# Fully Convolutional Networks with Sequential Information for Robust Crop and Weed Detection in Precision Farming

arXiv: https://arxiv.org/abs/1806.03412 · HTML: https://mlsanity.com/paper/1806.03412/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2018.

## Built on by (most cited first, 15 of 15)

- [PhenoBench -- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain](https://mlsanity.com/paper/2306.04557/) (2023)
- [A Survey of Deep Learning Techniques for Weed Detection from Images](https://mlsanity.com/paper/2103.01415/) (2021)
- [Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf Instance Segmentation in the Agricultural Domain](https://mlsanity.com/paper/2210.07879/) (2022)
- [GrowliFlower: An image time series dataset for GROWth analysis of cauLIFLOWER](https://mlsanity.com/paper/2204.00294/) (2022)
- [Virtual Temporal Samples for Recurrent Neural Networks: applied to semantic segmentation in agriculture](https://mlsanity.com/paper/2106.10118/) (2021)
- [Data-Centric Digital Agriculture: A Perspective](https://mlsanity.com/paper/2312.03437/) (2023)
- [Towards practical object detection for weed spraying in precision agriculture](https://mlsanity.com/paper/2109.11048/) (2021)
- [Deep Learning Techniques for In-Crop Weed Identification: A Review](https://mlsanity.com/paper/2103.14872/) (2021)
- [Simultaneously Predicting Multiple Plant Traits from Multiple Sensors via Deformable CNN Regression](https://mlsanity.com/paper/2112.03205/) (2021)
- [Deep-CNN based Robotic Multi-Class Under-Canopy Weed Control in Precision Farming](https://mlsanity.com/paper/2112.13986/) (2021)
- [On Domain-Specific Pre-Training for Effective Semantic Perception in Agricultural Robotics](https://mlsanity.com/paper/2303.12499/) (2023)
- [Resource-Constrained UAV-Based Weed Detection for Site-Specific Management on Edge Devices](https://mlsanity.com/paper/2604.23442/) (2026)
- [Transferring learned patterns from ground-based field imagery to predict UAV-based imagery for crop and weed semantic segmentation in precision crop farming](https://mlsanity.com/paper/2210.11545/) (2022)
- [WeedRepFormer: Reparameterizable Vision Transformers for Real-Time Waterhemp Segmentation and Gender Classification](https://mlsanity.com/paper/2601.03431/) (2026)
- [A Multi-Dataset Benchmark of YOLO-Based Weed Detection in Precision Agriculture](https://mlsanity.com/paper/2609.33991/) (2026)

## Abstract (arXiv)

Reducing the use of agrochemicals is an important component towards sustainable agriculture. Robots that can perform targeted weed control offer the potential to contribute to this goal, for example, through specialized weeding actions such as selective spraying or mechanical weed removal. A prerequisite of such systems is a reliable and robust plant classification system that is able to distinguish crop and weed in the field. A major challenge in this context is the fact that different fields show a large variability. Thus, classification systems have to robustly cope with substantial environmental changes with respect to weed pressure and weed types, growth stages of the crop, visual appearance, and soil conditions. In this paper, we propose a novel crop-weed classification system that relies on a fully convolutional network with an encoder-decoder structure and incorporates spatial information by considering image sequences. Exploiting the crop arrangement information that is observable from the image sequences enables our system to robustly estimate a pixel-wise labeling of the images into crop and weed, i.e., a semantic segmentation. We provide a thorough experimental evaluation, which shows that our system generalizes well to previously unseen fields under varying environmental conditions --- a key capability to actually use such systems in precision framing. We provide comparisons to other state-of-the-art approaches and show that our system substantially improves the accuracy of crop-weed classification without requiring a retraining of the model.