# MDE-AgriVLN: Agricultural Vision-and-Language Navigation with Monocular Depth Estimation

Source: [MDE-AgriVLN: Agricultural Vision-and-Language Navigation with Monocular Depth Estimation](<https://www.sota2.com/research/paper/693115d1d6b0dd3342933d50>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-06T05:00:04.578Z

Authors: Xiaobei Zhao, Xingqi Lyu, Xin Chen, Xiang Li

Published: 2025-12-03T16:52:07.000Z

## Abstract

Agricultural robots are serving as powerful assistants across a wide range of agricultural tasks, nevertheless, still heavily relying on manual operations or railway systems for movement. The AgriVLN method and the A2A benchmark pioneeringly extended Vision-and-Language Navigation (VLN) to the agricultural domain, enabling a robot to navigate to a target position following a natural language instruction. Unlike human binocular vision, most agricultural robots are only given a single camera for monocular vision, which results in limited spatial perception. To bridge this gap, we present the method of Agricultural Vision-and-Language Navigation with Monocular Depth Estimation (MDE-AgriVLN), in which we propose the MDE module generating depth features from RGB images, to assist the decision-maker on multimodal reasoning. When evaluated on the A2A benchmark, our MDE-AgriVLN method successfully increases Success Rate from 0.23 to 0.32 and decreases Navigation Error from 4.43m to 4.08m, demonstrating the state-of-the-art performance in the agricultural VLN domain. Code: https://github.com/AlexTraveling/MDE-AgriVLN.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Agricultural Vision-and-Language Navigation on A2A high-complexity subtask \>= 3](<https://www.sota2.com/research/sota/agricultural-vision-and-language-navigation-on-a2a-high-complexity-subtask-3.md>): Agricultural Vision-and-Language Navigation; A2A high-complexity subtask \>= 3; SR: 23.

- [Agricultural Vision-and-Language Navigation on A2A](<https://www.sota2.com/research/sota/agricultural-vision-and-language-navigation-on-a2a.md>): Agricultural Vision-and-Language Navigation; A2A; SR: 32.

- [Agricultural Vision-and-Language Navigation on A2A low-complexity subtask 2](<https://www.sota2.com/research/sota/agricultural-vision-and-language-navigation-on-a2a-low-complexity-subtask-2.md>): Agricultural Vision-and-Language Navigation; A2A low-complexity subtask 2; Success Rate: 44.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2512.03958>)

[PDF](<https://arxiv.org/pdf/2512.03958.pdf>)