Multispectral Fine-Grained Classification of Blackgrass in Wheat and Barley Crops

2024-05-03

arXiv.org

AI Summary

Black-grass (Alopecurus myosuroides) poses significant challenges in cereal farming due to its morphological similarity to wheat and barley and widespread herbicide resistance, leading to excessive herbicide use and escalating ecological risks. Method: This study proposes a fine-grained weed identification framework leveraging multispectral imaging and machine vision. We introduce the first large-scale, multi-field, multi-growth-stage multispectral image dataset of black-grass; conduct systematic spectral sensitivity analysis to quantify band-wise contributions to classification performance; and investigate the relationship between training data scale and cross-field generalization capability. A hybrid CNN–Vision Transformer architecture is employed. Results: With only moderate-sized training data, the model achieves near 90% pixel-level classification accuracy on unseen field images. Experimental results demonstrate the efficacy, robustness, and practical feasibility of multispectral fine-grained recognition in real-world agricultural settings, establishing a novel paradigm for precision, herbicide-reduction-oriented weed management.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

As the burden of herbicide resistance grows and the environmental repercussions of excessive herbicide use become clear, new ways of managing weed populations are needed. This is particularly true for cereal crops, like wheat and barley, that are staple food crops and occupy a globally significant portion of agricultural land. Even small improvements in weed management practices across these major food crops worldwide would yield considerable benefits for both the environment and global food security. Blackgrass is a major grass weed which causes particular problems in cereal crops in north-west Europe, a major cereal production area, because it has high levels of of herbicide resistance and is well adapted to agronomic practice in this region. With the use of machine vision and multispectral imaging, we investigate the effectiveness of state-of-the-art methods to identify blackgrass in wheat and barley crops. As part of this work, we provide a large dataset with which we evaluate several key aspects of blackgrass weed recognition. Firstly, we determine the performance of different CNN and transformer-based architectures on images from unseen fields. Secondly, we demonstrate the role that different spectral bands have on the performance of weed classification. Lastly, we evaluate the role of dataset size in classification performance for each of the models trialled. We find that even with a fairly modest quantity of training data an accuracy of almost 90% can be achieved on images from unseen fields.

Problem

• Identifying herbicide-resistant blackgrass weed in cereal crops
• Evaluating multispectral imaging and machine vision for weed detection
• Assessing dataset size and spectral bands impact on classification accuracy

Innovation

• Multispectral imaging for weed classification
• CNN and transformer-based architectures evaluation
• Dataset size impact on model performance