UAV-based crop and weed classification for smart farming

By Philipp Lottes, Raghav Khanna, Johannes Pfeifer, Roland Yves Siegwart, Cyrill Stachniss. IEEE International Conference on Robotics and Automation, 2017

353 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Plant Science

View Paper: https://www.semanticscholar.org/paper/896c33aa816426e1d1f5a595708d8f904684517a

How researchers see this paper

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

In Ecology

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

In Environmental Engineering

1. Johan Potgieter
2. Muhammad Hammad Saleem
3. Alper Yilmaz
4. Khalid Mahmood Arif
5. Guisong Xia

In Plant Science

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

Citations in the corpus

• UAVid: A semantic segmentation dataset for UAV imagery (2018, background): “They have already been used for smart farming [25], precision agriculture [10], and weed monitoring [26], but few researches have been done for urban scene analysis.”
• Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: A comprehensive review (2020, methodology): “In [82] , the authors proposed a system that detects vegetation, extracts features, and classifies results using Random Forest (RF) technique to obtain an estimation of the crops and weeds distribution in the field of sugar beet plants.”
• Precision Agriculture Techniques and Practices: From Considerations to Applications (2019, neutral): “[1] N Y N N N Y Y N N N N [4] N Y N N N N N Y Y N N [28] N Y N N N N Y N N N N [29] N Y N N N N Y N N N N [41] N N Y N N N N N N N N [52] N Y N N N N Y N N N N [55] N N Y N Y Y N N Y N N [56] Y N Y Y Y Y N N N Y N [57] N Y Y Y Y Y N N N Y N [58] N N N Y N N N N N N N [59] N Y N N N N Y N N N N [60] ”
• Survey on Coverage Path Planning with Unmanned Aerial Vehicles (2019, background): “Unmanned Aerial Vehicles (UAVs) have increasingly been used in a wide range of applications, such as surveillance [1], smart farming [2], photogrammetry [3], disaster management, civil security [4], wildfire tracking [5], cloud monitoring [6], structure supervision [7], and power line inspection [8].”
• Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments (2021, methodology): “Another research was conducted for the discrimination between a sugar beet crop and weed by a UAV; using information from RGB images; classification was done by RF classifier to achieve high precision and recall (Lottes et al., 2017).”