# Towards agricultural autonomy: crop row detection under varying field conditions using deep learning

Source: [Towards agricultural autonomy: crop row detection under varying field conditions using deep learning](<https://www.sota2.com/research/paper/68fa75691e209131cea67b3c>)

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

Data updated: 2025-10-26T03:33:53.801Z

Authors: Rajitha de Silva, Grzegorz Cielniak, Junfeng Gao

Published: 2021-09-16T23:12:08.000Z

## Abstract

This paper presents a novel metric to evaluate the robustness of deep learning based semantic segmentation approaches for crop row detection under different field conditions encountered by a field robot. A dataset with ten main categories encountered under various field conditions was used for testing. The effect on these conditions on the angular accuracy of crop row detection was compared. A deep convolutional encoder decoder network is implemented to predict crop row masks using RGB input images. The predicted mask is then sent to a post processing algorithm to extract the crop rows. The deep learning model was found to be robust against shadows and growth stages of the crop while the performance was reduced under direct sunlight, increasing weed density, tramlines and discontinuities in crop rows when evaluated with the novel metric.

## 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.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2109.08247>)

[PDF](<https://arxiv.org/pdf/2109.08247.pdf>)