Partially occluded weed classification using vision transformers and convolutional neural networks for precision agriculture
Hall, Euan, Zuza, Emmanuel Junior, Rial-Lovera, Karen and McCarthy, Chris (2026) Partially occluded weed classification using vision transformers and convolutional neural networks for precision agriculture. Scientific reports, 16 (28616). ISSN 2045-2322
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Abstract
Automated weed detection is essential for site-specific herbicide application, that can result into the reduced environmental footprint of conventional agriculture. However, for field deployment of automated weeding devices, occlusion remains a critical challenge that can weaken the precision of weed identification. Here, we compare the performance of Vision Transformers (ViT-B16 & PvTv2) and Convolutional Neural Networks (EfficientNet-B0 & ResNet-50) in accurate weed detection, using controlled synthetic occlusion levels (0%, 25%, and 50%). We found that ViT-B16 has superior occlusion resilience, with image testing accuracy increasing from 80% to 86% under 50% occlusion. In contrast, the testing accuracy of PvTv2, EfficientNet-B0 and ResNet-50 dropped from 45 to 76% under similar conditions. Multivariable regression confirmed architecture type as the dominant testing accuracy driver (p ≤ 0.001), with ViTs outperforming CNNs by an average of 14.56 percentage points. These results suggest that occlusion resilience is not uniform across architectural variants but depends critically on attention-based design. Consequently, for real time deployable automatic weed detection systems, hybrid architectures that balance ViT global context with CNN computational efficiency represent a critical future direction. Such approaches can support precise herbicide application, reduce chemical inputs, and enable more sustainable crop protection through reliable AI-driven automation.
| Item Type: | Article |
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| Keywords: | Vision transformers, Convolutional neural networks, Weed classification, Occlusion, Precision agriculture |
| Divisions: | Agriculture, Science and Practice |
| Depositing User: | Dr Emmanuel Zuza |
| Date Deposited: | 29 Sep 2026 17:43 |
| Last Modified: | 29 Sep 2026 17:43 |
| URI: | https://rau.repository.guildhe.ac.uk/id/eprint/17137 |
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