Image Classification for Soybean and Weeds Based on ViT

Liang, Jingxin; Wang, Dong; Ling, Xufeng

doi:10.1088/1742-6596/2002/1/012068

Cited by 8 publications

(4 citation statements)

References 2 publications

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“…The performance of 1Dtransformer models have generally been less studied compared to its 2D counterpart [47,48] and other CNN-based models. Our study proved its slight superiority in classifying weeds (mainly greenish), small-grain crops (mainly yellowish), and 'other', as well as estimating total weed coverage in sub-field plots.…”

Section: Discussionmentioning

confidence: 99%

“…Multi-Head Attention was adopted to further increase the flexibility of the information to be focused and potentially to boost the performance of a transformer model [46]. Transformer models have recently also proved to perform better than CNNbased DL models in different weeds and crop classifications [47,48], and even in semantical segmentation of different weeds species [48]. However, these studies focused on highresolution imagery taken by proximal sensors or UAVs flying at low altitudes, and DLbased semantic segmentation frameworks were only implemented in a 2D space, e.g., both 2D transformer and 2D CNN.…”

Section: Introductionmentioning

confidence: 99%

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Transformer in UAV Image-Based Weed Mapping

Zhao,

Berge,

Geipel

2023

Remote Sensing

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Weeds affect crop yield and quality due to competition for resources. In order to reduce the risk of yield losses due to weeds, herbicides or non-chemical measures are applied. Weeds, especially creeping perennial species, are generally distributed in patches within arable fields. Hence, instead of applying control measures uniformly, precision weeding or site-specific weed management (SSWM) is highly recommended. Unmanned aerial vehicle (UAV) imaging is known for wide area coverage and flexible operation frequency, making it a potential solution to generate weed maps at a reasonable cost. Efficient weed mapping algorithms need to be developed together with UAV imagery to facilitate SSWM. Different machine learning (ML) approaches have been developed for image-based weed mapping, either classical ML models or the more up-to-date deep learning (DL) models taking full advantage of parallel computation on a GPU (graphics processing unit). Attention-based transformer DL models, which have seen a recent boom, are expected to overtake classical convolutional neural network (CNN) DL models. This inspired us to develop a transformer DL model for segmenting weeds, cereal crops, and ‘other’ in low-resolution RGB UAV imagery (about 33 mm ground sampling distance, g.s.d.) captured after the cereal crop had turned yellow. Images were acquired during three years in 15 fields with three cereal species (Triticum aestivum, Hordeum vulgare, and Avena sativa) and various weed flora dominated by creeping perennials (mainly Cirsium arvense and Elymus repens). The performance of our transformer model, 1Dtransformer, was evaluated through comparison with a classical DL model, 1DCNN, and two classical ML methods, i.e., random forest (RF) and k-nearest neighbor (KNN). The transformer model showed the best performance with an overall accuracy of 98.694% on pixels set aside for validation. It also agreed best and relatively well with ground reference data on total weed coverage, R2 = 0.598. In this study, we showed the outstanding performance and robustness of a 1Dtransformer model for weed mapping based on UAV imagery for the first time. The model can be used to obtain weed maps in cereals fields known to be infested by perennial weeds. These maps can be used as basis for the generation of prescription maps for SSWM, either pre-harvest, post-harvest, or in the next crop, by applying herbicides or non-chemical measures.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Transformer in UAV Image-Based Weed Mapping

Zhao,

Berge,

Geipel

2023

Remote Sensing

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“…The introduction of Vision Transformer (ViT) [35] signified a major shift in deep learning for image analysis, proposed by Google in 2020. ViT represents the first successful application of the Transformer structure, initially designed for natural language processing (NLP) tasks [36], for image classification tasks, subsequently demonstrating significant potential in plant disease detection [37]. This innovation not only revealed the potential of the Transformer structure in processing non-sequential data [38] but also initiated a new chapter in the application of self-attention mechanisms in the field of computer vision [39].…”

Section: Vision Transformermentioning

confidence: 99%

Integration of Image and Sensor Data for Improved Disease Detection in Peach Trees Using Deep Learning Techniques

Chen,

Lang,

et al. 2024

Agriculture

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An innovative framework for peach tree disease recognition and segmentation is proposed in this paper, with the aim of significantly enhancing model performance in complex agricultural settings through deep learning techniques and data fusion strategies. The core innovations include a tiny feature attention mechanism backbone network, an aligned-head module, a Transformer-based semantic segmentation network, and a specially designed alignment loss function. The integration of these technologies not only optimizes the model’s ability to capture subtle disease features but also improves the efficiency of integrating sensor and image data, further enhancing the accuracy of the segmentation tasks. Experimental results demonstrate the superiority of this framework. For disease detection, the proposed method achieved a precision of 94%, a recall of 92%, and an accuracy of 92%, surpassing classical models like AlexNet, GoogLeNet, VGGNet, ResNet, and EfficientNet. In lesion segmentation tasks, the proposed method achieved a precision of 95%, a recall of 90%, and an mIoU of 94%, significantly outperforming models such as SegNet, UNet, and UNet++. The introduction of the aligned-head module and alignment loss function provides an effective solution for processing images lacking sensor data, significantly enhancing the model’s capability to process real agricultural image data. Through detailed ablation experiments, the study further validates the critical role of the aligned-head module and alignment loss function in enhancing model performance, particularly in the attention-head ablation experiment where the aligned-head configuration surpassed other configurations across all metrics, highlighting its key role in the overall framework. These experiments not only showcase the theoretical effectiveness of the proposed method but also confirm its practical value in agricultural disease management practices.

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“…Experiments were conducted to compare the effect of varying training and test set sizes. Similarly, Liang et al [ 24 ] used ViT for the classification of soybean and weeds.…”

Section: Related Studiesmentioning

confidence: 99%

Transformer-Based Weed Segmentation for Grass Management

Jiang

Afzaal

Lee

2022

Sensors

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Weed control is among the most challenging issues for crop cultivation and turf grass management. In addition to hosting various insects and plant pathogens, weeds compete with crop for nutrients, water and sunlight. This results in problems such as the loss of crop yield, the contamination of food crops and disruption in the field aesthetics and practicality. Therefore, effective and efficient weed detection and mapping methods are indispensable. Deep learning (DL) techniques for the rapid recognition and localization of objects from images or videos have shown promising results in various areas of interest, including the agricultural sector. Attention-based Transformer models are a promising alternative to traditional constitutional neural networks (CNNs) and offer state-of-the-art results for multiple tasks in the natural language processing (NLP) domain. To this end, we exploited these models to address the aforementioned weed detection problem with potential applications in automated robots. Our weed dataset comprised of 1006 images for 10 weed classes, which allowed us to develop deep learning-based semantic segmentation models for the localization of these weed classes. The dataset was further augmented to cater for the need of a large sample set of the Transformer models. A study was conducted to evaluate the results of three types of Transformer architectures, which included Swin Transformer, SegFormer and Segmenter, on the dataset, with SegFormer achieving final Mean Accuracy (mAcc) and Mean Intersection of Union (mIoU) of 75.18% and 65.74%, while also being the least computationally expensive, with just 3.7 M parameters.

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Image Classification for Soybean and Weeds Based on ViT

Cited by 8 publications

References 2 publications

Transformer in UAV Image-Based Weed Mapping

Transformer in UAV Image-Based Weed Mapping

Integration of Image and Sensor Data for Improved Disease Detection in Peach Trees Using Deep Learning Techniques

Transformer-Based Weed Segmentation for Grass Management

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