imply a formal risk-of-bias instrument.

3. Overview of Seeder and Seed Monitoring Requirements

A seeder meters and places seeds at a specified rate, row spacing, intra-row spacing, and depth. Precision seeders or planters add singulation and controlled spacing for row crops [5,16]. The metering device remains central, but a complete evaluation must distinguish the performance of the meter from the evidence supplied by a particular sensor. Foundational machine-design principles remain relevant to the interpretation of seed pickup, release, and delivery behavior [4,8].

The relevant measurement chain is meter pickup, seed release, seed-tube passage, furrow placement, and crop emergence. Encoder, motor current, vacuum, or pressure measurements can confirm meter or actuator state, but they do not directly prove that a seed was released. Optical or other tube sensors can confirm passage at their sensing plane, but they do not establish the final in-soil position because bounce, rolling, tube-wall interaction, and furrow dynamics occur downstream. Final placement requires an independent furrow-level reference, and agronomic success requires emergence validation.

This distinction separates four evaluation targets: seed-metering performance, seed-passage detection, actual furrow placement, and crop emergence response. Conflating these targets can overstate technology maturity. A monitor may count seed passages accurately while the meter produces poor singulation, or a meter may achieve a high quality-of-feed index while lacking any real-time diagnostic capability.

For single-seed drills, ISO 7256-1 [17] and Kachman and Smith [18] classify observed spacings x ≤ 0.5 s as multiples, x > 1.5 s as misses, and 0.5 s < x ≤ 1.5 s as acceptable, where s is theoretical spacing. If N is the total number of measured spacings, the multiple and miss indices are 100 times their respective category counts divided by N; the quality-of-feed index is 100 times the acceptable-spacing count divided by N, equivalently 100 minus the multiple and miss indices. It measures the proportion of acceptable spacings, not detection accuracy. For line-sown seed drills and bulk flow studies, ISO 7256-2 [19] provides the relevant equipment-testing context; flow rate and row distribution should be reported separately from single seed spacing indices.

Spacing precision is distinguished from the coefficient of variation calculated using the observed mean. In the Kachman–Smith framework [18], precision is the standard deviation of acceptable spacings divided by theoretical spacing, expressed as a percentage. If an ordinary CV is also reported, its observed-mean denominator must be stated explicitly.

Monitoring metrics must identify their reference events and matching rules. Precision, recall, event-level F1, and false-event measures complement aggregate count agreement. In this review, detection latency begins when a seed crosses the specified sensing plane and ends when the event decision is available. Feedback control latency begins at that same crossing and ends at the verified actuator response. Any seed release to sensor transport time is reported separately and added only when a release-referenced total is required. For a miss, no seed crossing occurs: the origin is the expected crossing time derived from the synchronized target sequence, and the waiting threshold is included. Figure 3 shows where each measurement ceases to establish the downstream outcome. Table 1 summarizes the performance indicators and stage specific validation requirements.

Figure 3. Precision seeding measurement chain and evidence boundary. Black dashed trajectories in the furrow panel depict possible seed bounce and rolling after tube passage. In the emergence inset, blue dashed vertical lines mark illustrative position intervals; the red dashed oval encloses closely spaced seeds and the red dashed circle marks an illustrative empty position. These symbols are schematic, not measured error thresholds. Red arrows connect the assessment stages and do not imply that a tube event verifies downstream placement or correction.

Table 1. Performance indicators and stage-specific validation requirements for precision seed metering and monitoring systems.

4. Real-Time Seed Monitoring Challenges and Sensing Technologies

4.1. Disturbances and Validation Requirements

Real-time seed monitoring is affected by seed characteristics, metering mechanisms, sensor performance, field conditions, communication and computing systems, and validation procedures. Variations in seed size, shape, coating, moisture content, and clustering can influence pulse amplitude and duration. Similarly, metering speed, vacuum stability, release geometry, seed-tube curvature, and component wear can alter seed movement before detection. The measured signal may also be affected by dust, vibration, changing illumination, sensor misalignment, electrical interference, and fouling [9,21].

A missing optical pulse may reflect seed-flow interruption or a detection failure. Encoder, pressure, and signal-baseline measurements can help discriminate these explanations, but they do not independently identify the physical cause. Stable shaft speed and vacuum do not exclude contamination, overlap, or a blind zone. Fault classification therefore requires an independent seed reference or a validated combination of diagnostic signals. Table 2 links these disturbances to proposed validation tests; the tests are author recommendations, not demonstrations that every cited system passed them.

Table 2. Disturbance-to-validation framework with supporting study examples.

The available evidence is insufficient to establish universal limits for acceptable accuracy degradation, dust concentration, vibration amplitude, or maximum seed-event frequency, as these parameters have been reported inconsistently or omitted in many studies. Future field studies should therefore report event frequency, timestamp resolution, packet loss, clock synchronization, electrical protection, sensor alignment, cleaning intervals, and maintenance requirements alongside conventional accuracy metrics.

4.2. Sensing Principles and Evidence Maturity

Sensors are first classified by measurement target: direct seed events or bulk flow, and auxiliary machine state. Within each target, classification follows physical transduction: optical attenuation, dielectric-field response, mechanical impact, imaging, rotation, pneumatic state, or actuator load. Fusion is an integration strategy rather than a separate transduction principle. Table 3 uses the same dimensions for all categories. Evidence grades are assigned to a specified target, so laboratory placement imaging and operational tuber detection can have different validation boundaries within the same modality.

Optical sensors register beam attenuation or interruption, with pulse width and inter-event timing used to separate events. Their direct-passage evidence is broader than that of the other event sensors in this catalog, but this does not establish universal field readiness. Liu et al. [9] reported counting accuracy ≥ 98.41% for cotton at 28.31–35.71 r/min and ≥98.19% for maize at 13.78–19.39 r/min. At 8–9 km/h, cotton missed event monitoring reached ≥ 94.06%. These count and miss metrics have different denominators and cannot be combined into one detection benchmark. Their reference methods and operating ranges are compared in the monitoring evidence synthesis in Section 8.

Multi-element optical arrangements address missed spatial coverage, while pulse-width rules address some temporally overlapping events. Optical path contamination and seed overlap can still change or merge pulses; tube impacts can also alter placement after detection [9,10]. Early photogate studies [35,36] compared optical estimates with grease-belt spacings, demonstrating why time interval alone is insufficient when the crossing position varies. Their contribution is controlled measurement validation, not proof of field fault correction. Recent laser [13,37], modular optoelectronic [22], and dust-resistant infrared [29] systems must likewise be interpreted within their particular reference and disturbance tests.

Flow sensing and individual-event sensing are different targets. Infrared, laser-diode, and light-dependent-resistor measurements have been compared against bulk seed-flow references [38,39], while flow reconstruction seeks to reduce overlapping passages before counting [12]. A correlation with seed mass is not an event-level confusion matrix. The monitoring evidence synthesis in Section 8 therefore identifies flow studies separately and retains unverified reference or operating-range fields instead of importing them from similar systems.

Capacitive sensing responds to changes in dielectric properties within an electrode field. Potential event sensitivity depends on seed moisture, orientation, electrode geometry, tube material, and baseline stability. Independent single-seed detection remains insufficiently verified in this catalog; Table 3 therefore assigns limited direct-event evidence rather than an experimentally mature event-detector grade.

Active-shielded wheat sensing [40] concerns mass flow, while the capacitive channel in the cotton fusion system [22] supports optical timing. These applications show useful dielectric sensing and signal integration, but neither establishes an independent, broadly validated single seed event detector. Optical contamination tolerance cannot be assumed to resolve moisture dependence or dense flow superposition. Validation must report the target explicitly, compare against an independent event or mass reference, and include installation and crop-specific calibration.

Rotation, pneumatic state, and actuator-load sensors form the auxiliary machine-state branch. Encoders and proximity sensors measure shaft or disc movement; pressure and vacuum transducers measure pneumatic conditions; current and torque sensors measure actuator load. A rotating disc does not guarantee occupied cells, and stable vacuum does not prove release. These channels can explain a seed signal deviation only after their relationships are validated against direct seed events. Drive regulation studies [41,42,43,44,45,46,47,48] therefore support actuator control rather than automatic confirmation of seed delivery.

Impact or acoustic detection converts seed contact into a transient waveform. Seed size and contact geometry affect amplitude, while bounce can generate repeated impulses and machine vibration can mimic an event. The present catalog provides limited verified direct event evidence for this branch. A deployment comparison therefore requires sensor position, sampling rate, mechanical isolation, event matching tolerance, and errors under imposed vibration; illumination independence alone does not establish field robustness.

Vision is graded by application rather than given a single readiness label. Controlled bench imaging [20] measures spacing and spacing index agreement; crop-specific operational imaging [49,50] detects or tracks tubers and empty planting positions. A camera can serve as an independent reference for an optical sensor, but it cannot validate its own detector without separate annotations or measurements. Laboratory spacing analysis, operational detection, and verified placement correction are therefore separate evidence targets in Table 3, and are examined further in Section 7 and Section 8.

Operational vision remains crop- and installation-specific [49,50]. Occlusion, motion blur, lens fouling, illumination, and processing delay affect the result. Model inference time excludes acquisition, transfer, event association, and actuator travel unless explicitly included. Demonstration on potato tubers does not establish generalization to small cereal seeds, different coatings, or multi-row field operation; Section 7 evaluates the model and dataset evidence separately from the sensing principle.

Fusion combines complementary targets, such as optical passage and encoder state, or optical timing and capacitance [10,22,34]. Its value must be measured against the same reference and operating conditions used for every single channel. An ablation comparison should quantify whether fusion reduces false and missed events, and at what additional delay, calibration burden, cost-per-row, and power demand. A communication network carrying several signals is integrated acquisition; it is not by itself evidence of improved diagnostic accuracy.

Five author-defined grades are used: G1, repeated direct measurements against a reference in more than one primary study; G2, a direct target demonstrated in a restricted crop or installation; G3, indirect or bulk-flow measurements without verified individual-event outcomes; G4, a plausible direct target with insufficient verified validation; and G5, a proposed architecture without demonstrated target-level performance. Field robustness, reference independence, replication, and event-level metrics are reported separately. G1 is not a certification of commercial readiness.

Table 3. Sensing taxonomy, target-specific evidence grades, and representative commercial functions with their evidence boundaries.

Cost and power are deployment dimensions, not maturity grades. Comparable numerical cost-per-row and wattage data were not established across the reviewed solutions. A simple optical channel adds emitter, receiver, conditioning, and timing circuitry; capacitive channels require stable electrodes and shielding; impact channels require mechanical isolation; imaging adds camera, illumination, and image processing; fusion combines channel and synchronization requirements. These describe hardware burdens rather than a measured price or power ranking. Future comparisons should report currency and year, installation and maintenance costs, supply voltage, average and peak current, and whether actuator demand is included.

5. ICT Modules, Embedded Architecture, and Communication Frameworks

5.1. Embedded Processing Platforms

The selection of an embedded processing platform should be based on event timing, number of monitored rows, input-signal type, computational requirements, and operating conditions. Microcontrollers are well suited to interrupt-driven pulse timing, encoder measurements, analog-to-digital converter (ADC) acquisition, deterministic alarm generation, and motor control. Edge computers can support image processing, data logging, user interfaces, and more computationally demanding models. However, operating-system latency, start-up time, heat generation, power-supply stability, and enclosure protection must be considered. The reviewed studies support a heterogeneous architecture in which a microcontroller manages time-critical events, while an edge computer performs higher-level, noncritical analyses [9,20].

No common experimental benchmark of Arduino, STM32, ESP32, Raspberry Pi, and Jetson was established in the reviewed studies. Table 4 lists candidate design roles only; its entries are author assessments, not measured rankings of determinism, row capacity, or power. Reference [9] supports microcontroller-based pulse processing without establishing an STM32 benchmark. Reference [20] supports a Raspberry Pi application without providing a matched comparison with other processors. Every implementation still requires measured worst-case timing and power under its actual row and communication load.

Table 4. Platform selection and communication frameworks with design roles and validation requirements.

5.2. Data Acquisition, Signal Processing, and Edge Computing

Let v denote forward speed in m/s and s the theoretical seed spacing in m. The expected event frequency f event is in Hz, and the nominal event interval T event is in seconds:

f event = v/s

(1)

The nominal interval between consecutive seed events is:

T event = s/v

(2)

At 12 km/h, v = 3.33 m/s; with s = 0.20 m, f event is approximately 16.7 Hz and T event approximately 60 ms. At 18 km/h, v = 5.00 m/s; with s = 0.15 m, the values are approximately 33.3 Hz and 30 ms. These intervals describe event load, not an automatic correction deadline. With the usual geometry meter → tube sensor → furrow, a downstream sensor cannot recover an already omitted upstream seed by adjusting meter speed. It can change subsequent delivery. Filling the original gap requires a supplementary unit with remaining spatial access to that gap.

The available correction window is the time from the specified observation or expected miss time until the last instant at which the selected actuator can affect the intended planting position. For a supplementary unit, it depends on the fore–aft separation, machine speed, seed transport, actuator stroke, and a safety margin. The measured budget comprises acquisition, filtering, decision, local communication, and verified actuator response. Diagnosis of an absence also includes the miss waiting threshold. Release-to-sensor transport is added only for a release-referenced total. A system meets the deadline only when its worst-case measured total is below the geometry-specific window; nominal event interval alone cannot prove this.

Digital pulse sensors should use timestamped interrupts or hardware-capture functions whenever possible. Analog signals from capacitive, impact, or pressure sensors require an ADC sampling rate sufficient to preserve the shortest valid waveform. Potential sources of error, including timer rollover, interrupt priority conflicts, electrical bouncing, aliasing, and ADC saturation, should be evaluated explicitly. Multi-row systems also require bounded buffering to prevent data loss or incorrect row assignment when events occur almost simultaneously.

Signal processing procedures should provide a traceable pathway from the raw signal to the final classification. Baseline compensation, electrical shielding, analog or digital filtering, and debouncing should be applied before feature extraction. Relevant features may include pulse width, amplitude, inter-event interval, signal energy, pressure variation, motor-current characteristics, and image-derived seed trajectories. These features can then be compared with a target model adjusted according to machine speed and the commanded seeding rate [9,41].

Machine learning models should be introduced only after a reliable deterministic acquisition pathway and an independent ground-truth method have been established. A model trained using one crop, seed coating, operating speed, or sensor configuration cannot be assumed to generalize to other conditions. Dataset size, class distribution, separation of training and test data, inference time, memory consumption, and performance under previously unseen conditions should therefore be treated as essential engineering specifications rather than optional reporting information.

5.3. Communication and IoT Integration

Communication requirements should be defined according to the function of each network layer. Links between sensors and row controllers transmit raw or event-level signals and therefore require predictable timing, resistance to interference, and clear row identification. Links between row controllers and the implement controller aggregate operating status and control commands while preserving timestamps during simultaneous multi-row communication. When supported by the commercial machine architecture, tractor–implement communication should use an interoperable machine network such as CAN or ISOBUS [30].

ISOBUS functionality must be specified rather than inferred from CAN connectivity. The Task Controller supports documented operations through TC-BAS, position-dependent rates through TC-GEO, and automatic section switching through TC-SC [55]. Compatibility depends on the functions supported by the implement, terminal, and controller. Proprietary row-controller CAN messages may still require vendor-specific interfaces; an ISOBUS tractor connection does not establish interoperability inside the implement. Neither CAN nor ISOBUS guarantees a control deadline without testing bus load, arbitration, synchronization, and fault recovery [30,31].

The communication architecture must be designed according to the control deadline. Wireless and IoT connections can support data visualization, remote monitoring, and supervisory control, as demonstrated by ZigBee-based monitoring systems and remote seeding interfaces [32,33]. However, time-critical corrective actions at the row level should remain on deterministic local networks. Multi-sensor maize monitoring systems using CAN have demonstrated the integration of GNSS, encoder, pressure, seeding, and fertilization data within an implement-level network [34]. Such systems provide stronger evidence of functional integration than studies demonstrating connectivity alone. Nevertheless, packet loss, synchronization error, network loading, and end-to-end latency should still be reported explicitly.

Bluetooth, Wi-Fi, and wider-area links can support setup, diagnostics, and telemetry, while immediate decisions remain local. The proposed separation in Figure 4 is a design recommendation rather than proof that every reviewed system has deterministic communication. Authentication, update control, data export, ownership, and recovery from network failure are part of adoption assessment. Commercial examples illustrate available functions in Table 3, and the manufacturer evi-dence is kept separate from independently validated research. Table 4 brings together the platform-selection and communication requirements discussed in Section 5.1, Section 5.2 and Section 5.3.

Figure 4. ICT architecture for real-time seed monitoring and control.

6. Control, Automation, and Decision-Support Systems

6.1. Closed-Loop Seed-Metering Control

Control is classified by the variable actually fed back. C0 denotes monitoring or alarm only, C1 denotes feedback regulation of motor speed, commanded rate, or section state, C2 denotes automatic seed event-based prevention or compensation, and C3 denotes C2 with independent verification that the intended placement error was reduced. Operator assistance is reported separately. C1 is a valid actuator loop but is not seed event corrective control. C2 demonstrates a detection-to-actuation pathway; C3 is required for the strongest claim of verified seed placement correction. Table 5 defines the control levels and their minimum reporting requirements.

Table 5. Control levels and minimum reporting requirements for seed-metering systems.

Figure 5 separates the authority to act from the evidence needed to substantiate a control claim. Operator-assisted systems present information or recommendations for a human decision. Automatic systems act within a defined operating envelope, but a C1 or C2 result alone does not demonstrate fully autonomous operation. Such a claim additionally requires validated fault handling, fallback behavior, safe operating limits, and sustained field performance. The figure therefore treats full autonomy as a deployment claim requiring further evidence, not as an automatic consequence of adding a sensor or AI model.

Figure 5. Classification framework linking monitoring and decision-support authority with evidence levels required to substantiate automatic control claims in precision seed-metering systems.

Mechanism optimization [23,24,25] identifies favorable settings but is not a feedback experiment. A control report must identify target, measured feedback, manipulated variable, controller, sensor and actuator positions, reference method, and validation setting. Detection and control delays use the sensing-plane origin defined in Section 3; seed-release transport is a separate component. For a miss, the expected event time is the origin. A missing timing report does not prove an untimely controller, but prevents verification of its correction deadline.

Electric actuation provides the control authority required for closed-loop correction, but actuator accuracy alone is insufficient. Laboratory and field evaluations of electromechanical and variable-rate drives demonstrated the value of individual-row control, speed feedback, CAN communication, and lag compensation [42,43,44,45]. More recent systems have extended this architecture through automatic section control, field-oriented motor control, and multi-sensor fusion [34,46,47]. Closed-loop claims should nevertheless identify the sensed error, manipulated variable, timing budget, stability, and independent verification of the corrected seed placement.

Empty-cell prevention [56,57] and potato compensation [50,58] differ from motor-speed tracking. They can support a C2 claim when an observed seed state triggers an automatic action, but C3 additionally requires an independent measurement of corrected placement. The control evidence synthesis in Section 8 includes the control and integration studies discussed in the text, while marking source details not confirmed in the extraction. Missing delay or verification fields prevent an upgrade to C3; monitoring accuracy alone is not a correction-success rate.

6.2. Variable-Rate Seeding Systems

Variable rate seeding enables the target seed population to be adjusted independently of a fixed ground wheel transmission. Ground-wheel drives, centralized electric drives, individual row electric drives, and hydraulic or pneumatic actuation systems differ in their speed-feedback capability, row-level control authority, transition response, and ability to verify seed delivery. The reviewed drive-by-wire system demonstrated the value of electronic rate control; however, achieving the commanded motor speed did not necessarily confirm that seeds were delivered or placed as intended [24].

During acceleration, turning, wheel slip, and transitions between prescription zones, the theoretical seed interval changes according to forward speed and the commanded application rate. Encoder feedback can confirm shaft rotation, while GNSS or wheel-speed measurements can validate the speed input used by the control model. However, only a direct seed sensor can determine whether the adjusted command produces the intended sequence of seed events. Even seed tube sensing cannot confirm the final in-furrow spacing because seed bounce, rolling, and soil interaction may alter the placement position after the seed leaves the delivery tube. Independent measurements of seed placement or crop emergence are therefore required for complete field validation.

An author-proposed four-level architecture links these functions. The drive loop regulates actuator speed, the row loop compares direct events with a speed-dependent target, the implement controller coordinates rows and detects shared deviations, and the supervisory layer handles prescriptions, records, alarms, and visualization. This architecture combines engineering requirements from the reviewed studies; it is not presented as one experimentally validated system.

Transition performance should be evaluated using settling time or distance, delivered-population overshoot, row-to-row synchronization error, and the difference between the commanded rate and independently verified seed placement. Additional evaluation should include rate-tracking error under speed variation, performance during prescription-zone transitions, response to wheel slip and turning, and consistency across individual rows. Reporting these measures would provide stronger evidence of variable rate performance than motor-speed accuracy alone. Table 6 compares the drive architectures against these feedback and validation requirements.

Table 6. Comparison framework for variable-rate drive architectures with supporting studies.

7. Artificial Intelligence and Machine Learning

This section evaluates model evidence, dataset validity, and deployment constraints. Section 4 describes what sensors measure; Section 5 describes acquisition and timing. Rule-based pulse processing [9] and conventional bench image analysis [20] are not classified as machine learning unless a trained model is reported. By contrast, potato detection and tracking [49,50] use learned image representations. This distinction prevents all digital processing from being described as AI.

In operational vision, object detection identifies seeds or empty carrier positions, while tracking associates observations across frames. Duplicate tracks, occlusion, and delayed association can produce count errors even when frame-level detection metrics are high. Xiao et al. [49] combine a lightweight detector with tracking and report 29 ms inference; this is an algorithm-stage value rather than an end-to-end correction time. Li et al. [50] link image decisions to compensation, but their stated dataset total and split counts are inconsistent. The uncertainty limits interpretation of reproducibility rather than being silently corrected.

Adjacent frames from the same run and augmented versions of one image should remain in the same data partition to avoid leakage. Independent runs, machines, crops, and environmental conditions provide stronger tests than a random frame split. Report per-class precision, recall, F1, and false alarms for operational events; MAP evaluates object detection and does not directly measure recovered misses or final spacing. Confidence thresholds, tracking parameters, class prevalence, and annotation agreement are required to interpret performance. When errors are rare, pooled accuracy can remain high despite poor miss recall.

Model integration must also quantify resource and fault behavior. Tests should measure acquisition-to-decision delay, worst-case inference under simultaneous rows, memory, processor load, power, and dropped frames. Sensor-fusion comparisons need synchronized labels and single-channel ablations. Model confidence should not replace physical checks: an encoder deviation and a missing optical event may suggest different failures, but a causal label requires validation. A defined fallback to a reliable local monitor or operator alarm is needed when images, timestamps, or model inputs become invalid.

Digital twins and federated learning remain proposed directions rather than established seed-monitoring outcomes. Federated learning [59] offers a general method for decentralized training, but does not by itself resolve inconsistent event labels, crop imbalance, communication availability, or private machine interfaces. A seed-specific evaluation would require an independent multi-farm test, drift assessment, model-update governance, and the same measured local-control deadline used for a centralized model. Near-term progress depends on representative datasets and verified correction outcomes before additional models are introduced. Table 7 summarizes the method categories and the evidence required for deployment.

Table 7. AI evidence categories by method and demonstrated deployment target.

8. Integrated Evidence Synthesis and Technology Maturity

The synthesis revealed a persistent gap between seed-metering mechanism optimization and digital monitoring. Mechanical and pneumatic studies covered a wide range of crops and frequently reported favorable miss, multiple, and quality of feed indices. In contrast, direct monitoring studies were less common and mainly focused on optical pulse detection or image analysis under controlled conditions. Because the evaluated crops, metering mechanisms, operating speeds, experimental conditions, and performance metrics varied considerably, the reported percentages should not be interpreted as universal performance benchmarks.

Repeated optical measurements support direct passage detection, while imaging supports controlled spacing assessment and crop-specific operational event recognition. Capacitive wheat mass flow and optical timing support [22,40] remain G3 for the individual event target; direct capacitive event detection remains G4 in this catalog. Auxiliary shaft, pressure, and load signals support machine-state interpretation rather than seed delivery. Evidence of actuator regulation and compensation must then be judged against its own feedback and placement reference, rather than inherited from the sensor category.

Recent research on metering mechanisms further demonstrates that monitoring results cannot be interpreted separately from seed dynamics. Discrete element method simulations and bench experiments on centralized rapeseed delivery systems have associated seed tube interactions and seed-motion characteristics with distribution quality [26]. Similarly, high-speed maize-metering systems have modified seed posture, centrifugal geometry, and electric-drive operation to maintain metering performance [27,28]. Sensor-validation stud-ies should therefore describe the tested metering mechanism, seed-delivery geometry, seed properties, and operating conditions rather than treating the seed tube as an independent and context-free measurement channel.

The stages in Figure 6 are defined by demonstrated function, not simply publication year. Stage A comprises post-run metering or spacing assessment [3,35,36], Stage B com-prises mechanism and transport optimization [23,24,25,26,27,28], Stage C comprises direct monitoring or connected acquisition [9,10,22,32,33,34,37,51], and Stage D comprises oper-ational image interpretation or automated compensation [49,50,56,57,58]. Studies may oc-cupy more than one stage, and the functions coexist. The figure is a narrative map of rep-resentative examples, not a chronological claim that all systems achieved each stage or a bibliometric ranking.

Figure 6. Overlapping functional stages of metering assessment, physical optimization, monitoring, and compensation. Arrows indicate the narrative progression from metering assessment through physical optimization and monitoring to compensation. The functions overlap and may coexist within one system, the arrows do not establish a universal chronology, causal relationship, or achieved readiness level.

Adoption depends on integration burden as well as the demonstrated measurement target. Optical modules may be retrofitted where tube geometry permits, while imaging adds protection, illumination, and processing requirements. Multimodal systems add calibration and synchronization demands. These are architectural considerations rather than measured cost or power rankings. Whole-system maturity must be assessed across sensing, processing, communication, actuation, and independent validation, and cannot be inferred from one laboratory accuracy value.

Figure 7 summarizes technology maturity as a set of evidence boundaries and deployment gates. Evidence strength refers to independently supported observations in the reviewed catalog; functional maturity refers to the target actually demonstrated; deployment readiness additionally requires robustness, timing, integration, maintenance, and field validation. These dimensions are not interchangeable. The map is qualitative and does not assign numerical technology readiness levels or certify commercial readiness. It links the evidence in Table 3 and Table 8 to the research priorities discussed in Section 9. Table 8 brings together the mechanism, monitoring, and control evidence, Table 9 presents the proposed research roadmap.

Figure 7. Qualitative technology maturity map distinguishing the current evidence base from validation requirements for broader deployment of seed-monitoring and control technologies.

Table 8. Representative mechanism, monitoring, and control studies classified by measurement target, reference method, and evidence boundary.

Table 9. Priority roadmap for precision seed-monitoring research.

9. Limitations and Future Research

The immediate research priority is the standardization of laboratory validation procedures. Common seed event definitions, independent ground truth measurements, confusion matrices, latency decomposition, detailed crop and seed characteristics, and quantified disturbance conditions are required to enable meaningful comparisons among sensing technologies. Dust, vibration, operating speed, overlapping seeds, electrical interference, sensor misalignment, and fouling should be introduced systematically during testing. Their effects on detection performance should be quantified rather than reported only as qualitative limitations.

The second priority is synchronized, multi-row field validation. Such experiments should include synchronized clocks, bounded communication delays, reliable row identification, and controlled fault injection. Seed passage measurements should be evaluated together with meter-state signals and, whenever feasible, independently measured in-furrow placement or crop-emergence data. Once these requirements have been satisfied, closed-loop correction can be assessed using response time, correction error, stability, overshoot, unnecessary actuation, and recovery from sensor, communication, or actuator faults.

Adoption requires interoperability, affordable installation, manageable power and maintenance, cybersecurity, and clear data ownership. Table 9 orders author-proposed work by prerequisite dependence: a verified reference is needed before robust sensing, robust sensing before scale-up, and measured timing before verified correction. The sequence is not a scored literature ranking and does not imply that lower-numbered topics have greater economic value. Cost and power must be measured at the system boundary, including sensor, controller, network, protection, and actuation; component prices or processor classes alone are insufficient [71].

The main review limitations are incomplete historical search documentation and incompletely verified source fields. These prevent a claim of exhaustive coverage or a formal cross-technology ranking. Appendix B distinguishes reconstructed search expressions from original logs. Additional contextual references strengthen technical and commercial context, but do not retrospectively establish database yields, independent screening, or missing full-text extraction. Results from unavailable or unconfirmed source fields remain excluded from quantitative maturity claims.

No validated risk-of-bias tool or weighted quality score was used, and independent duplicate appraisal is not documented. The qualitative domains in Table A4 support cautious interpretation but cannot remove selection or assessment bias. Table A2 and Table A3 distinguish reconstructed search expressions from missing historical documentation.

10. Conclusions

The reviewed evidence forms a chain of distinct claims: mechanism studies explain how errors arise; direct sensors observe passage or flow; machine-state sensors help interpret operation; controllers change an actuator; and independent placement or emergence measurements verify the downstream outcome. Optical passage measurements are the most repeatedly demonstrated direct-event approach in this catalog. Vision adds spacing or carrier-state information in controlled and crop-specific applications. Capacitive bulk flow and timing applications should not be promoted to independent single seed detection without appropriate validation.

Drive-speed feedback, automatic seed-event compensation, and independently verified placement correction are separate achievements. No sensor category inherits a complete closed-loop maturity grade from a favorable counting value, and no motor-speed result proves seed placement. Missing reference, delay, disturbance, or replication data limit the strength of a claim without demonstrating that the underlying technology is ineffective. Study-specific values are therefore interpreted within their target and operating conditions rather than averaged across incompatible metrics.

The next practical step is to join independently referenced events with geometry-aware corrective action under disturbed, synchronized multi-row operation. Standardized errors, measured worst-case delay, verified placement recovery, and transparent cost, power, maintenance, interoperability, security, and ownership information would make deployment decisions more defensible. AI can support interpretation and prediction when its dataset and timing evidence meet these same requirements. The review’s historical search records and unresolved source extractions require completion before the evidence base can be described as exhaustive.

Author Contributions

Conceptualization, A.M., M.N.R. and S.-O.C.; methodology, A.M., M.N.R. and S.-O.C.; software, A.M.; formal analysis, A.M., M.N.R., K.Y.L., M.M.R., Y.I.M. and D.-H.L.; investigation, D.-H.L. and S.-O.C.; data curation, A.M., M.N.R., K.Y.L., M.M.R., Y.I.M. and D.-H.L.; writing—original draft preparation A.M.; writing—review and editing, A.M., M.N.R., D.-H.L., and S.-O.C.; visualization, A.M., K.Y.L., M.M.R. and Y.I.M.; supervision, S.-O.C.; project administration, S.-O.C.; funding acquisition, S.-O.C. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET), through the Agriculture and Food Convergence Technologies Program for Research Manpower Development, funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) (Project No. RS-2024-00397026), Republic of Korea.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this review. Data is contained within the article.

Conflicts of Interest

Author Yong Il Mun was employed by Hwang Geum Seeders, Pyeongtaek, Republic of Korea. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare that the company had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Appendix A

Table A1. Main characteristics of studies included in the qualitative synthesis.

Appendix B. Search Documentation and Qualitative Appraisal

Table A2. Reconstructed database formulations for an auditable rerun.

Before submission, the historical log should identify the actual Boolean structure, fields, database coverage, limits, execution dates and source-specific record counts. It should also record the number and independence of screeners, disagreement resolution, and full-text exclusions by reason. The available totals cannot resolve these items, so the reconstructed expressions must not be substituted for missing records.

Table A3. Search and screening audit: documented scope and unresolved historical fields.

Table A4. Qualitative engineering appraisal domains and limits. No formal risk-of-bias score was calculated.

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