A W-Shaped Convolutional Network for Robust Crop and Weed Classification in Agriculture

Authors

Syed Imran Moazzam, National University of Science and Technology - PakistanFollow
Tahir Nawaz, National University of Science and Technology - PakistanFollow
Waqar Shahid Qureshi, Technological University DublinFollow
Umar Shahbaz Khan, National University of Science and Technology - PakistanFollow
Mohsin Islam Tiwan, National University of Science and Technology - PakistanFollow

Author ORCID Identifier

https://orcid.org/0000-0003-0176-8145

Document Type

Article

Disciplines

1.2 COMPUTER AND INFORMATION SCIENCE, Computer Sciences

Publication Details

DOI:10.1007/s11119-023-10027-7
Precision Agriculture, Springer Nature

Abstract

Agricultural image and vision computing are significantly different from other object classification-based methods because two base classes in agriculture, crops and weeds, have many common traits. Efficient crop, weeds, and soil classification are required to perform autonomous (spraying, harvesting, etc.) activities in agricultural fields. In a three-class (crop-weed-background) agricultural classification scenario, it is usually easier to accurately classify the background class than the crop and weed classes because the background class appears significantly different feature-wise than the crop and weed classes. However, robustly distinguishing between the crop and weed classes is challenging because their appearance features generally look very similar. To address this problem, we propose a framework based on a convolutional W-shaped network with two encoder-decoder structures of different sizes. The first encoder-decoder structure differentiates between background and vegetation (crop and weed), and the second encoder-decoder structure learns discriminating features to classify crop and weed classes efficiently. The proposed W network is generalizable for different crop types. The effectiveness of the proposed network is demonstrated on two crop datasets – a tobacco dataset and a sesame dataset, both collected in this study and made available publicly online for use by the community – by evaluating and comparing the performance with existing related methods. The proposed method consistently outperforms existing related methods on both datasets.

DOI

https://doi.org/10.1007/s11119-023-10027-7

Recommended Citation

Moazzam, Syed Imran; Nawaz, Tahir; Qureshi, Waqar Shahid; Khan, Umar Shahbaz; and Tiwan, Mohsin Islam, "A W-Shaped Convolutional Network for Robust Crop and Weed Classification in Agriculture" (2023). Articles. 184.
https://arrow.tudublin.ie/scschcomart/184

Funder

Higher Education Commission of Pakistan; National Centre of Robotics and Automation

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.