A two-stage feature aggregation network for multi-category soybean leaf disease identification

Pan, Renyong; Lin, Jie; Cai, Jitong; Zhang, Licai; Liu, Jiaming; Wen, Xingtian; Chen, Xiaoyulong; Zhang, Xin

doi:10.1016/j.jksuci.2023.101669

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…Merr. ), a crucial legume and oil crop recognized for its high nutritional value, is extensively grown globally [1,2]. The widespread cultivation of soybean is driven by its importance in agribusiness and the food industry and by its contribution to enhancing food security and soil fertility [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…Conversely, opting for higher volumes of chemical fertilizers would prove economically impractical for farmers and could adversely affect the environment and soil properties. Therefore, in this study, we aimed (1) to study the individual and integrated application of inorganic fertilizer (urea) and organic fertilizer (cow manure) on the growth and yield of soybeans in Afghanistan, (2) to find out the effectively integrated amounts of organic and inorganic fertilizers, and (3) to determine the usability of this method for farmers within the county.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Soybean Crop Performance through the Integrated Application of Organic and Chemical Fertilizers: A Study on Alkaline Soil in Afghanistan

Turabi,

Habibi,

Kakar

et al. 2024

Crops

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The excess application of chemical fertilizer contributes to environmental pollution. Therefore, this study aims to explore the integrated use of animal manure and chemical fertilizer to identify a more sustainable and environmentally friendly alternative to nitrogen fertilizer. Two experiments were conducted in 2018 and 2019 at Kabul University, Afghanistan, using an LD04-13265 soybean variety. The integration of animal manure and chemical fertilizer was categorized into six treatments: (1) control (no fertilizer), (2) animal manure low dose (AMLD), (3) animal manure high dose (AMHD), (4) chemical fertilizer (CF), (5) CF 50% + AMLD, and (6) CF 75% + AMLD. The results revealed that AMHD and its integration with 75% chemical fertilizer greatly influenced the vegetative growth of soybean plants in experiments I and II. The number of pods per plant and pod length significantly (p < 0.05) increased under the CF 75% + AMLD treatment. In experiment I, soybean yield increased the most with the CF treatment, followed by CF 75% + AMLD. In contrast, in experiment II, the highest yield per hectare was observed in the CF 75% + AMLD treatment, followed by CF. The CF 75% + AMLD treatment significantly increased the SPAD value in both experiments. Consequently, a strong relationship was observed between the SPAD value and yield (r = 0.74) in experiment I and between SPAD and pod length (r = 0.82) in experiment II. Incorporating animal manure with chemical fertilizer significantly impacted soybean growth and yield, offering a potential possibility for reducing reliance on nitrogen fertilizer application to mitigate environmental pollution.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing Soybean Crop Performance through the Integrated Application of Organic and Chemical Fertilizers: A Study on Alkaline Soil in Afghanistan

Turabi,

Habibi,

Kakar

et al. 2024

Crops

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FG‐UNet: fine‐grained feature‐guided UNet for segmentation of weeds and crops in UAV images

Lin,

Zhang,

Qin

et al. 2024

Pest Management Science

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BACKGROUNDSemantic segmentation of weed and crop images is a key component and prerequisite for automated weed management. For weeds in unmanned aerial vehicle (UAV) images, which are usually characterized by small size and easily confused with crops at early growth stages, existing semantic segmentation models have difficulties to extract sufficiently fine features. This leads to their limited performance in weed and crop segmentation of UAV images.RESULTSWe proposed a fine‐grained feature‐guided UNet, named FG‐UNet, for weed and crop segmentation in UAV images. Specifically, there are two branches in FG‐UNet, namely the fine‐grained feature branch and the UNet branch. In the fine‐grained feature branch, a fine feature‐aware (FFA) module was designed to mine fine features in order to enhance the model's ability to segment small objects. In the UNet branch, we used an encoder–decoder structure to realize high‐level semantic feature extraction in images. In addition, a contextual feature fusion (CFF) module was designed for the fusion of the fine features and high‐level semantic features, thus enhancing the feature discrimination capability of the model. The experimental results showed that our proposed FG‐UNet, achieved state‐of‐the‐art performance compared to other semantic segmentation models, with mean intersection over union (MIOU) and mean pixel accuracy (MPA) of 88.06% and 92.37%, respectively.CONCLUSIONThe proposed method in this study lays a solid foundation for accurate detection and intelligent management of weeds. It will have a positive impact on the development of smart agriculture. © 2024 Society of Chemical Industry.

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I3-YOLOv8s: An improved YOLOv8s for infrequent irregular imbalanced detection and segmentation of rape stomata

Gong,

Zhang,

Cheng

et al. 2025

Expert Systems with Applications

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A two-stage feature aggregation network for multi-category soybean leaf disease identification

Cited by 5 publications

References 30 publications

Optimizing Soybean Crop Performance through the Integrated Application of Organic and Chemical Fertilizers: A Study on Alkaline Soil in Afghanistan

Optimizing Soybean Crop Performance through the Integrated Application of Organic and Chemical Fertilizers: A Study on Alkaline Soil in Afghanistan

FG‐UNet: fine‐grained feature‐guided UNet for segmentation of weeds and crops in UAV images

I3-YOLOv8s: An improved YOLOv8s for infrequent irregular imbalanced detection and segmentation of rape stomata

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