ELKHA : Jurnal Teknik Elektro.
Vol.
18 No.
April 2026, pp.
1 - 9
ISSN: 1858-1463 .
, 2580-6807 .
Adaptive Low-Power LoRa WSN for Real-Time Soil Monitoring in Remote Oil Palm Plantations Syahfrizal Tahcfulloh1*).
Michael Yehezkiel Rattu.
Etty Wahyuni.
Irawan Wijaya Kusuma.
Irawati HM.
Dwi Santoso.
Nina Fapari Arif.
Nur Fatwa.
Rusdy Setiawan.
, and Arwan.
Department of Electrical Engineering.
Universitas Borneo Tarakan.
Tarakan.
Indonesia Department of Agribusiness.
Universitas Borneo Tarakan.
Tarakan.
Indonesia Faculty of Forestry.
Universitas Mulawarman.
Kalimantan Timur.
Indonesia Department of Accounting.
Universitas Borneo Tarakan.
Tarakan.
Indonesia Department of Agrotechnology.
Universitas Borneo Tarakan.
Tarakan.
Indonesia Universitas Pembangunan Nasional AuVeteranAy Yogyakarta.
Yogyakarta.
Indonesia Middle East and Islamic Studies.
School of Strategic and Global Studies.
University Indonesia.
Jakarta.
Indonesia Department of Development Economics.
Universitas Borneo Tarakan.
Tarakan.
Indonesia Corresponding Email: *) syahfrizal@borneo.
Abstract Ae Oil palm plantations in remote regions such as Sebatik.
North Kalimantan, face significant challenges in sustainable soil management due to limited infrastructure and dynamic peat soil conditions.
Conventional monitoring methods lack real-time capability and energy efficiency.
address this, this research proposes a novel adaptive lowpower LoRa-based Wireless Sensor Network (WSN) that dynamically adjusts sensing and transmission frequency based on real-time soil parametersAispecifically, moisture, temperature, and pH.
Unlike fixed-interval systems, the proposed architecture implements edge-based logic on ESP32 nodes to escalate sampling during critical events .
pH O 4.
and reduce activity during stable periods, optimizing energy use without cloud dependency.
The system integrates LoRa SX1278 modules, a RAK2245 gateway.
ChirpStack for secure data routing, and OpenRemote for visualization and alerts.
Field testing over 7 days in three micro-zones .
oadside, plantation center, drainag.
demonstrated robust performance with average Packet Delivery Ratios of 97.
2%, 82.
5%, and 88.
3%, respectively, and a communication range of up to 2.
8 km.
Crucially, the adaptive strategy reduced daily power consumption to 7.
mAhAi58% lower than a fixed 10-minute scheduleAi extending theoretical battery life from 6Ae8 months to over 14 Sensor accuracy remained high .
oisture error:
temperature: 3.
pH: 1.
47 unit.
, enabling timely agronomic interventions such as targeted liming.
This work contributes an environment-responsive WSN architecture that balances energy efficiency and event responsiveness, offering a scalable, deployable model for precision agriculture in tropical peripheral regions with acidic soils and intermittent connectivity.
Keywords: Adaptive WSN.
LoRa, low-power IoT, oil palm plantation, soil monitoring.
INTRODUCTION
Palm oil plantations are among the strategic sectors in IndonesiaAos economy, contributing significant foreign exchange earnings while absorbing millions of workers.
However, challenges in sustainably managing plantations Manuscript received 2025-10-20.
revised 2026-02-22.
accepted 2026-02-24 continue to rise, particularly concerning the efficient use of resources such as water, fertilizer, and energy.
One key factor in oil palm productivity is soil conditionAiincluding moisture, temperature, pH, and nutrient contentAiwhich must be monitored regularly and accurately .
Conventional methods, still widely used, such as manual sampling and laboratory analysis, are reactive, timeconsuming, and unable to provide real-time information, often hindering operational decision-making in the field.
The advancement of Internet of Things (IoT) technology presents significant opportunities for digital transformation in the agricultural sector, particularly through the implementation of Wireless Sensor Networks (WSN) capable of automatically and continuously monitoring environmental parameters .
However, the main challenges in deploying WSN in large plantation areas are limited communication range and high power Conventional wireless technologies such as Wi-Fi and Bluetooth have limited coverage and inefficient power consumption for long-range applications .
, .
, while cellular networks like NB-IoT require complex infrastructure and high operational costs .
, .
In this context.
LoRa (Long Rang.
technology operating under the LoRaWAN architecture emerges as a highly promising solution.
LoRa offers communication ranges of several kilometers with low power consumption, making it ideal for smart agriculture applications in remote and expansive areas such as palm oil plantations .
Studies by .
demonstrate that LoRa-based sensor networks can improve irrigation efficiency by up to 30% through real-time soil moisture monitoring in large-scale agricultural fields.
Moreover.
LoRaAos ability to support thousands of nodes per gateway makes it highly scalable and cost-effective .
Nevertheless, most existing LoRa-based WSN implementations adopt static duty-cycling strategies, where sensor nodes transmit data at fixed intervals regardless of actual field conditions.
This approach leads to unnecessary energy expenditure in stable environments -1- This work is licensed under a Creative Commons Attribution 4.
0 License For more information, see https://creativecommons.
org/licenses/by-nc-sa/4.
Adaptive Low-Power LoRa WSN for Real-Time (S.
Tahcfulloh, et al.
and missed critical events during rapid changes in soil parametersAiparticularly problematic in dynamic ecosystems such as oil palm plantations on acidic peat soils, where moisture and pH levels can fluctuate significantly due to rainfall, drainage, and organic To address this limitation, this research introduces a novel adaptive power management architecture for LoRabased WSN, specifically tailored to the environmental dynamics of remote oil palm plantations.
The proposed system dynamically adjusts the sensing and transmission frequency based on real-time soil conditions: .
Highfrequency sampling is triggered when soil parameters .
, moisture or pH) approach critical thresholds, and .
Low-power sleep mode is activated during stable conditions to conserve energy.
This context-aware strategy leverages lightweight edge logic on the ESP32 microcontroller to balance data granularity and battery longevity without relying on cloud-based decision enginesAimaking it suitable for areas with intermittent connectivity such as Sebatik.
North Kalimantan.
Furthermore, the system integrates open-source platforms (ChirpStack and OpenRemot.
for end-to-end data management, visualization, and automated alerts, ensuring both technical robustness and practical usability for plantation managers.
By embedding environmentresponsive intelligence directly into the node firmware, this work advances beyond conventional Aualways-onAy or fixed-interval WSN designs, offering a sustainable, adaptive, and deployable solution for precision agriculture in tropical periphery regions.
This research thus contributes not only a functional monitoring system but also a new architectural paradigm for energy-aware WSN deployment in resource-constrained agroecosystems, with potential applicability to other perennial crops in similar Functional Requirements The system continuously monitors soil moisture, temperature, and pH, dynamically adjusting sampling and transmission frequency when parameters approach critical thresholds .
H < 4.
8, moisture < 60%).
Data are transmitted via LoRa to ChirpStack and OpenRemote for real-time visualization and automated alerts within optimal ranges .
H 4.
5Ae6.
5, temperature 24Ae32AC, moisture 60Ae 80%), enabling precision interventions such as targeted liming and irrigation scheduling.
Non-Functional Requirements The system achieves ultra-low power consumption for six-month battery operation while maintaining a 1Ae3 km communication range under dense oil palm vegetation.
withstands tropical conditionsAihigh humidity, rainfall, and temperatures exceeding 35ACAithrough edge-based decision logic on the microcontroller that minimizes transmissions and extends battery life without cloud dependency, enabling reliable deployment in remote areas with intermittent connectivity.
Adaptive System Design .
System Architecture The system architecture (Figure .
comprises adaptive ESP32-based sensor nodes integrating moisture, temperature, and pH sensors with SX1278 LoRa modules that transmit to a RAK2245 gateway with 4G backhaul.
ChirpStack v4.
0 handles secure packet routing and authentication, while OpenRemote v2024.
12 provides PostgreSQL data storage, real-time visualization, and rulebased alerts via a responsive web dashboard.
II.
METHODOLOGY
This research employs a system development approach grounded in the Internet of Things (IoT) to design and implement a context-aware, low-power Wireless Sensor Network (WSN) for real-time soil monitoring in remote oil palm plantations.
Unlike conventional fixed-interval WSN architectures, the proposed system introduces an adaptive power management strategy that dynamically adjusts sensing and transmission frequency based on real-time soil conditionsAispecifically tailored to the environmental dynamics of acidic peat soils in Sebatik.
North Kalimantan.
The methodology encompasses requirement analysis, adaptive system design, hardware-software coimplementation, sensor calibration, network performance evaluation, and field validation.
Figure 1.
LoRa and OpenRemote-based soil monitoring system .
Adaptive Power Management Logic The core novelty is a context-aware duty-cycling algorithm embedded in ESP32 firmware (Figure .
that operates through four adaptive states.
In baseline mode under stable conditions, sensors sample every 30 minutes and transmit only when changes exceed 5% moisture, 0.
pH units, or 1AC.
Alert mode activates when parameters approach critical zones .
H < 4.
8 or moisture < 65%), increasing sampling to every 10 minutes.
Critical mode triggers upon threshold breach .
, pH O 4.
, escalating to 2-minute sampling for one hour while sending urgent OpenRemote alerts.
After 24 consecutive hours within optimal ranges, the node automatically returns to baseline mode to conserve energy.
System Requirements Analysis Requirement analysis was conducted in collaboration with agronomists and plantation managers to ensure alignment with operational needs in SebatikAos unique agroecosystemAicharacterized by high humidity, acidic peat soil .
H 4.
0Ae5.
, and limited infrastructure.
-2-
Adaptive Low-Power LoRa WSN for Real-Time (S.
Tahcfulloh, et al.
Figure 2.
Workflow of the LoRa-based soil condition monitoring system .
cIyceycuycycuycOeycIycycaycuyccycaycyc.
ycIycycaycuyccycaycycc .
Figure 3.
ESP32 LoRa as transmitter (T.
, .
ESP32 LoRa as receiver (R.
, .
temperature sensor, .
moisture sensor, and .
pH sensor This adaptive strategy reduces unnecessary transmissions by up to 60% compared to fixed 10-minute intervals .
alidated in simulatio.
, significantly extending battery life while preserving responsiveness to critical soil Sensor Calibration and Validation Calibration followed the same procedure as in the original study using certified reference instruments.
The error (%) was calculated using Equation .
yaycycycuyc(%) = .
Figure 4.
Receiver (R.
, .
Transmitter (T.
, and .
appearance of sensors in the soil and Tx y 100% a (A) Accuracy was derived as 100%OeError (%) (Equation .
Special attention was given to pH sensor drift in acidic peat soil, with recalibration protocols integrated into the maintenance plan.
Software Stack The software stack integrates three layers: edge-layer C firmware on Arduino Core for ESP32 executes adaptive duty-cycling logic.
the network layer employs ChirpStack on a virtual private server for authentication, packet routing, and MQTT forwarding.
and the application layer uses OpenRemote to store data in PostgreSQL and trigger alerts when soil parameters breach thresholds, with the dashboard interface shown in Figure 5.
yaycaycaycycycaycayc = 100% Oe yaycycycuyc(%) a (A) System Implementation .
Hardware Deployment Three adaptive nodes were deployed across distinct micro-zones in Sebatik to capture environmental heterogeneity: Node A roadside with open terrain.
Node B at the plantation center under dense 10Ae15 m canopy, and Node C near drainage channels experiencing high humidity and acidic runoff from peat decomposition.
Each node runs adaptive ESP32 firmware powered by a 3000 mAh 18650 Li-ion battery for extended field operation, with hardware details shown in Figures 3 and 4.
Figure 5.
Laptop/PC view of the web dashboard displaying soil condition monitoring results Performance Evaluation Metrics System performance was evaluated over seven days using five metrics: continuous Packet Delivery Ratio (PDR) measurements assessed network reliability under variable vegetation.
daily power consumption .
easured via INA219 senso.
quantified energy savings from adaptive sampling.
transmission frequency distribution across operational modes validated context-aware battery lifetime was extrapolated from 3000 mAh capacity under adaptive versus fixed-interval and soil variability across micro-zones confirmed agronomic relevance for precision management .
-3- Adaptive Low-Power LoRa WSN for Real-Time (S.
Tahcfulloh, et al.
Research Location Figure 6 depicts the strategic deployment of three sensor nodes across a 500 m y 250 m plantation to capture peatland soil heterogeneity .
Node A .
features open terrain with minimal canopy and stable hydrology as a baseline reference.
Node B .
lantation cente.
lies under a dense 10Ae15 m canopy that restricts solar radiation and rainfall infiltration, yielding drier Node C .
rainage-proximat.
experiences frequent waterlogging and acidic leachate from decomposing organic matter, making it critical for pH monitoring .
This tri-zonal approach ensures comprehensive spatial coverage essential for precision agriculture, as peatland soil properties can vary significantly within just 30 meters .
regardless of contex.
, our node firmware embeds lightweight decision logic to optimize energy use without sacrificing responsiveness.
During the seven-day field test, the system automatically transitioned between operational modes based on real-time soil dynamics.
In baseline mode under stable conditions, nodes sampled every 30 minutes and transmitted only when parameter changes exceeded 5% moisture, 0.
5 pH units, or 1AC.
Alert mode triggered when pH dropped below 4.
8, moisture fell under 65%, or temperature exceeded 31AC, increasing sampling to every 10 minutes.
Critical mode is activated at pH O4.
5 or moisture O60%, escalating to 2-minute sampling for one hour while triggering urgent alerts.
Figures 7 and 8 illustrate this adaptive behavior, showing transmission spikes coinciding with rapid pH drops near drainage (Node C) on Day 5, whereas Node A .
remained in baseline mode for 92% of the test period due to stable .
Figure 7.
Adaptive vs stable transmission behavior over 7 days .
Figure 6.
Data collection locations: .
Geographic coordinates of the oil palm field, and .
Aerial view of the plantation area i.
RESULTS AND DISCUSSION
Adaptive Power Management in Real-World Deployment The core innovation of this research lies in the implementation of an adaptive duty-cycling strategy that dynamically adjusts sensing and transmission frequency based on real-time soil conditionsAispecifically tailored to the environmental dynamics of acidic peat soils in SebatikAos oil palm plantations.
Unlike conventional fixedinterval systems .
, transmit every 10 minutes Figure 8.
Performance comparison: adaptive vs fixed-interval The adaptive strategy reduced transmissions by 58% compared with fixed 10-minute scheduling .
1,008
packets/nod.
, cutting daily energy consumption from 5 mAh to 7.
8 mAh and extending theoretical battery life from 6Ae8 months to over 14 months using a 3000 mAh 18650 battery.
This 58% power reduction significantly outperforms fixed-interval LoRa WSNs in recent
-4-
Adaptive Low-Power LoRa WSN for Real-Time (S.
Tahcfulloh, et al.
literature: static 15-minute sampling in Malaysian oil palm plantations consumed 14.
2 mAh/day .
, .
, while Indian irrigation systems with 10-minute intervals required 8 mAh/day .
Our edge-based logic bridges the critical gap identified by .
regarding static duty-cycling limitations in remote agriculture.
By embedding thresholdtriggered escalation directly on the ESP32 without cloud dependency, we overcome latency-energy trade-offs inherent in cloud-assisted approaches like .
, which suffer 15Ae30 second delays during outagesAiunacceptable for rapid pH events in peat soils.
This architecture advances the state of the art by demonstrating that context-aware edge intelligence achieves >50% energy savings while maintaining sub-5-minute event detection latency, a balance previously unattained in tropical periphery .
Analysis of one-week data in Figure 9 shows a consistent pattern: soil moisture is highest in the drainageproximate zone .
Ae85%), while in the plantation center it ranges only from 60Ae68%, indicating potential localized Soil temperature varied between 25Ae31AC, falling within the optimal range .
Ae32AC), but was higher at midday in the plantation center zone.
Meanwhile, soil pH was lowest near drainage areas .
2Ae4.
, indicating high acidity due to organic matter decomposition in peat soil.
These findings provide a basis for precision decisionmaking, such as adjusting irrigation schedules based on actual moisture data, locally applying lime to low-pH areas, and monitoring temperature to mitigate plant stress during dry seasons.
Figure 9.
reveals a pronounced pH gradient across the three micro-zones, with drainage-proximate areas (Node C) exhibiting critically low values of 4.
2Ae4.
8 compared to roadside zones .
1Ae5.
6 at Node A).
This 1.
4-unit difference is agronomically significant: oil palm (Elaeis guineensi.
experiences reduced nutrient uptake and aluminum toxicity below pH 4.
, directly threatening yield stability.
The sharp pH drop to 4.
2 observed at Node C on Day 5 .
oinciding with heavy rainfall in Figure 9.
) exemplifies the 'acid pulse' phenomenon documented in tropical peatlands .
, where rainwater mobilizes organic acids from decomposing litter.
Critically, our adaptive system detected this event within 2 minutes .
riggering critical mod.
, enabling lime application before prolonged root damage occurredAia response that would have been impossible with conventional weekly manual sampling.
Figure 9.
One-week monitoring results of the oil palm plantation across different node zones for the parameters: .
moisture, .
temperature, and .
soil pH LoRa Network Performance Under Adaptive Traffic Despite variable transmission loads.
LoRa network performance remained robust.
Average Packet Delivery Ratios (PDR) across the three nodes demonstrated robust network performance under adaptive traffic patterns: Node A positioned roadside achieved 97.
2% PDR with 980 packets received out of 1,008 transmitted.
Node B at the plantation centre recorded 82.
5% PDR with 832 packets received, and Node C near drainage channels attained 3% PDR with 890 packets received .
ee Figure 10 and Table .
The observed Packet Delivery Ratios .
match or slightly exceed LoRa benchmarks under similar .
reported 78Ae89% PDR for LoRaWAN in South African forests at 2.
5 km, with losses attributed to multipath fading.
Our plantation center achieved an 82.
PDR .
s 78% in .
), likely due to adaptive traffic reducing channel congestion during stable periodsAia finding consistent with .
's demonstration that variable data rates improve LoRa reliability by 8Ae12%.
Criticalmode bursts .
-minute interval.
caused no packet-loss spikes, confirming our 1-hour escalation window remains within LoRa's duty-cycle limits (<1% airtim.
In contrast, -5- Adaptive Low-Power LoRa WSN for Real-Time (S.
Tahcfulloh, et al.
Table 2.
Average Measured RSSI and SNR Across All Nodes in the Oil Palm Plantation NB-IoT alternatives .
, .
suffered 22Ae35% packet loss under intermittent 4G coverage, underscoring LoRa's superiority for infrastructure-limited tropical agriculture when paired with intelligent traffic management.
Node Table 1.
Daily Average Packet Delivery Ratio per Node Total packets Total packets SNR .
B) Sensor Accuracy and Environmental Responsiveness Calibration confirmed high sensor accuracy: moisture 68%, temperature 3.
09%, and pH 1.
47 units.
Although the pH error exceeds laboratory-grade precision (<0.
1 uni.
, it falls within the A1.
5-unit tolerance for fieldbased irrigation decisions in acidic soils .
More critically, the sensor's responsivenessAidetecting a 0.
5unit pH drop from 4.
7 to 4.
2 within three hours (Figure 9.
)Aiproves more valuable than absolute accuracy for triggering timely agronomic interventions.
This aligns with Faridah et al.
, who emphasized that trend detection outweighs absolute precision when thresholds guide action.
A seven-day recalibration protocol integrated into our maintenance routine mitigates drift from electrode fouling in organic-rich peat .
Figure 10.
Packet Delivery Ratio (PDR) in the LoRa WSN network for palm oil plantation Node RSSI .
Near the road
Center of the plantation Dekat drainase PDR
(%)
Table 3.
Calibration of the Moisture Sensor Sensor (%) These values are comparable to .
nd slightly better tha.
the fixed-interval results reported in the original design, confirming that adaptive traffic does not degrade network reliabilityAieven during burst transmissions in critical The RAK2245 gateway successfully handled variable data rates, and ChirpStackAos store-and-forward mechanism ensured zero data loss during temporary 4G RSSI and SNR values (Table .
also remained within acceptable ranges, with no significant degradation during high-traffic periodsAivalidating that the adaptive strategy operates well within LoRaAos channel capacity.
Overall, the average RSSI values across all three nodes are considered good according to the RSSI signal-level criteria defined by .
, which ranges from -90 dBm to -105 dBm.
Node A falls into the Auvery goodAy category, with RSSI values between -60 dBm and -90 dBm.
Regarding SNR levels, all three nodes fall within the AunormalAy range as reported in .
, with SNR values between 0 dB and 15 Table 2 reveals a clear correlation between vegetation density and RSSI degradation: Node A .
31 dBm RSSIAiclassified as "very good" per .
Aiunder line-of-sight conditions, whereas Node B .
lantation cente.
dropped to -102.
52 dBm due to dense canopy attenuation, approaching LoRa's -104 dBm sensitivity limit at SF12.
Despite this.
PDR remained acceptable at 82.
5% (Table .
owing to adaptive traffic reducing channel contention during stable periods.
SNR
79Ae8.
91 dB) stayed above the 0 dB demodulation threshold .
, confirming a reliable link budget under Practically, gateway elevation or repeaters may be required in dense-canopy zones where RSSI consistently falls below -100 dBmAia key insight for scaling this architecture across larger plantations.
Standard (%) Average Error (%) Accuracy (%) Table 4.
Calibration of the Temperature Sensor Sensor .
C) Standard ( C) Average Error (%) Accuracy (%) Table 5.
Calibration of the pH Sensor Standard Sensor Average Error (%) Accuracy (%) Critically, the pH sensorAos responsiveness enabled timely detection of acidification events.
On Day 5.
Node C recorded a sharp pH drop from 4.
7 to 4.
2 within 3 hours following heavy rainfallAia common phenomenon in peat soils due to leaching and organic acid release.
The system immediately entered critical mode, triggering a web/email alert to plantation managers, who applied dolomite lime within 24 hours.
These findings are consistent with previous research indicating that microcontroller-based sensor systems can provide reliable data for real-time soil monitoring .
Nevertheless, the pH sensor requires periodic recalibration due to its sensitivity to acidic peat
-6-
Adaptive Low-Power LoRa WSN for Real-Time (S.
Tahcfulloh, et al.
soil conditions and susceptibility to measurement drift .
Overall, these results demonstrate that the developed sensor system achieves adequate accuracy for precision agriculture applications, particularly for continuous soil condition monitoring.
This demonstrates that contextaware sensing enhances agronomic relevance, transforming the system from a passive monitor into an active decision-support tool.
The pH sensor's 1.
47-unit error, though higher than laboratory instruments, remains within the A1.
5-unit field tolerance for irrigation decisions .
Our weekly recalibration protocol effectively counters the 0.
8Ae1.
2 pH unit drift over 30 days documented in Malaysian peat soils .
, maintaining measurement stability throughout the trialAiunlike progressive drift.
The moisture sensor's 1.
68% error also outperforms commercial TDR probes .
Ae5% in peat .
), demonstrating that low-cost capacitive sensors achieve agronomic-grade accuracy with site-specific calibration.
These results validate Faridah et al.
's .
principle that "sensor affordability should not compromise data reliability if calibration protocols are rigorously applied," a cornerstone for scalable IoT adoption in smallholder Figure 11.
Bar chart comparing daily power consumption and event detection latency across fixed vs.
adaptive strategies EnergyAeResponsiveness Trade-off Analysis A key design challenge in Wireless Sensor Networks (WSN) is balancing energy efficiency and event detection Our adaptive architecture achieves this balance by minimizing transmissions during stable environmental periods to conserve energy while simultaneously prioritizing data granularity during critical soil transitions to preserve system responsivenessAiensuring that the network remains both sustainable for long-term deployment and capable of capturing rapid agronomic events such as sudden pH drops or moisture fluctuations.
Simulation-based comparison .
ee Figure .
shows that while a 2-minute fixed interval detects events fastest .
atency OO 1 mi.
, it consumes 42.
3 mAh/dayAi unsustainable for remote deployment.
Conversely, a 60minute interval saves energy .
1 mAh/da.
but misses rapid pH shifts.
Our adaptive approach achieves nearoptimal latency .
Ae5 mi.
with moderate consumption .
mAh/da.
, offering the best compromise for oil palm .
Implications for Precision Management in Peatland Plantations Figure 12.
Ae.
reveals significant micro-zonal soil variability: moisture ranges from 7885% near drainage versus 6068% in the plantation center, supporting zonal irrigation strategies, while pH shows pronounced acidity gradients .
2Ae4.
8 near drainage versus 5.
1Ae5.
6 roadsid.
, justifying targeted liming.
The adaptive monitoring system enables precise spatiotemporal interventions when parameters deviate from optimal ranges, substantially reducing fertilizer and lime waste while minimizing environmental impactAiembodying core precision agriculture principles through data-driven, spatially explicit decision-making.
Figure 12.
Micro-zonal soil variability across plantation zones:
Moisture distribution and .
pH gradient The observed pH gradient .
2Ae4.
8 near drainage vs.
1Ae5.
6 roadsid.
's findings on hydrologically driven acidification in Sarawak peatlands, where drainage accelerates organic matter decomposition and proton release.
Our system's real-time micro-zone detection overcomes the limitation noted by Lubis et al.
that static 100-m-interval sampling misses critical pH transitions within 20Ae30 m of waterways.
By triggering alerts at pH O4.
5 (Node C.
Day .
, targeted liming reduced lime use by 40% compared with blanket application.
This -7- Adaptive Low-Power LoRa WSN for Real-Time (S.
Tahcfulloh, et al.
aligns with .
, .
precision agriculture frameworks emphasizing spatially explicit interventions based on realtime heterogeneity.
Consequently, our WSN shifts soil management from calendar-based routines to event-driven actions, directly supporting ISPO certification requirements for input optimization.
REFERENCES