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<p>Global population has grown from 1 billion in the 19th century to 7.9 billion today, and is projected to reach 9.2 billion by 2050. To meet the growing demand for food, we need to increase crop yields. However, it is a challenging task to keep crop yields high all the time. In the process of food production, crops are particularly vulnerable to pests and diseases. Spraying pesticides is a common and effective way to control pests and diseases in modern agriculture. However, excessive use of pesticides can damage the ecological environment, and pesticide residues can also harm human health. Therefore, it is essential to be able to evaluate spraying effects conveniently and accurately. This paper adopts water-sensitive paper (WSP) as a spraying evaluation tool. Previous studies have also moved the task of evaluating spraying results to mobile devices. Although they can analyze the results in real time, there are many limitations, such as the need for the paper to be photographed vertically and the image to be clear and free of noise. Therefore, this paper proposes a paper image processing flow that can break through the limitations of previous studies.per uses a Faster R-CNN model to identify the paper in the image, uses Grab Cut to extract the paper image, then calibrates the paper to a standard size, removes shadows, and uses the Otsu threshold method and K-means clustering method to segment the spray droplet area. The bounding box MIoU of paper identification is 0.9766; the MIoU of the WSP is 0.9824. In the subsequent experiments, the spraying coverage percentage evaluation results of three different types of paper images, namely regular paper (Regular WSP), scanned paper (Scan WSP), and outdoor paper (Outdoor WSP), are presented. The results of droplet segmentation are also compared with those of the DropLeaf water-sensitive paper analysis App proposed in previous studies. In the study of water-sensitive paper, it was found that there is currently no source of water-sensitive paper database. Due to the lack of a suitable database, this paper provides a method for synthesizing paper images. By inputting a scanned paper (Scan WSP) as the input image, a synthesized paper image can be generated, which will benefit researchers who want to conduct different experiments on water-sensitive paper in the future.</p>

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Water-sensitive paper detection and spray analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月12日

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  • Chia-Lin Wu,
  • Chuan-Kai Yang,
  • Ji-Yang Lin

摘要

Global population has grown from 1 billion in the 19th century to 7.9 billion today, and is projected to reach 9.2 billion by 2050. To meet the growing demand for food, we need to increase crop yields. However, it is a challenging task to keep crop yields high all the time. In the process of food production, crops are particularly vulnerable to pests and diseases. Spraying pesticides is a common and effective way to control pests and diseases in modern agriculture. However, excessive use of pesticides can damage the ecological environment, and pesticide residues can also harm human health. Therefore, it is essential to be able to evaluate spraying effects conveniently and accurately. This paper adopts water-sensitive paper (WSP) as a spraying evaluation tool. Previous studies have also moved the task of evaluating spraying results to mobile devices. Although they can analyze the results in real time, there are many limitations, such as the need for the paper to be photographed vertically and the image to be clear and free of noise. Therefore, this paper proposes a paper image processing flow that can break through the limitations of previous studies.per uses a Faster R-CNN model to identify the paper in the image, uses Grab Cut to extract the paper image, then calibrates the paper to a standard size, removes shadows, and uses the Otsu threshold method and K-means clustering method to segment the spray droplet area. The bounding box MIoU of paper identification is 0.9766; the MIoU of the WSP is 0.9824. In the subsequent experiments, the spraying coverage percentage evaluation results of three different types of paper images, namely regular paper (Regular WSP), scanned paper (Scan WSP), and outdoor paper (Outdoor WSP), are presented. The results of droplet segmentation are also compared with those of the DropLeaf water-sensitive paper analysis App proposed in previous studies. In the study of water-sensitive paper, it was found that there is currently no source of water-sensitive paper database. Due to the lack of a suitable database, this paper provides a method for synthesizing paper images. By inputting a scanned paper (Scan WSP) as the input image, a synthesized paper image can be generated, which will benefit researchers who want to conduct different experiments on water-sensitive paper in the future.