IoT-Based Nitrogen, Phosphorus, and Kalium Nutrient Control for Improving Morphological and Physiological Quality of Brassica rapa L.

2.1 The evolution of precision agriculture through the Internet of Things

A central component of this paradigm is the IoT, which utilizes wireless sensors and communication networks to gather high-accuracy micro-environmental data. Consequently, agronomic decision-making has transitioned from being reactive, based on manual observation, to being data-driven, thereby supporting more efficient resource management [16, 17]. Contemporary literature positions IoT-based systems not merely as automation tools, but as instruments for optimizing agricultural inputs, such as water and nutrients. For instance, research by Noerhayati and Mustika demonstrates the implementation of an IoT-integrated drip irrigation system utilizing soil sensors to enhance water and nutrient use efficiency in lettuce (Lactuca sativa). Their findings illustrate how IoT enables the adjustment of irrigation and fertilization schedules based on the plant's actual requirements, directly impacting both crop productivity and quality [18, 19]. IoT is also widely reported to reduce human error in monitoring agronomic parameters—such as soil temperature, moisture, and pH—making cultivation decisions more precise and responsive to environmental fluctuations [10, 20, 21]. This automated, periodic measurement aligns with the needs of farming systems that must adapt to dynamic field microclimates. Furthermore, the implementation of IoT in contexts like urban farming and small-scale agriculture has gained significance, as spatial and resource limitations demand more efficient and sustainable cultivation systems. Through connected environmental monitoring and centralized data computation, IoT helps maximize the biological efficiency of plants per unit area, a critical aspect in supporting urban food security.

Collectively, the integration of IoT within precision agriculture provides a technological foundation enabling a shift from conventional practices toward more efficient, adaptive, and environmentally considerate cultivation systems [22, 23]. This evolution aligns with the principles of sustainability and resource optimization in modern agriculture.

2.2 The dynamics of smart fertigation and specific nutrient management

Building on its role in data acquisition, the IoT has empirically proven capable of mitigating subjectivity and human error in agronomic management. Through integrated smart sensors, edaphic parameters such as soil temperature, moisture, and pH can be monitored via high-resolution telemetry, yielding cultivation decisions that are both precise and adaptive to microclimatic fluctuations. The synchronization of this data into a central system optimizes the biological efficiency per unit area, a fundamental prerequisite for strengthening local food resilience. From a holistic perspective, IoT integration in precision agriculture facilitates a socio-technical transition from conventional methods toward sustainable agroecological systems, where resource optimization is aligned with the principle of minimizing environmental impact [24, 25].

2.3 Energy sustainability through off-grid photovoltaic systems

The synergy between renewable energy and smart agricultural architecture is a fundamental prerequisite for decarbonizing the agricultural sector, particularly for digital, IoT-dependent cultivation systems. In such ecosystems, integrating off-grid photovoltaic (PV) systems serves not merely as an alternative power source but as an instrument for achieving energy independence. It ensures the continuous operation of sensors and actuators without reliance on the stability of conventional power grids. Literature by the study [26] affirms that transitioning to solar power within fertigation mechanisms can integrate circular economy principles into nutrient management, where long-term operational cost efficiency aligns with a reduced carbon footprint. In the context of urban farming in regions characterized by microclimatic fluctuations and primary energy infrastructure challenges, such as Indonesia, solar energy utilization provides additional systemic resilience. This approach shifts the paradigm of agricultural technology from a mere productivity tool to an adaptive ecological engineering solution. By leveraging centralized data computation powered by clean energy, smart farming systems can maintain high precision under dynamic environmental conditions [27, 28]. This simultaneously supports local and global Sustainable Development Goals (SDGs).

2.4 Physiological response and morphological plasticity in spinach (Spinacia Oleracea)

Spinach (Spinacia oleracea) is a physiologically sensitive crop whose growth trajectory and root system architecture are directly influenced by the dynamics of nitrogen (N) availability and soil moisture [29, 30]. In environments with high and consistent resource supply, such as those created by IoT-based precision fertigation, plants exhibit significant morphological plasticity as an adaptation mechanism to microclimatic stability [31].

Referring to the Optimal Partitioning Theory, plants adopt biomass allocation strategies based on the principle of efficiency toward the limiting growth factor [32]. When water and nutrients are abundantly and homogeneously available in the root zone, the plant's need to develop an extensive root system for exploration is reduced [33]. This phenomenon explains why smart fertigation systems often result in lower root mass compared to conventional methods, without compromising above-ground growth traits. In this context, reduced root biomass is not an indicator of nutrient stress but rather a reflection of metabolic optimization. The plant reduces its energetic investment in nutrient-foraging organs and reallocates resources to maximize the function of photosynthetic organs (leaves) [34]. This adaptive strategy demonstrates that precise environmental control can enhance a plant's biological efficiency through a more productive distribution of biomass.

2.5 Identification of research gaps

Although discourse on IoT applications in agriculture has accelerated, the majority of literature remains focused on large-scale industrial implementations or rigidly controlled laboratory environments. This has created a disconnect between theory and empirical evidence regarding the performance of IoT systems in dynamic, open-field conditions at small-to-medium scales, particularly in tropical regions. This study identifies three critical research gaps in the current literature:

1. Spatial Validation: Limited data exist on the extent to which nutrient distribution homogeneity and microclimatic stability can be maintained within a single cultivation bed under external environmental fluctuations.
2. Morphological Trade-off: There is a lack of understanding regarding the impact of input efficiency on biomass allocation plasticity, specifically the phenomenon of reduced root biomass in Brassica rapa L. under precision fertigation regimes.
3. Energy Autonomy: The effectiveness of off-grid solar systems in ensuring the continuity of real-time soil nutrient (NPK) telemetry for local farmer applications requires further evaluation.

This study aims to address these gaps by evaluating the performance of a solar-powered smart fertigation system through a comparative analysis of agronomic parameters and micro-environmental stability. This approach was expected to provide a significant empirical contribution toward developing a framework for precision agriculture that is adaptive, efficient, and aligned with global sustainability targets in real-world farming ecosystems.

4.1 Experimental design and data acquisition

The system was deployed in a field-scale Brassica rapa L. cultivation bed to evaluate its capability in maintaining environmental homogeneity and nutrient stability. To rigorously test the distribution efficiency of this smart fertigation system, monitoring was conducted using two sensor nodes positioned at opposite extremes: the Inlet (U1) near the primary pump, and the Distal End (U2) at the furthest point. By comparing soil parameters, including pH, temperature, EC, and NPK levels at these two opposite nodes, the study aimed to quantify the IoT system's capacity to prevent spatial imbalances and ensure uniform resource delivery to all plants.

The experimental setup was carefully designed to ensure valid statistical power. This study was conducted in a Completely Randomized Design (CRD) with 2 treatment groups. To ensure reliability, each treatment was replicated 3 times, resulting in a total of 6 independent experimental plots (beds). Each bed contained 30 Brassica rapa L. plants, meaning a total of 180 plants were planted during the study. From each plot, 10 plants were selected as samples for observation of plant growth and yield variables. The reported morphological and physiological values represent statistical averages across replicated plots for each treatment, not from a single bed, thus validating the statistical significance of plant responses to the IoT-based fertigation system.

4.2 Statistical analysis: Spatial uniformity assessment

To determine the statistical significance of the observed data and validate the hypothesis of nutrient distribution uniformity, a one-way analysis of variance (ANOVA) was employed. This statistical framework was utilized to identify whether significant deviations existed between the environmental readings at the inlet (U1) and the distal terminal (U2).

Statistical analysis was performed using a one-way ANOVA based on a CRD. Prior to the analysis, the fundamental assumptions of parametric statistics were tested. Data normality was verified using the Kolmogorov-Smirnov test, while the homogeneity of variance was assessed using Levene’s test. Results indicated that all datasets satisfied these assumptions ($p>0.05$). For variables showing significant differences, a Tukey’s HSD post-hoc test was applied at a 95% confidence interval ($\alpha=0.05$) to evaluate the specific differences between the IoT-integrated and conventional fertigation treatments.

The stability of soil nutrients and environmental parameters was monitored daily to evaluate the system's performance in ensuring resource uniformity. Table 2 presents the statistical summary of the collected data, expressed as mean values ± standard deviation (SD), to demonstrate the consistency of the IoT-based fertigation. The results indicate a high degree of correlation between the inlet and distal nodes, with minimal deviations, confirming that the automated control logic successfully maintained nutrient concentrations within the optimal growth range for Brassica rapa L.

Table 2. Average soil nutrient and environmental stability during the cultivation period

By assessing the variance across these critical points, the ANOVA provides a quantitative measure of the system’s precision in maintaining a stabilized micro-environment. A summary of the resulting statistical metrics, including the distribution of macronutrients and physical soil parameters, is presented in Table 1.

The statistical analysis provides robust empirical validation of the system's core functionality. As summarized in Table 3, all monitored parameters exhibited no statistically significant variation between the inlet (U1) and distal terminal (U2) of the cultivation bed, with P-values consistently exceeding the 0.05 threshold. This finding, clearly illustrated by the overlapping confidence intervals in Figure 2, confirms that the automated system effectively eliminated the spatial heterogeneity typically inherent in open-field fertigation, establishing a uniform rhizosphere environment across the experimental unit.

Table 3. Summary of one-way ANOVA results assessing the spatial homogeneity of edaphic and nutrient parameters between the inlet (U1) and distal (U2) monitoring nodes

Note: ANOVA = analysis of variance

4.2.1 Precision in spatial nutrient distribution

The system demonstrated exceptional precision in macronutrient delivery. As detailed in Table 1, concentrations of nitrogen (N), phosphorus (P), and potassium (K) remained consistent along the entire longitudinal axis of the bed, a uniformity graphically depicted in Figure 3. This result was critical, as conventional systems often exhibit a decline in nutrient concentration with distance from the injection point due to factors like sedimentation and pipeline friction losses. The observed homogeneity indicates that the logic-driven control mechanism successfully compensated for these hydraulic dynamics, ensuring equidistant plants received an identical nutrient solution. This precision directly supports the ecological engineering objective of maximizing improved nutrient uptake while preventing the localized stress responses caused by nutrient imbalances.

Figure 3. Comparative analysis of mean ± standard deviation (SD) for six key environmental parameters: Nitrogen (N), Phosphorus (P), Potassium (K), soil pH, soil temperature, and electrical conductivity (EC). Blue bars represent the inlet position (U1), while orange bars represent the end-point position (U2)

4.2.2 Homogeneity of soil physicochemical conditions

A parallel uniformity was observed in key soil physicochemical properties. Parameters including pH, temperature, and electrical conductivity (EC) show no significant spatial gradients between the monitored nodes (Table 1). This consistency indicates that the IoT framework effectively regulated the irrigation regime to buffer the rhizosphere against the patchy wet-dry cycles characteristic of manual watering. By ensuring even water distribution, the system maintained a stable and predictable soil environment, which was fundamental for optimal root function and microbial activity.

In synthesis, the data presented in Table 1 move beyond technical validation to demonstrate a substantive agroecological benefit. The IoT-based fertigation system proved to be a fundamental tool for imposing micro-environmental equilibrium. The achieved homogeneity was a prerequisite for synchronized crop development and physiological stability within a cultivation unit, thereby enhancing both yield predictability and the overall resilience of the production system.

4.3 Visualization of microclimate homogeneity

This visualization effectively illustrates the homogeneity achieved by the smart fertigation system across the cultivation bed.

U (N, P, and K) monitored over the 30-day period were visualized in Figure 3 using box-and-whisker plots. This visualization provides a clear statistical comparison between the inlet (U1) and distal node (U2). The data show that the Nitrogen (N) levels at the inlet remained stable with a median of 58.2 mg/kg, while the distal node exhibited a slightly lower but consistent distribution. The narrow interquartile range (IQR) across all parameters confirms the precision of the IoT-based fertigation system in maintaining nutrient homogeneity, which was essential for the uniform growth of Brassica rapa L.

4.4 Comparative assessment of plant morphological and agronomic responses

To evaluate whether the precision of the IoT system confers tangible agronomic advantages, a comparative assessment of key morphological markers—plant height, leaf count, and root biomass—was conducted against conventional cultivation methods. The statistical summary of these parameters was provided in Table 4, with visual comparisons presented in Figure 3.

Table 4. Comparative statistics of plant growth parameters between Internet of Things (IoT)-based and conventional systems

Conversely, the IoT-based system, by maintaining edaphic stability and a consistent nutrient flux within the rhizosphere, reduces the metabolic cost of resource acquisition. The reliable input regime diminishes the plant's need to invest in extensive root architecture for survival, resulting in the observed leaner root system. The IoT system thus achieves equivalent above-ground growth with a more efficient, economically streamlined physiological structure.

The distribution of growth data was further analyzed using z-score normalized histograms (Figure 4). The pronounced overlap between the distributions for both systems indicates that the observed morphological variation resides primarily within the bounds of natural biological plasticity. Crucially, the absence of distributional extremes confirms that the significant root biomass difference was a targeted adaptive response to the IoT system's stable environment, not an artifact of systemic instability or error.

Figure 4. Standardized (z-score) frequency distribution of growth parameters for IoT and conventional systems

In synthesis, this integrated morphological analysis confirms that the IoT framework successfully engineers a growth environment where plant development is governed by precision and metabolic efficiency, rather than by stress-driven resource exploration. This shift was fundamental to advancing sustainable, high-efficiency agroecological systems.

4.5 Physiological characterization and phytochemical integrity

Building on the morphological assessment, this analysis examines the internal physiological status and nutritional quality of the Brassica rapa L. crop to determine if the IoT system's environmental stability translates into enhanced metabolic health and preserved phytochemical integrity.

4.5.1 Photosynthetic efficiency and chlorophyll density

The efficiency of the photosynthetic apparatus was gauged through spectral absorbance measurements, with key results detailed in Table 5.

The data reveal a consistent trend: plants under the IoT system exhibited higher absorbance values at the critical wavelengths of 663 nm and 665 nm across both diluted and undiluted samples. These spectral signatures were primary indicators of Chlorophyll *a*, the central pigment in the light-harvesting complex. Although the number of biological replicates limited formal statistical inference, this observational trend suggests that the IoT-managed plants maintained a more robust concentration of key photosynthetic pigments.

Table 5. Spectral absorbance analysis of chlorophyll content: (a) Diluted and (b) undiluted extracts

From an ecophysiological perspective, this stability can be attributed to the minimized environmental stress afforded by the autonomous control system. By stabilizing rhizosphere moisture and temperature—as quantitatively demonstrated in Section 4.3—the IoT system likely mitigates the thermal and osmotic degradation of chlorophyll that commonly occurs under the fluctuating conditions of conventional management. Consequently, the framework supports sustained photosynthetic efficiency by maintaining a consistent micro-environment, allowing the plant's primary metabolism to operate within an optimal, non-stressed range.

4.5.2 Nutritional quality: Vitamin C content

The nutritional value of the harvest was assessed by quantifying ascorbic acid (Vitamin C) content, with results summarized in Table 6.

Table 6. Comparative Vitamin C content analysis (mg/100 g fresh weight)

The analysis demonstrates parity in Vitamin C levels between the two cultivation regimes (0.40 mg/100 g). This finding was significant for the broader adoption of precision agricultural technologies, as it confirms that the operational efficiencies and resource conservation achieved through IoT automation do not compromise the nutritional density of the crop.

4.6 Deployment portability and system scalability

The modular architecture of the proposed IoT system ensures high portability across various agricultural contexts. While this study focuses on Brassica rapa L., the system’s control logic can be easily recalibrated for other leafy greens or high-value crops by adjusting the nutrient set-points in the firmware. From a scalability perspective, the use of the RS485 Modbus protocol allows for the integration of up to 247 sensor nodes over long distances (up to 1,200 meters), making it suitable for large-scale greenhouse operations. Furthermore, the solar-powered energy harvesting unit provides the necessary autonomy for deployment in remote regions with limited grid access, ensuring adaptability to diverse climates and geographical locations.

4.7 Synthesis and implications for ecological engineering

Collectively, these physiological and phytochemical results validate the proposed IoT-based approach within a sustainable food systems framework. The system successfully harmonizes high-precision resource management with the production of nutritionally stable, high-quality produce. By ensuring that both photosynthetic efficacy and key phytochemical profiles were maintained—or even enhanced—the technology proves to be a viable ecological engineering solution. It supports the dual objectives of optimizing the ecological footprint of intensive cultivation while safeguarding the dietary standards required for functional food security.

4.8 Synthesis: Ecological implications of precision farming

The synthesis of empirical findings from environmental data (Sections 4.1–4.4) and plant physiological responses (Sections 4.5–4.6) converges on a central conclusion: the IoT framework acts as a transformative tool for engineering sustainable agroecosystems. The system demonstrably created a homogenized rhizosphere, characterized by uniform nutrient distribution and stable microclimatic conditions, which in turn elicited a distinct, efficiency-oriented plant phenotype.

4.8.1 Uniform physiological response and biomass partitioning

The stable environment engineered by the IoT system precipitated a fundamental shift in the Brassica rapa L. crop’s energy allocation strategy. The significant reduction in root biomass, juxtaposed with maintained or enhanced chlorophyll density, signals a transition from a stress-adapted “resource foraging” phenotype to one optimized for “metabolic efficiency.” In conventional systems, plants invest substantial energy in extensive root architectures as a risk mitigation strategy against fluctuating resource availability. The precision and reliability of the IoT-regulated fertigation regime lower the metabolic cost of resource acquisition. This reallocation of energy from defensive root expansion to the maintenance of photosynthetic machinery and above-ground productivity exemplifies a core principle of ecological engineering: enhancing system output by optimizing internal resource flows rather than maximizing external inputs.

4.8.2 Energy autonomy and system-wide sustainability

The agronomic benefits were underpinned by a design that addresses systemic sustainability. The off-grid, PV-powered architecture (Section 3.3) ensures operational resilience and decouples precision agriculture from fossil-fuel-dependent infrastructure. This energy autonomy not only reduces the carbon footprint of production but also enhances the system’s applicability in remote or resource-constrained settings, a critical factor for decentralized food security. It embodies the integration of technological innovation with ecological principles, moving beyond simple automation to create a self-sustaining cultivation node.

4.8.3 A framework for precision ecological engineering

Collectively, this study demonstrates a practical model for precision ecological engineering in agriculture. The IoT system functions as a dynamic regulator, continuously aligning edaphic inputs with plant physiological demand. This closed-loop management harmonizes crop growth with resource provision, minimizing waste and environmental stress. The result was a production system that achieves biological efficiency and product quality not through input intensification, but through intelligent system design and control.

In conclusion, the integration of solar-powered IoT infrastructure represents a substantive advance in sustainable cultivation. It forges a synergistic link between technological precision, renewable energy, and plant ecophysiology. This approach charts a viable pathway for contemporary agriculture, where productivity and nutritional quality are sustained by engineered system intelligence and precise water and nutrient delivery, ultimately reducing the ecological footprint of food production.

The authors are grateful to the Ministry of Higher Education, Science, and Technology and Universitas Islam Malang for the financial support granted to cover the publication fees of this research article.