Weed Identification in Maize Fields Based on Improved Swin-Unet

Zhang, Jiaheng; Gong, Jinliang; Zhang, Yanfei; Mostafa, Kazi; Yuan, Guangyao

doi:10.3390/agronomy13071846

Cited by 15 publications

(7 citation statements)

References 18 publications

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“…Through field trials, they identified the most effective weeding knife for different field conditions, achieving an impressive 85.91% weed removal rate and a minimal 1.17% crop injury rate. Zhang et al (2023) [131,134] introduce an improved Swin-Unet model for precise weed recognition in maize fields, achieving remarkable results with a mean intersection over union of 92.75% and a mean pixel accuracy of 95.57%. The model's high inference speed of 15.1 FPS further enhances its significance for real-time, accurate crop and weed segmentation, aiding the development of intelligent agricultural equipment.…”

Section: Advancements In Machine Learning For Weed Detectionmentioning

confidence: 99%

Advancements in precision control in intra-row weeding: A comprehensive review

Lahre,

Satpathy

2024

Int. J. Res. Agron.

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Sensor-based weed detection is a significant breakthrough in precise weed management that reduces the need for manual work and herbicides. GPS systems have shown promising results, with a single pass covering 30-49% of the intra-row area. Additionally, using an RGB sensor and a laser sensor together can identify the center of a plant, regardless of lighting conditions. Robots have demonstrated the ability to distinguish 99.7% of crop plants in dense outdoor areas with high weed density. These advancements offer opportunities for more effective and environmentally-friendly weed management in different farming systems.

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Section: Advancements In Machine Learning For Weed Detectionmentioning

confidence: 99%

Advancements in precision control in intra-row weeding: A comprehensive review

Lahre,

Satpathy

2024

Int. J. Res. Agron.

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“…It can effectively capture long-distance dependencies by computing the correlation between all positions within a sample. (Jiang et al, 2022;Reedha et al, 2022;Zhang et al, 2023a) have added SA to the network to apply it to crop and weed detection and segmentation tasks. However, SA possesses high computational complexity and does not address the inter-sample connection, which contradicts the original objective of designing a lightweight network in this paper.…”

Section: Dual Attention Blockmentioning

confidence: 99%

Attention-aided lightweight networks friendly to smart weeding robot hardware resources for crops and weeds semantic segmentation

Wei,

Feng,

Zhou

et al. 2023

Front. Plant Sci.

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Weed control is a global issue of great concern, and smart weeding robots equipped with advanced vision algorithms can perform efficient and precise weed control. Furthermore, the application of smart weeding robots has great potential for building environmentally friendly agriculture and saving human and material resources. However, most networks used in intelligent weeding robots tend to solely prioritize enhancing segmentation accuracy, disregarding the hardware constraints of embedded devices. Moreover, generalized lightweight networks are unsuitable for crop and weed segmentation tasks. Therefore, we propose an Attention-aided lightweight network for crop and weed semantic segmentation. The proposed network has a parameter count of 0.11M, Floating-point Operations count of 0.24G. Our network is based on an encoder and decoder structure, incorporating attention module to ensures both fast inference speed and accurate segmentation while utilizing fewer hardware resources. The dual attention block is employed to explore the potential relationships within the dataset, providing powerful regularization and enhancing the generalization ability of the attention mechanism, it also facilitates information integration between channels. To enhance the local and global semantic information acquisition and interaction, we utilize the refinement dilated conv block instead of 2D convolution within the deep network. This substitution effectively reduces the number and complexity of network parameters and improves the computation rate. To preserve spatial information, we introduce the spatial connectivity attention block. This block not only acquires more precise spatial information but also utilizes shared weight convolution to handle multi-stage feature maps, thereby further reducing network complexity. The segmentation performance of the proposed network is evaluated on three publicly available datasets: the BoniRob dataset, the Rice Seeding dataset, and the WeedMap dataset. Additionally, we measure the inference time and Frame Per Second on the NVIDIA Jetson Xavier NX embedded system, the results are 18.14 msec and 55.1 FPS. Experimental results demonstrate that our network maintains better inference speed on resource-constrained embedded systems and has competitive segmentation performance.

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“…Janneh et al [60] proposed an improved deep convolutional neural network (DCNN) combined with a lightweight backbone, a multi-level feature re-weighted fusion module, and a convolutional weighted fusion decoder for efficient weed control in agriculture. Zhang et al [61] introduced an improved Swin-Unet model for weed recognition in maize fields. The model addresses challenges such as color similarity and overlapping between weeds and maize, as well as varying lighting and weather conditions.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

Weed–Crop Segmentation in Drone Images with a Novel Encoder–Decoder Framework Enhanced via Attention Modules

Khan,

Basalamah,

Lbath

2023

Remote Sensing

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The rapid expansion of the world’s population has resulted in an increased demand for agricultural products which necessitates the need to improve crop yields. To enhance crop yields, it is imperative to control weeds. Traditionally, weed control predominantly relied on the use of herbicides; however, the indiscriminate application of herbicides presents potential hazards to both crop health and productivity. Fortunately, the advent of cutting-edge technologies such as unmanned vehicle technology (UAVs) and computer vision has provided automated and efficient solutions for weed control. These approaches leverage drone images to detect and identify weeds with a certain level of accuracy. Nevertheless, the identification of weeds in drone images poses significant challenges attributed to factors like occlusion, variations in color and texture, and disparities in scale. The utilization of traditional image processing techniques and deep learning approaches, which are commonly employed in existing methods, presents difficulties in extracting features and addressing scale variations. In order to address these challenges, an innovative deep learning framework is introduced which is designed to classify every pixel in a drone image into categories such as weed, crop, and others. In general, our proposed network adopts an encoder–decoder structure. The encoder component of the network effectively combines the Dense-inception network with the Atrous spatial pyramid pooling module, enabling the extraction of multi-scale features and capturing local and global contextual information seamlessly. The decoder component of the network incorporates deconvolution layers and attention units, namely, channel and spatial attention units (CnSAUs), which contribute to the restoration of spatial information and enhance the precise localization of weeds and crops in the images. The performance of the proposed framework is assessed using a publicly available benchmark dataset known for its complexity. The effectiveness of the proposed framework is demonstrated via comprehensive experiments, showcasing its superiority by achieving a 0.81 mean Intersection over Union (mIoU) on the challenging dataset.

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Weed Identification in Maize Fields Based on Improved Swin-Unet

Cited by 15 publications

References 18 publications

Advancements in precision control in intra-row weeding: A comprehensive review

Advancements in precision control in intra-row weeding: A comprehensive review

Attention-aided lightweight networks friendly to smart weeding robot hardware resources for crops and weeds semantic segmentation

Weed–Crop Segmentation in Drone Images with a Novel Encoder–Decoder Framework Enhanced via Attention Modules

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