arXiv CSopen access

Direction-Dependent Path Loss Modeling in Olive Orchards for Precision Agriculture

2026 · arxiv_cs

arXiv CS · Papers · License: Open Access · 2026

distributed-systems internet networking protocols

networking, internet, protocols, distributed systems

Received Month DD, YYYY; accepted Month DD, YYYY; date of publication Month DD, YYYY; date of current version Month DD, YYYY. Digital Object Identifier 10.1109/ACCESS.XXXXXXX

Direction-Dependent Path Loss Modeling in Olive Orchards for Precision Agriculture MOHAMMAD ROWHANI SISTANI123 , KATARZYNA KOSEK-SZOTT4 , PIERLUIGI GALLO15

Department of Engineering, University of Palermo (UNIPA), Palermo, Italy School of Advanced Studies, University of Camerino (UNICAM), Camerino, Italy SEEDS s.r.l., Italy 4 Department of Telecommunications, AGH University of Science and Technology, Krakow, Poland 5 CNIT – Consorzio Nazionale Interuniversitario per le Telecomunicazioni, Italy

arXiv:2604.19427v1 [cs.NI] 21 Apr 2026

2 3

Corresponding author: Mohammad Rowhani Sistani (e-mail: [email protected]) This work was supported in part by the SMOOL Project (Smart Olive Oil Traceability).

ABSTRACT Wireless links deployed in orchards often exhibit significant variability in the strength of the received signal that is not adequately captured by classical distance-based propagation models. In rowstructured olive groves, signal attenuation differs markedly between along-row and cross-row propagation directions, leading to discrepancies when using omnidirectional propagation assumptions such as those adopted in the Free Space Path Loss (FSPL) model or ITU-R vegetation loss formulations. This paper proposes a topology-based propagation model that explicitly accounts for orchard layout and the relative positions of radio devices within the plantation structure. Experimental validation was conducted using LoRa technology operating at 868 MHz, and the results were compared with established models from the literature and with the proposed two-dimensional model. The proposed approach achieves a closer fit to measured RSSI data than conventional models, providing a more reliable basis for link budgeting and network planning in structured agricultural environments. INDEX TERMS LoRa, path loss modeling, olive orchards, wireless propagation, agricultural IoT, direction-dependent attenuation

I. INTRODUCTION

LoRa-based modules are becoming a practical option for transmitting data from field activities in commercial orchards. In olive production, for example, low-power nodes can be attached to harvesting bins while a mobile gateway can be installed on a tractor, which moves around the field and collects data as work progresses. For these deployments, to remain usable, path-loss estimates that reflect how radio signals actually propagate within the planted area are needed. This is especially important in order to ensure a complete coverage, e.g., in today’s orchards and structured plantation patterns. The conventional propagation models used for LoRa, described in Section II, are typically calibrated in open fields or generic foliage scenarios. They provide a useful approximation, but describe the environment as horizontally homogeneous and express attenuation as a function of a single distance variable [1]. In a real olive grove, the situation is VOLUME 4, 2016

different; trees are planted on a grid, canopies occupy a sizable portion of the inter-row space, and the tractor follows fixed corridors between them. Under these conditions, path loss depends not only on how far the receiver is from the transmitter but also on how the nodes are oriented with respect to the rows and how much canopy it crosses. Existing studies on radio propagation in plantations and orchards confirm that vegetation and geometry matter, yet most of the results are reported as one-dimensional distance fits or as case-specific maps [2], [3]. In this study, we address this gap by analyzing 868 MHz LoRa modules in an olive orchard in Palermo, Sicily, under a realistic operating scenario with a tractor-mounted gateway and bin-mounted sensor nodes. To complement the field campaign, we use CupCarbon to encode the actual orchard layout and measurement geometry, providing a controlled setting to examine the directional effects on LoRa propagation [4]. Additionally, we propose a 2D direction-dependent path-loss model that operates directly 1

Rowhani Sistani et al.: Direction-Dependent Path Loss Modeling in Olive Orchards

on the orchard grid. Compared with representative isotropic baselines, it reproduces the measured signal distribution over the field more accurately and offers a practical tool for planning LoRa coverage in structured olive groves. The novelty of this work lies in modeling LoRa path loss in a structured olive orchard as a two dimensional direction dependent function defined on the orchard grid, rather than as a function of distance alone. In this way, links with similar Euclidean separation but different orientations across the tree rows can be distinguished. The main contributions of this study are the experimental characterization of LoRa propagation in a real olive orchard, the formulation of a geometry aware path loss model, and its comparison with representative baseline models used in vegetation affected environments. II. RELATED WORKS

In the realm of the Internet of Things (IoT), the communication side, making sure stable connections in agricultural environments, presents unique challenges. Although LoRa technology is designed for low-power, wide-area communication, its performance is highly sensitive to environmental conditions such as dense vegetation, uneven terrain, and low antenna placement. These factors can significantly attenuate signal strength, resulting in packet loss and unreliable data transfer. Therefore, reliable communication in such scenarios is key, as intermittent connectivity can lead to delays in data reporting or missed activation signals. Making certain efficient and stable data transmission requires not only lowpower design, but also context-aware deployment and adaptive tuning of transmission parameters. In the literature, it has been shown that a LoRa node could achieve a range of 5 km with clear LoS, while the range decreased to approximately 1 km when the link was blocked by obstructions [5]. This underscores the critical impact of such obstacles, and limited propagation in the non-line-of-sight (NLoS) scenarios, which leads to severe attenuation of the signal. Field experiments in fruit orchards have shown that dense vegetation can greatly limit communication distance. In one case, LoRa transmissions with a moderate spreading factor (SF) became undetectable above about 40 m under a thick canopy [6]. These studies show that heavy vegetation cover can significantly shorten the effective range of LoRa compared to open field conditions [2]. A. GENERIC PATH LOSS MODELS

For baseline estimation, the free-space path loss (FSPL) model is often used to approximate LoRa signal attenuation under ideal conditions [7], but in real orchard deployments, these assumptions do not hold true [8]: foliage and trunk density produce direction-dependent losses that FSPL cannot resolve. The FSPL is given by: P LFS (dB) = −27.55 + 20 log10 fMHz + 20 log10 dm (1) 2

where fMHz = 868 and dm is the distance given in meters, and -27.55 is the constant term for the frequency given in MHz and the distance given in meters. This simple formula provides a convenient reference to compare with real-world path loss measurements in LoRa deployments [9], [10], [11]. In practical outdoor deployments, free-space propagation alone is not sufficient to describe signal attenuation. When vegetation is present along the propagation path, an additional excess loss is commonly introduced. In such cases, the total path loss can be expressed as the sum of the freespace component and a vegetation-related attenuation term, as defined in Recommendation ITU-R P.833 [12]: P Ltotal (d) = P LFSPL (d) + LITU-R (f, d)

(2)

On the other side, the multi-wall (MW) model [13], [14], [2], [15], extends the log-distance path loss by adding the following excess attenuation of discrete obstacles along the link:

P LMW (d) = P L(d0 ) + 20 log10(d) +

N X

Lwi .

(3)

i=1

where P LMW (d) is the total path loss at distance d, P L(d0 ) is the reference path loss evaluated at the reference distance that is considered d0 is 1 m, and 20 log10 (d) representsPthe distance-dependent free-space attenuation. The N term i=1 Lwi accounts for the cumulative excess loss introduced by N discrete obstacles intersecting the direct propagation path, where Lwi denotes the attenuation associated with the i-th wall or floor. The baseline of the MW model is primarily intended for indoor environments, where walls and floors constitute the dominant sources of additional attenuation beyond free-space propagation. These elements are treated as discrete, homogeneous obstructions, whose individual losses are summed along the transmitter–receiver link. B. PATH LOSS MODELS IN AGRICULTURAL ORCHARDS

Recent studies on wireless propagation in agricultural orchards have shown that tree morphology can substantially affect radio performance [16]. Measurements carried out in date palm plantations with ZigBee transceivers mounted close to the trunks revealed that the received signal strength depends strongly on antenna height and relative orientation between the transmitter and the tree rows. Directional signals along the rows maintained higher strength and lower path loss than those crossing the rows, indicating directional direction-dependent propagation in structured orchards. However, the analysis remained descriptive and relied on a one-dimensional log-distance fit, no directional coefficients or two-dimensional path loss formulation was introduced. This leaves a clear gap, despite the empirical evidence of directionality. We address this gap with a 2D direction-dependent model, described in Section IV-B, VOLUME 4, 2016

Rowhani Sistani et al.: Direction-Dependent Path Loss Modeling in Olive Orchards

which parameterizes attenuation separately along columns and rows. The ITU-R vegetation model provides an experimental baseline for signal attenuation through foliage, widely used to assess propagation in forested or semi-urban areas [12]. It assumes homogeneous vegetation and moderate canopy density, which simplifies modeling at the cost of realism. The additional path loss due to the trees is given by the following formula: 0.3 LITU-R (dB) = 0.2 · fMHz · d0.6 m

(4)

This simplified form, often referred to as an ITU-R–based vegetation model, follows the general structure of ITU-R P.833 but uses coefficients adapted from field data reported in recent studies [8], [13], [17]. We adopt this term because its reported validity range (200 MHz–95 GHz, d < 400 m) covers our 868 MHz orchard links over tens of meters and provides a clean additive “excess-loss” reference for foliage environments [8]. However, in structured orchards, tree spacing and trunk periodicity produce direction-dependent propagation paths that the ITU-R formulation cannot fully capture. A specialization of the multi-wall model in [2], the plantation multi-wall (PMW) considers row-structured trees as discrete obstacles causing additional path loss added to a oneslope baseline: P LPMW (dm ) = P L0 + 10 n log10 dm + Lcanopy Ncan (dm ) (5) where Ncan (dm ) is the counted canopy crossings along the transmitter–receiver path and Lcanopy is the empirical per-canopy loss. The baseline terms of this model are obtained from short-range calibration consistent with the oneslope derivation model: P L0 = used by the Pmulti-wall r (d0 )−Pr (d) 20 log10 fMHz − 27.55, n = 10 log10 (d/d0 ) , and d0 = 1 m. This model considers the number of intercepted canopies but does not relate it with the orchard layout. We adopt the PMW model as a baseline and compare it with our proposed model (PM), described in Section IV-B. Recent work has addressed LoRa propagation modeling specifically for structured orchard environments. In the FLog model [18], a three-dimensional approach was proposed for the shadowing effect of tree canopies and the ground by combining the intrinsic path-loss exponents of different air, foliage, and soil within the First Fresnel Zone (FFZ). By numerically sampling the FFZ, the model estimates the fraction of each medium and integrates them into a weighted Log-Normal Shadowing formulation, validated in almond and walnut orchards. However, while FLog successfully incorporates volumetric shadowing, it remains omnidirectional in nature, the model assumes uniform propagation in all horizontal directions. A specific exponent is defined for the classical log–normal model: dm +Xσ , (6) P LFLog (dm ) = P L(d0 )+10 nFLog log10 d0 VOLUME 4, 2016

where P L(d0 ) is the reference loss at distance d0 and Xσ a Gaussian random variable that accounts for shadowing, nFLog = Popen α + Pfoliage β + Pground γ, where Popen , Pfoliage , and Pground denote the fractions of the FFZ occupied by free space, canopy, and ground, respectively, and α, β, γ are their intrinsic path–loss exponents obtained via least–squares fitting. Additionally, recent wide-band field measurements in rowstructured crops have quantified direction-dependent loss. In a sugarcane field at 2.45 GHz excess attenuation increased with the number of rows, intersecting the line of sight; model selection based on the Akaike Information Criterion and its small-sample correction (AIC/AICc) favored a ridgecount vegetation obstruction formulation [19]. The presented evidence supports direction-dependent propagation (alongrow versus across-row). However, because the model in [19] is tied to the ridge counts and local conditions, its transferability and map-scale planning utility are limited. This context motivates our explicit two-dimensional, directionaware path-loss surface with separate parameters for alongrow and inter-row directions. Reflects the fact that attenuation in olive orchards differs significantly from that reported in a tall, densely planted sugarcane field. Directional behavior in small-cell networks has been captured by dual-directional path-loss formulations that pair distance with angular dependence [20]. In these models, the line-of-sight probability varies with direction due to direction-dependent blockage from randomly oriented obstacles, so coverage and rate depend on propagation direction and range. Although developed for urban geometries, the same principle motivates direction-aware modeling in structured orchards, where row alignment and foliage density induce comparable anisotropy. In summary, the evidence points to a clear need for propagation models that reflect the geometry and materials of working farms, row structure, foliage density, and terrain, rather than relying on distance-only formulas. We follow this path and propose a new 2-D direction-dependent model for olive orchards, described in Section IV-B. A concise overview of previous work and the remaining gaps in directionality and 2-D modeling is reported in Table 1. None of the previous models takes care about the regular structure of the orchard considering the distance among rows, columns, and the number of rows and columns between the transmitter and the receiver. Taken together, the existing literature provides useful references for distance based attenuation, canopy excess loss, and volumetric or direction dependent propagation effects. However, these approaches do not simultaneously capture the regular orchard grid, the row and column dependent propagation geometry, and the resulting spatial RSSI distribution over the plantation. This is the point addressed by the proposed model in the present work.

Rowhani Sistani et al.: Direction-Dependent Path Loss Modeling in Olive Orchards

TABLE 1: Propagation models for orchards and gaps. Key

Year

[14]

2001

[2]

2022

[18]

2023

[3]

2024

[6]

2024

This study

2025

Directionality Notes / Model Focus Multi–Wall–and–Floor (indoor); obstacle ∼ penalties, not foliage–row anisotropy. Foliage–loss Multi–Wall model (palm planta∼ tion, 433 MHz); row structure treated uniformly. FLog (3D Fresnel–based lognormal) orchard ∼ model; horizontally isotropic approximation. Empirical LoRa RSSI characterization in olive ✓ orchards; introduces Z(x) excess–loss term. Critical review of LoRa models; notes isotropy × limits; no direction-dependent proposal. 2D direction-dependent model on ∆x and ✓ ∆y; distinguishes across cols/rows with row aware fitting.

III. THE OLIVE ORCHARD CONTEXT

We assume grid-shaped olive orchards and a Tx/Rx channel between a mobile gateway mounted on the tractor and a set of compact sensor units integrated into the harvesting bins. The design follows the practical workflow of olive collection, where the tractor naturally moves along the interrow corridors and the bins remain distributed around the trees during picking activities. This arrangement creates effective communication, with the mobile gateway acting as the receiver and the bin-mounted devices serving as low-power transmitters. Each bin unit contains a small LoRa transceiver. During normal field operation, the devices are expected to report basic indicators such as location or temperature and other data of harvesting procedures; only the radio link itself is relevant for the present study. The tractor-mounted gateway, positioned approximately at transmitter height, provides a stable reception point in the experimental measurements. The interaction between these two components, the mobile gateway and the bin nodes, defines the operational setting in which our propagation data were collected. This system configuration mirrors a realistic deployment scenario while remaining simple enough to isolate the effects of orchard geometry and canopy structure on the received signal. IV. METHODOLOGY

This section presents the methodological framework adopted in this study. It begins by presenting the propagation behavior and anisotropy observed in olive orchards, followed by a detailed description of the geometric environment in the experimental area. Finally, the simulation design performed in CupCarbon [24], is introduced as a preliminary stage prior to real test measurements. A. PROPAGATION CONTEXT AND ANISOTROPY IN OLIVE ORCHARDS

Orchards, including olive groves, often follow repetitive planting patterns with pruned canopies and fixed row–tree spacing. Depending on the cultivation system, trees can be widely spaced as in traditional groves or tightly planted in rows, as in modern high-density and super-intensive orchards. These variations directly influence the propagation 4

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FIGURE 1: Propagation conditions in two different positions at the same distance from the transmitter when using an omnidirectional propagation model with d as single parameter. paths and introduce a strong directional dependence in signal attenuation. In conventional isotropic models such as FSPL, ITU-R P.833, or multi-wall, the path loss is assumed to depend only on the Euclidean distance between the transmitter and receiver. However, in a row-based plantation, the radio wave interacts differently when it travels along the rows versus across them, due to the distinct number of tree intersections. Fig. 1 shows this effect: although the receivers P1 and P2 are located at the same distance d from the transmitter, the signal reaching P1 experiences higher attenuation because it crosses several trees, while P2 is located on an unobstructed middle corridor. As a result, the loss function P L(d) becomes direction-dependent, revealing the directiondependent nature of radio propagation in orchards. To generalize this behavior, Table. 2 summarizes common olive cultivation systems by typical density, row × tree spacing, and canopy arrangement. More denser layouts typical of high-density (HD) and super-intensive (SHD) hedgerow systems form foliage walls that increase cross-row attenuation and reduce the isotropy assumed in classical propagation models. As the inter-row distance decreases and canopy continuity increases, signal paths are more likely to cross foliage layers rather than travel through open corridors. In traditional construction layouts, trees are separated by open gaps that allow relatively direct paths between transmitter and receiver. In contrast, dense hedgerow systems create continuous foliage corridors where signals are repeatedly attenuated by tree layers. As a result, even when two receiver points are at similar or shorter distances, the one located across multiple rows may experience a stronger loss than a farther point aligned along the rows. Figure 2 shows this behavior: on the left, the traditional configuration shows wider spacing and fewer obstructions, while on the right, the high-density pattern forms compact rows that enhance cross-row attenuation. This contrast emphasizes that propagation in structured orchards cannot be described by distance alone, but depends on the directional components of the field layout. VOLUME 4, 2016

Rowhani Sistani et al.: Direction-Dependent Path Loss Modeling in Olive Orchards

TABLE 2: Olive orchard planting systems depending on spacing, density, and canopy form across traditional, high-density, and super-high-density layouts [21]–[23]. Row × Tree Spacing (m) up to 10 × 10 5.7 × 2.25 to 3.55 × 1.25 4 × 1.5

System Type Traditional Intensive / High-Density (HD) Super-High-Density (SHD)

Density (trees ha−1 ) ≈ 100–150 780–2254 ≈ 1667

Canopy / Row Structure Isolated crowns; open inter-row spacing Semi-continuous rows; controlled canopy volume Hedgerow-type continuous foliage walls

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2!x

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(a) Traditional plantation density

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(b) High-density plantation

FIGURE 2: Traditional plantation density (a); high-density plantation pattern (b). Propagation conditions in two different receiver positions P1 , P2 with our proposed model; d1 < d2 but A(d1 ) > A(d2 ) because of canopy interception depends on row/cols and not on the Euclidean distance. Unlike literature models, our model can caught this inconsistency because we use two parameters for the attenuation function.

B. THE PROPOSED RADIO PROPAGATION MODEL

In this subsection, we formalize the proposed propagation model as follows: P Ln,m = P L0 + 10n log10 d(n,m) (∆x, ∆y) + (7) L(n,m) can (∆x, ∆y), where P L0 is the reference path loss at 1 m. Additionally, m and n denote the discrete transmitterreceiver displacement in the column and row directions of the orchard grid, respectively. Accordingly, the geometric link length is expressed as d(n,m) (∆x, ∆y). The canopy term (n,m) Lcan (∆x, ∆y) represents the excess attenuation associated with the canopy intersections encountered by that specific Tx–Rx path and depends on the corresponding number of intercepted canopies together with the representative canopy radius rc measured in the field. This makes explicit that two links with the same Euclidean distance may still yield different path loss values when their orientations within the orchard grid are different. The proposed model describes the path loss as a twodimensional function of the transmitter–receiver separation, expressed in meters along the horizontal and vertical axes of the orchard grid. Rather than relying on a single scalar distance, the path loss depends on the orchard layout (∆x, ∆y) and the displacement of the TX and RX (relative positions) (n, m) in terms of the number of rows and columns of trees between them. These values capture how far the receiver moves in each direction of the planting layout as a realistic scenario in an olive orchard. This 2-D formulation allows links with the same overall length to be distinguished by their VOLUME 4, 2016

orientation within the grid, which is essential to accurately represent the propagation behavior observed in the orchard. In addition to the distance term, the model includes a vegetation component based on the tree canopies that lie along the transmitter and receiver. For each link, we determine how many canopies are intersected by the TX–RX line and we use the mean radius of the canopies measured in the field. This captures the effective amount of foliage that blocks the signal: links that cross multiple rows typically intersect a larger number of canopies and therefore experience higher excess attenuation, whereas along–row links intersect fewer crowns and exhibit lower loss. This formulation provides a compact description of the large-scale behavior observed in the orchard. The model captures the differences in directional attenuation that cannot be explained by distance alone. The resulting expression remains simple enough for practical use while preserving the key geometric effects of the plantation layout. C. GEOMETRY AND DESCRIPTION OF THE STUDY AREA

In the experimental setup, we focus on an olive field located in Palermo, Sicily, Italy (cf. Fig. 3). The field is located at coordinates 38,10664° N, 13,34976° E, with a typical Mediterranean farming area with a square pattern of olive trees, some uneven terrain, and moderate vegetation density. The dimensions of the plantation are 43 meters by 38 meters. These dimensions were obtained directly from satellite imagery and mapping tools to accurately represent the actual layout of the field used in this study. This field corresponds 5

Rowhani Sistani et al.: Direction-Dependent Path Loss Modeling in Olive Orchards

to a traditional plantation layout, characterized by moderate spacing and discrete trees, consistent with the parameters of conventional Mediterranean orchards [21].

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FIGURE 4: Simulated LoRa coverage within the CupCarbon environment reflecting expected link quality for the selected communication parameters [27]. 40

Distance (m)

25 Row Index

The field is largely flat, with only minor variations in terrain elevation, ensuring near line-of-sight visibility between adjacent rows. The trees form a uniform geometric grid roughly aligned north-south. This configuration allows the site to serve as a controlled environment for analyzing propagation anisotropy owing to canopy interception rather than topographic effects. Before conducting real-world RSSI measurements, we reproduced the same ∆x−∆y orchard layout in simulation to (i) define and emulate the data-collection path, (ii) plan transmitter/receiver placement and examine coverage under the intended LoRa configuration (SF/Tx power/antenna height), and (iii) screen for potential weak-signal zones while checking the consistency of theoretical models by using simulator written in Python, available on GitHub [26].

20 4

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3 4 Column Index (Tree Lines)

D. CUPCARBON SIMULATOR

FIGURE 5: Simulated receiver trajectory within the orchard grid. The zigzag path illustrates the sequential movement between tree rows used to emulate data collection.

To analyze the expected propagation behavior before field deployment, the entire orchard layout was recreated in the CupCarbon simulator [27], using the same dimensions and coordinates described in the previous section. The nodes representing the transmitter and receiver were positioned according to the ∆x − ∆y grid of the real plantation, preserving both the inter-row and intra-row spacing observed on site. The simulation parameters were configured to replicate the LoRa setup later adopted in field measurements, including spreading factor, transmission power, and transmission packet timing. The pink-shaded overlay shown in Fig. 4 shows the simulated coverage strength for the configured TX/RX settings (SF, BW, transmit power) over the orchard grid. Rather than

a simple range boundary, it provides a qualitative map of the expected link quality across cells, which we used to place sampling points and prioritize areas for empirical verification in the field. This visualization also helped identify potential low-coverage regions within the plot, guiding the selection of data-collection points for the experimental phase. The movement of the receiver within the simulation followed a sequential pattern throughout the grid as shown in Fig. 5. The trajectory was designed to emulate a systematic sweep between the tree rows, ensuring that each node of the field was sampled with comparable distance steps. This representation served as the reference path for the real mea-

VOLUME 4, 2016

Rowhani Sistani et al.: Direction-Dependent Path Loss Modeling in Olive Orchards

surement test, enabling a consistent comparison between the simulated and real collected RSSI values.

separate attenuation into leaf on and leaf off cases. Instead, vegetation loss is represented through the measured orchard geometry, namely the canopy intersections along the Tx–Rx path and the representative mean canopy radius measured in the field. The proposed model is therefore calibrated for the canopy condition observed during the reported campaign [21]. In the experiment, the entire orchard was systematically surveyed along mid-row and under-canopy trajectories. The orchard exhibits near-uniform spacing along both axes; average inter-row and intra-row spacings are shown in Table 3. TABLE 3: Site parameters of the experimental olive orchard Location Inter-row spacing srow (m) Intra-row spacing scol (m) Representative canopy radius rc (m)

FIGURE 6: Predicted RSSI along the simulated gateway trajectory for one representative bin. The profile is obtained by combining the CupCarbon-defined path with the Python implementation of the proposed model. In this work, CupCarbon is used as a pre-deployment planning aid for layout reproduction, waypoint definition, and qualitative coverage inspection. To further illustrate the proposed model, the gateway trajectory defined in CupCarbon was combined with the Python implementation of the proposed path loss model. An example of the predicted RSSI profile for one representative bin is shown in Fig. 6. V. EXPERIMENTAL SETUP

This section introduces the field methodology adopted to characterize LoRa propagation in an olive field in Palermo (Sicily, Italy). Two measurement campaigns were conducted: an experiment with movements in the middle of consecutive rows of olive trees. This experiment establishes a controlled baseline within the orchard geometry, followed by an extended campaign incorporating two antenna heights and measurements both inter-row and under-canopy. Each node was a Heltec CubeCell HTCC-AB01 (SX1262-class) configured for EU-868 operation, as reported in Fig. 7. A. MEASUREMENT CONFIGURATION AND PROCEDURE

All field measurements were conducted in September and October 2025 under a single canopy condition of a regulargrid olive orchard. Since olive trees, Olea europaea L., are an evergreen species. The timing of the campaign is also relevant to the intended application, since it leads into and partly overlaps the main olive-harvesting period in Italy, which typically extends from mid-October through November. and the orchard did not exhibit a true leaf off state comparable to deciduous orchards. Therefore, the present study does not VOLUME 4, 2016

Palermo, Sicily, Italy 7.12 7.12 4.16

In the present study, vegetation attenuation is represented through the measured orchard geometry, namely the canopy intersections along the Tx–Rx path and the representative mean canopy radius measured in the field. However, the proposed formulation does not introduce a separate local term for links propagating predominantly inside dense foliage. Accordingly, the model is intended to describe large scale direction dependent attenuation in the structured olive orchard, while the main model comparison is performed for the reference mid corridor configuration. In our setup, both the transmitter and the receiver used identical omnidirectional monopole antennas of approximately 48 mm length, operating at 868 MHz. Such elements are considerably shorter than a half-wave dipole (λ/2 ≈ 172 mm) and therefore exhibit a negative effective gain (approximately −9 dBi per antenna). Because both ends of the link use the same antenna type, this term acts as a fixed offset in the link budget and does not affect the distancedependent behavior of the received power. Consequently, the antenna gain is omitted in our formulation, and all fixed front-end effects are implicitly absorbed into an effective transmit power defined over the horizontal 2D plane. Within the present measurement setup, this approximation acts as a constant offset and therefore does not affect the comparative evaluation of the considered propagation models. Different antenna or front end configurations would require recalibration of the effective transmit power term. RSSI values were recorded through serial monitoring. At each receiver position, 30 samples were collected and averaged to reduce instantaneous fluctuations caused by smallscale fading. The resulting value was then rounded to represent the effective signal level at that point. The configuration summarized in Table 4 was used for all measurements to ensure stability between measurement campaigns. The TX/RX nodes were programmed using a custom Arduino script [26], which automated packet transmission and reception under the same LoRa parameters. The script defined a solid transmission interval, recording each received packet timestamp and RSSI value. This approach guaranteed 7

Rowhani Sistani et al.: Direction-Dependent Path Loss Modeling in Olive Orchards

FIGURE 8: Line-of-sight propagation corridor in the experimental olive orchard, with TX and RX antennas at 1.2 m above ground.

identical behavior of the transmitter and receiver during all field tests. TABLE 4: LoRa radio and sampling setup Heltec CubeCell HTCC-AB01 (SX1262) 125 kHz 4/5 SF10 21 dBm Omnidirectional monopole ≥ 30 (constant payload, constant interval) waypoint ID, timestamp, per-packet RSSI

In all tests, the transmitter was mounted fixed at 1.2 m above ground. Measurements were taken along two paths: the mid-corridor between rows and under-canopy near the trunk. Figure 8 illustrates only the reference mid-corridor configuration with both antennas at 1.2 m. After comparing the datasets across height and path, we selected this configuration as the baseline case for the main model comparison reported in this work. This choice reflects RSSI ranges that were closely aligned with those observed in the other configurations with the intended deployment operational scenario. Additional experimental results and datasets are available in the GitHub repository associated with this study [26]. VI. NUMERICAL RESULTS

To understand how different propagation models reproduce the signal distribution measured in the olive orchard, we compare their predictions directly against the experimental RSSI data sets. Three representative formulations are considered in detail: the ITU–R vegetation model, a multi-wall obstruction model, and our proposed 2-D direction-dependent model (cf. Section II). In all cases, the models are evaluated on the same transmitter–receiver geometry used during the field 8

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FIGURE 9: Olive orchard measured RSSI heatmap

experimental tests, so that their outputs can be contrasted point by point with the measurements. Each propagation model is driven by the same set of transmitter–receiver distances used during the measurement experimental test, producing its own RSSI heatmap over the grid. This makes it possible to inspect how each model represents the signal distribution across and along the tree rows. Then, the point-wise difference between measured and modeled RSSI is computed to obtain an error heatmap, which highlights where and to what extent each model departs from the observed behavior, as in Fig. 10. To complement visual inspection, the mean square error (MSE) and the root mean square error (RMSE) were calculated for each model over the full set of measurement points. These indicators condense the pointwise discrepancies into single figures and provide a compact basis for comparison. The results are summarized in Table 5. VOLUME 4, 2016

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Rowhani Sistani et al.: Direction-Dependent Path Loss Modeling in Olive Orchards

(f)

FIGURE 10: Comparison of modeled and measured RSSI for the mid-corridor scenario at 1.2 m height: the ITU-R (a), multiwall (c), the proposed 2-D direction-dependent model (e) and their respective error heatmaps (b), (d), and (f).

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Rowhani Sistani et al.: Direction-Dependent Path Loss Modeling in Olive Orchards

TABLE 5: MSE and RMSE of our model with respect to experimental measurements. Model ITU–R vegetation Multi-wall Proposed 2-D direction-dependent

MSE (dB2 ) 769.76 373.62 303.15

RMSE (dB) 27.74 19.33 17.41

Table 5 and Fig 10 provide a direct comparison between the proposed formulation and the representative baseline models evaluated in this study under the same mid-corridor measurement geometry. In addition to the lower overall error reported in Table 5, Fig. 9 shows that the proposed directiondependent model follows the measured RSSI distribution over the orchard grid more closely, with smaller and less structured residual regions than the ITU–R vegetation and plantation multi-wall models. These results indicate that explicitly accounting for orchard geometry improves the agreement between the modeled and measured signal distribution in the studied olive orchard. The ITU–R vegetation model exhibits the largest error, with an RMSE close to 28 dB, which is consistent with its isotropic structure and its limited ability to reproduce the strong cross-row attenuation seen in Fig. 9. Introducing canopy-related losses through discrete obstacle counts already improves the comparison: the multi-wall model reduces the RMSE to about 19.3 dB, showing that an explicit treatment of foliage intersections is beneficial in this setting. The proposed 2-D direction-dependent formulation not only reduces RMSE to 17.4 dB, but also produces a visibly more homogeneous error map: large patches of over- and underestimation along the rows are replaced by smaller, more diffuse residuals across the grid. As a result, it provides a closer description of the directional attenuation patterns that characterize the olive orchard, which is particularly relevant for network planning and reliability analysis and motivates the discussion in the final section. VII. CONCLUSION

This study examined radio propagation in a real olive orchard and showed that structured fields play a decisive role in shaping LoRa behaviors. Measurements consider a realistic scenario that a tractor-mounted gateway and integrated modules on bins node revealed marked differences between propagation along and across the rows, confirming that distance alone cannot capture the behavior of sub-GHz links in such environments. By introducing a two-dimensional directiondependent path-loss model, we demonstrated that separating the attenuation into components ∆x (along–row) and ∆y (cross-row) improves the precision of the model. Compared with classical isotropic baselines, ITU-R vegetation attenuation, and multi-wall approaches, the proposed model yields a more homogeneous error distribution and better reflects the physical structure of the orchard. These results underscore the need for direction-sensitive propagation models adapted to agricultural plantation patterns. As farms increasingly rely on distributed sensing and automated workflows, capturing 10

these geometric effects becomes essential for planning reliable wireless infrastructures and optimizing node placement, coverage, and energy consumption. VIII. FUTURE WORK

Future research can extend the present study in several directions. First, incorporating seasonal variations such as canopy growth, pruning stages, and moisture levels, especially in the southern Italian environmental conditions, would allow the model to reflect temporal changes in different situations. Another direction is the combination of the proposed direction-dependent formulation with machine-learning techniques to refine parameter estimation while preserving interpretability. Finally, validating the model across different orchards and also different planting systems (HD/SHD hedgerows and irregular traditional layouts) will help determine its generality and support the development of deployment guidelines for large-scale agricultural IoT systems. IX. ACKNOWLEDGMENT

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MOHAMMAD ROWHANI SISTANI Mohammad Rowhani Sistani received the M.Sc. degree in International Business Management. He is currently a Ph.D. candidate at the University of Camerino, Italy, with doctoral research activities carried out at the University of Palermo, Italy. His research interests focus on blockchain-based traceability systems, agri-food supply chains, smart agriculture, and the integration of IoT and wireless communication technologies in agricultural environments. He has experience in agri-food export and applied research on saffron traceability systems. His Ph.D. studies are financially supported by SEEDS s.r.l.

KATARZYNA KOSEK-SZOTT Katarzyna KosekSzott received the Ph.D. degree in telecommunications in 2011 and the Habilitation degree in 2016. She is currently a Full Professor with the Institute of Telecommunications, AGH University of Science and Technology, Krakow, Poland. Her research focuses on wireless networking, including wireless LANs, quality of service provisioning, novel amendments to the IEEE 802.11 standard, 5G/6G networks, and coexistence of radio technologies. She has coauthored more than 70 research papers and serves as a reviewer for international journals and conferences. She has been involved in several European research projects, including DAIDALOS II, CONTENT, CARMEN, FLAVIA, PROACTIVE, and RESCUE, as well as projects funded by the Polish government.

PIERLUIGI GALLO Pierluigi Gallo received the Ph.D. degree in information engineering. He is currently an Associate Professor with the Department of Engineering, University of Palermo, Italy, and a researcher with CNIT. His research interests include wireless networks, indoor localization, communication security, blockchain technologies and architectures, blockchain-based systems for secure data management and traceability, and Internet of Things applications. He has been involved in several national and international research projects related to wireless and IoT-enabled systems. He is a Member of the IEEE. 11

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