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Weed Detection by Faster RCNN Model: An Enhanced Anchor Box Approach

Saleem, Muhammad Hammad; Potgieter, Johan; Arif, Khalid Mahmood

Weed Detection by Faster RCNN Model: An Enhanced Anchor Box Approach Thumbnail


Authors

Johan Potgieter

Khalid Mahmood Arif



Abstract

To apply weed control treatments effectively, the weeds must be accurately detected. Deep learning (DL) has been quite successful in performing the weed identification task. However, various aspects of the DL have not been explored in previous studies. This research aimed to achieve a high average precision (AP) of eight classes of weeds and a negative (non-weed) class, using the DeepWeeds dataset. In this regard, a DL-based two-step methodology has been proposed. This article is the second stage of the research, while the first stage has already been published. The former phase presented a weed detection pipeline and consisted of the evaluation of various neural networks, image resizers, and weight optimization techniques. Although a significant improvement in the mean average precision (mAP) was attained. However, the Chinee apple weed did not reach a high average precision. This result provided a solid ground for the next stage of the study. Hence, this paper presents an in-depth analysis of the Faster Region-based Convolutional Neural Network (RCNN) with ResNet-101, the best-obtained model in the past step. The architectural details of the Faster RCNN model have been thoroughly studied to investigate each class of weeds. It was empirically found that the generation of anchor boxes affects the training and testing performance of the Faster RCNN model. An enhancement to the anchor box scales and aspect ratios has been attempted by various combinations. The final results, with the addition of 64 × 64 scale size, and aspect ratio of 1:3 and 3:1, produced the best classification and localization of all classes of weeds and a negative class. An enhancement of 24.95% AP was obtained in Chinee apple weed. Furthermore, the mAP was improved by 2.58%. The robustness of the approach has been shown by the stratified k-fold cross-validation technique and testing on an external dataset.

Citation

Saleem, M. H., Potgieter, J., & Arif, K. M. (in press). Weed Detection by Faster RCNN Model: An Enhanced Anchor Box Approach. Agronomy, 12(7), 1580. https://doi.org/10.3390/agronomy12071580

Journal Article Type Article
Acceptance Date Jun 27, 2022
Online Publication Date Jun 29, 2022
Deposit Date Feb 17, 2024
Publicly Available Date Feb 20, 2024
Journal Agronomy
Print ISSN 2073-4395
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 12
Issue 7
Pages 1580
DOI https://doi.org/10.3390/agronomy12071580
Keywords Agronomy and Crop Science

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