Weed detection in soybean crops using ConvNets
By Alessandro dos Santos Ferreira, Daniel Matte Freitas, Gercina Gonçalves da Silva, Hemerson Pistori, Marcelo Theophilo Folhes. Computers and Electronics in Agriculture, 2017
492 citations recorded by the source corpus.
Subfields: Plant Science
View Paper: https://www.semanticscholar.org/paper/39c958e3e72fdb488cf64e194bcf781b42d89e9b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Plant Science
- Johan Potgieter
- Muhammad Hammad Saleem
- Arpit Jain
- Stamatia Bibi
- Panagiotis G. Sarigiannidis
- Dimosthenis C. Tsouros
- Khalid Mahmood Arif
- Abhinav Sharma
Citations in the corpus
- A Survey of Deep Learning Techniques for Weed Detection from Images (2021, methodology): “On the other hand, dos Santos Ferreira et al. (2019) proposed a semi-automatic labelling approach. Unlike semi-supervised data annotation, they did not use any manually labelled data, but applied the clustering method to label the data. First, they divided the data into different clusters according ”
- A Review on UAV-Based Applications for Precision Agriculture (2019, background): “In general, ANNs present higher accuracy, compared to other classification algorithms, that reaches up to 99% for weed mapping in some cases [98].”
- Machine Learning Applications for Precision Agriculture: A Comprehensive Review (2021, methodology): “[77] deployed CNN for the identification of weeds in soybean crops.”
- DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning (2018, background): “The majority of current weed species classification methods also lean towards weed control in cropping applications 7,28–30 , where classification using machine vision is simple because the land is often flat, vegetation homogeneous and the light conditions are controlled.”
- Deep learning and machine vision for food processing: A survey (2021, background): “Dos Santos Ferreira (2017) developed software to segment and detect weeds growing among soybean crops.”
- Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments (2021, methodology): “To perform the crop/weed discrimination task, ResNet outperformed SVM and RF models (Bah et al., 2018), and, as described in (dos Santos Ferreira et al., 2017), a Convnet achieved better precision than RF.”