Attention-aided semantic segmentation network for weed identification in pineapple field

Cai, Yulin; Zeng, Fanguo; Xiao, Jing; Ai, Wenjie; Kang, Gaobi; Lin, Yongda; Cai, Zhuxuan; Shi, Haowen; Zhong, Shuxin; Yue, Xuejun

doi:10.1016/j.compag.2023.107881

Cited by 10 publications

(2 citation statements)

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“…Bishwa et al [82] used a UAV to collect weed images in morning glory and cotton to generate synthetic images and trained them with the Mask R-CNN model, proving the effectiveness of using synthetic images to train weed models to detect models. Cai et al [83] proposed a weed segmentation network that used the drone platform to identify weeds in pineapple fields. The ECA module was inserted into the model to improve the performance of the weed detection model without significantly changing the size of the model.…”

Section: Weed Detection and Classificationmentioning

confidence: 99%

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Advancements of UAV and Deep Learning Technologies for Weed Management in Farmland

Zhang,

Yu,

Zhang

et al. 2024

Agronomy

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With the continuous growth of the global population and the increasing demand for crop yield, enhancing crop productivity has emerged as a crucial research objective on a global scale. Weeds, being one of the primary abiotic factors impacting crop yield, contribute to approximately 13.2% of annual food loss. In recent years, Unmanned Aerial Vehicle (UAV) technology has developed rapidly and its maturity has led to widespread utilization in improving crop productivity and reducing management costs. Concurrently, deep learning technology has become a prominent tool in image recognition. Convolutional Neural Networks (CNNs) has achieved remarkable outcomes in various domains, including agriculture, such as weed detection, pest identification, plant/fruit counting, maturity grading, etc. This study provides an overview of the development of UAV platforms, the classification of UAV platforms and their advantages and disadvantages, as well as the types and characteristics of data collected by common vision sensors used in agriculture, and discusses the application of deep learning technology in weed detection. The manuscript presents current advancements in UAV technology and CNNs in weed management tasks while emphasizing the existing limitations and future trends in its development process to assist researchers working on applying deep learning techniques to weed management.

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Section: Weed Detection and Classificationmentioning

confidence: 99%

“…For weed mapping tasks, commonly used convolutional neural network models include U-Net [126], Mask R-CNN [82], FCN, PSPNet [83], DeepLab [78], etc. In [126], the study collected datasets of barley fields using unmanned aerial vehicles (UAVs) equipped with multi-spectral and thermal imagers.…”

Section: Algorithm Selectionmentioning

confidence: 99%