Real-Time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs

By Andres Milioto, Philipp Lottes, Cyrill Stachniss. IEEE International Conference on Robotics and Automation, 2017

434 citations recorded by the source corpus.

Precision farming robots, which target to reduce the amount of herbicides that need to be brought out in the fields, must have the ability to identify crops and weeds in real time to trigger weeding actions. In this paper, we address the problem of CNN-based semantic segmentation of crop fields separating sugar beet plants, weeds, and background solely based on RGB data. We propose a CNN that exploits existing vegetation indexes and provides a classification in real time. Furthermore, it can be effectively re-trained to so far unseen fields with a comparably small amount of training data. We implemented and thoroughly evaluated our system on a real agricultural robot operating in different fields in Germany and Switzerland. The results show that our system generalizes well, can operate at around 20 Hz, and is suitable for online operation in the fields.

Subfields: Artificial Intelligence, Plant Science

View Paper: https://www.semanticscholar.org/paper/a0cfaada6f2291bb83df4e202c4d81ff1695e89b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Johan Potgieter
2. Muhammad Hammad Saleem
3. Yassine Ruichek
4. Fahad Lateef
5. Khalid Mahmood Arif

In Plant Science

1. Johan Potgieter
2. Muhammad Hammad Saleem
3. Khalid Mahmood Arif

Citations in the corpus

• Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments (2021, methodology): “A CNN-based semantic segmentation for real-time crop and weed classification was done in a sugar beet field (Milioto et al., 2018).”
• Survey on semantic segmentation using deep learning techniques (2019, background): “…driving [2, 3], robot navigation [4], industrial inspection [5]; remote sensing [6]; In cognitive and computational sciences − saliency object detection [7, 8]; In Agriculture sciences [9]; Fashion − categorizing clothing items [10]; In medical sciences − medical imaging analysis [11] etc.”