Pesticide usage practices and the exposure risk to pollinators: A case study in the North China Plain

Mu, Hongyu; Wang, Kai; Yang, Xiaomei; Xu, Wen; Liu, Xuejun; Ritsema, C.J.; Geissen, Violette

doi:10.1016/j.ecoenv.2022.113713

Cited by 21 publications

(6 citation statements)

References 31 publications

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“…To combat these diseases, the primary approach is the application of pesticides [3]. However, the conventional methods of agricultural chemical application often overlook the severity of the disease, leading to inconsistent dosages administered across different areas, with some areas receiving insufficient treatment while others experience excessive chemical exposure [4,5]. Moreover, the incidence of pepper leaf diseases has shown a consistent increase over time.…”

Section: Introductionmentioning

confidence: 99%

AMS-MLP: Adaptive Multi-Scale MLP Network with Multi-Scale Context Relation Decoder for Pepper Leaf Segmentation

Fang,

Jiang,

Liu

et al. 2024

Preprint

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Pepper leaf segmentation plays a crucial role in monitoring pepper leaf diseases in various backgrounds and ensuring the healthy growth of peppers. However, existing transformer-based segmentation methods suffer from computational inefficiency, excessive parameterization, and limited utilization of edge information. To tackle these challenges, we propose an adaptive multi-scale MLP framework, named AMS-MLP, which combines the multi-path aggregation module (MPAM) and the multi-scale context relation mask module (MCRD) to refine the object boundaries in pepper leaf segmentation. AMS-MLP consists of an encoder-based network, an adaptive multi-scale MLP (AM-MLP) module, and a decoder network. In the encoder network, the MPAM module effectively fuses five-scale features to generate a single-channel mask, improving the accuracy of pepper leaf boundary extraction. The AM-MLP module divides the input features into two branches: the global multi-scale MLP branch captures long-range dependencies between image information, while the local multi-scale MLP branch focuses on extracting local feature maps. Adaptive attention mechanism is designed to dynamically adjust the weights of global and local features. The decoder network incorporates the MCRD module into the convolutional layer, enhancing the extraction of boundary features. To verify the performance of the proposed method, we conducted extensive experiments on three pepper leaf datasets with different backgrounds. The results demonstrate mIoU scores of 97.39%, 96.91%, and 97.91%, as well as F1 scores of 98.29%, 97.86%, and 98.51%, respectively. Comparative analysis with U-Net and state-of-the-art models reveals that the proposed method dramatically improves the accuracy and efficiency of pepper leaf image segmentation.

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Section: Introductionmentioning

confidence: 99%

AMS-MLP: Adaptive Multi-Scale MLP Network with Multi-Scale Context Relation Decoder for Pepper Leaf Segmentation

Fang,

Jiang,

Liu

et al. 2024

Preprint

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“…However, apple leaves are often affected by pests and diseases, and failure to identify and prevent diseases promptly can easily lead to a decrease in fruit quality and yield, causing severe economic losses to growers ( Bansal et al., 2021 ; Yogeshwari and Thailambal, 2021 ; Aggarwal et al., 2023 ). In reality, the identification of crop diseases usually relies on manual work ( Chen et al., 2022 ; Mu et al., 2022 ). The manual approach has many drawbacks, such as excessive subjectivity, low efficiency, and high recognition error rate ( Barman et al., 2020 ; Xu et al., 2023 ).…”

Section: Introductionmentioning

confidence: 99%

MixSeg: a lightweight and accurate mix structure network for semantic segmentation of apple leaf disease in complex environments

Lu,

et al. 2023

Front. Plant Sci.

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IntroductionSemantic segmentation is effective in dealing with complex environments. However, the most popular semantic segmentation methods are usually based on a single structure, they are inefficient and inaccurate. In this work, we propose a mix structure network called MixSeg, which fully combines the advantages of convolutional neural network, Transformer, and multi-layer perception architectures.MethodsSpecifically, MixSeg is an end-to-end semantic segmentation network, consisting of an encoder and a decoder. In the encoder, the Mix Transformer is designed to model globally and inject local bias into the model with less computational cost. The position indexer is developed to dynamically index absolute position information on the feature map. The local optimization module is designed to optimize the segmentation effect of the model on local edges and details. In the decoder, shallow and deep features are fused to output accurate segmentation results.ResultsTaking the apple leaf disease segmentation task in the real scene as an example, the segmentation effect of the MixSeg is verified. The experimental results show that MixSeg has the best segmentation effect and the lowest parameters and floating point operations compared with the mainstream semantic segmentation methods on small datasets. On apple alternaria blotch and apple grey spot leaf image datasets, the most lightweight MixSeg-T achieves 98.22%, 98.09% intersection over union for leaf segmentation and 87.40%, 86.20% intersection over union for disease segmentation.DiscussionThus, the performance of MixSeg demonstrates that it can provide a more efficient and stable method for accurate segmentation of leaves and diseases in complex environments.

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“…The intense pesticide use in this region has led to soil contamination and potential risks to ecological endpoints (Mu et al, 2022b;. Pesticides were frequently misused in the study region with excessive amounts of fungicides application observed ( Mu et al, 2022a). Meanwhile, multiple residues were detected in surface soils, moreover, nearly half of monitored sites showed high ecological risks to soil biota .…”

Section: Silicone Wristbands With the Ability To Absorb Volatilementioning

confidence: 99%

“…S5.4). Given that these fungicides were used in significantly higher dosages in the study region (Mu et al, 2022a), they contributed substantially to the occupational exposure of farmers during pesticide applications. The overlap between the driver pesticides and the exposure contributors to farmers and bystanders indicates that the exposure to driver pesticides might have originated from occupational exposure, while the exposure to other pesticides could have originated from dissipated pesticide drifts from other regions.…”

Section: 2c)mentioning

confidence: 99%

“…Quzhou county, a typical agricultural county located at the center of the NCP, was selected as the case study site in this study. It is studied that overuse pesticides and using forbidden pesticides contributed to Pesticides were found to be largely applied in the field with overuse and using forbidden ones occur in roughly 50% of pesticide application events in the NCP, which is asking for highlights the need for proper regulation to supervise pesticide use, as well as potential systemic monitoring of pesticides in the soil, and a comprehensive risks assessment for in soil biota (Mu et al, 2022a). Ranking the pesticides to be used based on multiple critical and quantitative criteria is important for the sustainable management and risk mitigation of pollutants (Li, 2022;Sang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Field and residential exposure of pesticides : Integrated risk analysis on terrestrial ecosystem and rural residents

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Pesticide usage practices and the exposure risk to pollinators: A case study in the North China Plain

Cited by 21 publications

References 31 publications

AMS-MLP: Adaptive Multi-Scale MLP Network with Multi-Scale Context Relation Decoder for Pepper Leaf Segmentation

AMS-MLP: Adaptive Multi-Scale MLP Network with Multi-Scale Context Relation Decoder for Pepper Leaf Segmentation

MixSeg: a lightweight and accurate mix structure network for semantic segmentation of apple leaf disease in complex environments

Field and residential exposure of pesticides : Integrated risk analysis on terrestrial ecosystem and rural residents

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