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• W4387048983 abstract "Over the past ten years, precision agriculture has raised much awareness in the agricultural sector. It automates and optimizes almost all agriculture practices. But the success of this technology depends upon the data. The more accurate and extensive the data is, the more accurate the system will be. Despite large datasets available online, there is still a lack of datasets from the Indian perspective. One of the main roadblocks to advancement is the shortage of publicly available statistics. This paper proposes a benchmark dataset named IndianPotatoWeeds for Potato crops and weeds from Indian farms. The dataset comprises 270 images with annotations and is available online https://www.kaggle.com/datasets/rajni88/indianpotatoweed-dataset . All images were acquired with the Sony CyberShot W830 20.1 M camera and mobile phone. There were intra and inter-row weeds present at the time of data collection. We have provided mask and manual annotation of the plant type (crop vs. weed) for every dataset image using VIA annotation tool. Images can be split into background and foreground via masking, enabling us to concentrate on the areas of the image that interest us. By making this information available to the public, we hope to encourage study in this field." @default.
• W4387048983 created "2023-09-27" @default.
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• W4387048983 date "2023-01-01" @default.
• W4387048983 modified "2023-09-27" @default.
• W4387048983 title "IndianPotatoWeeds: An Image Dataset of Potato Crop to Address Weed Issues in Precision Agriculture" @default.
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• W4387048983 doi "https://doi.org/10.1007/978-3-031-43605-5_9" @default.
• W4387048983 hasPublicationYear "2023" @default.
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