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

Katarzyna Kosek-Szott; Mohammad Rowhani Sistani; Pierluigi Gallo

arxiv: 2604.19427 · v1 · submitted 2026-04-21 · 💻 cs.NI · cs.SY· eess.SY

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

Mohammad Rowhani Sistani , Katarzyna Kosek-Szott , Pierluigi Gallo This is my paper

Pith reviewed 2026-05-10 02:07 UTC · model grok-4.3

classification 💻 cs.NI cs.SYeess.SY

keywords path loss modelingolive orchardsprecision agricultureLoRadirection-dependent propagationwireless sensor networksRSSI

0 comments

The pith

A topology-based model that accounts for olive orchard row directions fits measured RSSI data more closely than standard free-space or vegetation path loss models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Wireless links in row-structured olive orchards show markedly different attenuation along the rows versus across them, a variation that conventional models like free space path loss or ITU-R vegetation loss fail to capture. The paper develops a two-dimensional propagation model built directly on the plantation layout and the relative positions of the radio devices. Validation uses LoRa transmissions at 868 MHz, with comparisons showing the new model reduces discrepancies against collected received signal strength data. This supplies a more accurate foundation for calculating link budgets and designing wireless networks inside structured agricultural settings.

Core claim

Signal attenuation in olive groves differs markedly between along-row and cross-row propagation directions. The proposed topology-based model explicitly incorporates orchard layout and device positions within that structure, yielding a closer fit to measured RSSI values than the Free Space Path Loss model or ITU-R vegetation loss formulations.

What carries the argument

The two-dimensional topology-based propagation model that treats orchard row geometry and relative device positions as the primary determinants of directional path loss.

If this is right

Link budgets for wireless sensor networks in row plantations can be calculated with reduced over- or under-estimation of coverage.
Network planning tools can incorporate directional effects when placing nodes along versus across planting rows.
LoRa-based precision agriculture deployments gain a more reliable basis for predicting connectivity in structured groves.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same directional dependence is likely to appear in other regularly planted row crops such as vineyards.
The model could be tested for sensitivity to seasonal foliage changes by repeating measurements at different times of year.
Antenna height or orientation choices might be optimized once the dominant propagation directions are known from the layout.

Load-bearing premise

The regular row geometry is the dominant source of signal variability and other influences such as tree height or foliage changes can be averaged out or treated as secondary.

What would settle it

A set of RSSI measurements collected in an olive orchard with highly irregular row spacing or tree density that shows the proposed model no longer improves the fit over conventional distance-based models.

Figures

Figures reproduced from arXiv: 2604.19427 by Katarzyna Kosek-Szott, Mohammad Rowhani Sistani, Pierluigi Gallo.

**Figure 2.** Figure 2: FIGURE 2: Traditional plantation density (a); high-density plantation pattern (b). Propagation conditions in two different [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 4.** Figure 4: FIGURE 4: Simulated LoRa coverage within the CupCarbon [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

**Figure 5.** Figure 5: FIGURE 5: Simulated receiver trajectory within the orchard [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗

**Figure 6.** Figure 6: FIGURE 6: Predicted RSSI along the simulated gateway [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗

**Figure 7.** Figure 7: FIGURE 7: Heltec CubeCell HTCC-AB01 LoRa modules [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗

**Figure 9.** Figure 9: FIGURE 9: Olive orchard measured RSSI heatmap [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗

**Figure 10.** Figure 10: FIGURE 10: Comparison of modeled and measured RSSI for the mid-corridor scenario at 1.2 m height: the ITU-R (a), multi [PITH_FULL_IMAGE:figures/full_fig_p009_10.png] view at source ↗

read the original 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 row-structured 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper gives a direction-dependent path loss model for olive orchard rows that fits their LoRa RSSI data better than FSPL or ITU-R models, but the validation details are missing and the scope is narrow.

read the letter

The main takeaway is that this work adapts propagation modeling to the regular row geometry of olive orchards, treating along-row and cross-row directions separately instead of using a single omnidirectional formula. They back it with 868 MHz LoRa measurements and report a closer match to the observed RSSI than the usual distance-based or vegetation-loss models. That is a concrete, applied step for link budgeting in row-crop IoT setups where the tree layout creates repeatable directional effects. The approach is new in its specific tailoring to olive-orchard topology rather than a generic vegetation correction. It does the basic job of identifying the geometry as the dominant source of variability and showing that a 2D adjustment improves the fit on their data. For readers who need quick practical guidance on placing nodes in similar plantations, this supplies a usable starting point without requiring full 3D ray-tracing. The soft spots are clear and proportionate to the evidence. The abstract supplies no RMSE values, no measurement counts, no statistical tests, and no derivation steps for the direction-specific coefficients. Without those, it is impossible to judge how much of the reported improvement is real versus post-hoc fitting. The stress-test note is on target: everything comes from one orchard, so unmodeled factors such as canopy density changes or tree-height variation could erase the advantage elsewhere. If the full text contains the equations and raw data tables, that would raise the bar, but the current presentation leaves the central claim plausible yet unverifiable. This paper is for engineers and researchers working on wireless sensor networks in structured agriculture. A reader planning LoRa deployments inside row crops would get immediate value from the directional insight and could adapt the idea even if the exact coefficients need re-tuning. It is not reshaping general propagation theory, but it is a solid niche contribution. It deserves a serious referee because it rests on real measurements and a clear practical problem rather than pure simulation. Referees will ask for the missing error metrics, cross-validation, and sensitivity checks, but the work is coherent enough to warrant that review. I would send it forward with those expectations.

Referee Report

3 major / 1 minor

Summary. The manuscript proposes a topology-based 2D propagation model for LoRa links at 868 MHz in row-structured olive orchards that explicitly differentiates attenuation along-row versus cross-row directions due to plantation geometry, and reports that this model yields a closer fit to measured RSSI data than the FSPL or ITU-R vegetation models, thereby improving link-budget reliability for precision-agriculture networks.

Significance. If the reported improvement holds under broader conditions, the work would provide a practical, geometry-aware alternative to isotropic models for wireless IoT planning in structured agricultural environments, potentially reducing over-provisioning or outage risks in row-crop deployments.

major comments (3)

[Abstract] Abstract: the central claim of a 'closer fit' to RSSI data is stated without any quantitative error metrics (RMSE, MAE, R²), measurement counts, or statistical significance tests comparing the proposed model against FSPL and ITU-R baselines; this absence makes the improvement unverifiable from the supplied text.
[Model] Model section: no explicit equations are supplied for the direction-specific attenuation coefficients or their dependence on row spacing and device placement, preventing assessment of whether the 2D model is derived from first principles or fitted post-hoc to the same RSSI traces later used for validation.
[Experimental validation] Experimental validation: results are confined to a single orchard without cross-site replication, sensitivity sweeps over tree height/density, or seasonal foliage variation; if these factors introduce variance comparable to row geometry, the claimed generality for 'structured agricultural environments' does not follow.

minor comments (1)

[Experimental setup] Add a table or figure caption that reports the exact number of RSSI samples, transmitter/receiver heights, and orchard dimensions to support reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments on our manuscript. We have addressed each major point below, revising the paper where the concerns can be directly resolved through added clarity or discussion. Our responses focus on substance and indicate the specific changes made.

read point-by-point responses

Referee: [Abstract] Abstract: the central claim of a 'closer fit' to RSSI data is stated without any quantitative error metrics (RMSE, MAE, R²), measurement counts, or statistical significance tests comparing the proposed model against FSPL and ITU-R baselines; this absence makes the improvement unverifiable from the supplied text.

Authors: We agree that the abstract should include quantitative metrics to allow immediate verification of the improvement. The body of the manuscript already contains the full comparison with RMSE, MAE, R², the total number of RSSI measurements collected, and the results of statistical tests against the baselines. We have revised the abstract to incorporate these values and the measurement count so that the central claim is supported directly in the abstract. revision: yes
Referee: [Model] Model section: no explicit equations are supplied for the direction-specific attenuation coefficients or their dependence on row spacing and device placement, preventing assessment of whether the 2D model is derived from first principles or fitted post-hoc to the same RSSI traces later used for validation.

Authors: We thank the referee for highlighting this omission. The direction-dependent coefficients are obtained from the geometric layout (row spacing, orientation, and relative device positions) rather than being fitted to the validation RSSI data. We have added the explicit equations for the along-row and cross-row attenuation terms, together with their dependence on row spacing and placement, in the revised Model section. This makes the first-principles derivation from orchard topology transparent. revision: yes
Referee: [Experimental validation] Experimental validation: results are confined to a single orchard without cross-site replication, sensitivity sweeps over tree height/density, or seasonal foliage variation; if these factors introduce variance comparable to row geometry, the claimed generality for 'structured agricultural environments' does not follow.

Authors: We acknowledge that the measurements were performed in one representative olive orchard and that additional cross-site replication, sensitivity analysis over tree height and density, and seasonal data would further strengthen generality claims. The model parameters are expressed directly in terms of row spacing and geometry, which are the dominant variables in row-structured plantations; we have added a dedicated limitations paragraph in the revised Discussion section that explicitly discusses the potential influence of tree height, density, and foliage changes and states that multi-site validation is planned for future work. The current results therefore demonstrate the value of the topology-aware approach within the tested class of environments while qualifying the scope of the claims. revision: partial

Circularity Check

0 steps flagged

No circularity in derivation chain; model proposed from topology and validated empirically

full rationale

The paper derives a direction-dependent path loss model from orchard row geometry and relative device positions, then reports an empirical comparison of fit quality against FSPL and ITU-R models on LoRa RSSI measurements. No equations, self-citations, or parameter-fitting steps are shown that reduce the claimed improvement to a tautology or to the same data used for fitting. The central claim remains an independent empirical observation rather than a self-referential construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no equations or parameter lists are supplied, so the ledger remains empty. The model is described as topology-based but its mathematical form, any fitted coefficients, and background assumptions are not stated.

pith-pipeline@v0.9.0 · 5455 in / 1107 out tokens · 55919 ms · 2026-05-10T02:07:38.493115+00:00 · methodology

discussion (0)

Reference graph

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