使用GIS,绿地检测,天气API和GPT进行基于卫星图像的肥沃土地发现和作物适合性的融合方法
Ananthakrishnan Balasundaram1,2, A B Abdul Aziz3, Aman Gupta3
1Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India. balasundaram.a@vit.ac.in.
Scientific reports
|July 14, 2024
概括
这项研究使用计算机视觉和人工智能绘制可耕地的地图,并建议合适的作物,改善农业用地利用和粮食生产. 该模型在确定特定地区适合的作物的准确性达到80%.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 有效地利用农业用地是一个全球性的挑战,由于对其种植潜力缺乏了解,巨大的土地仍然处于休耕状态.
- 全球人口和粮食需求的增加需要提高农业生产率和土地利用效率.
研究的目的:
- 开发一种使用计算机视觉和机器学习的系统,以识别和绘制农业可耕地的地图.
- 根据环境和土壤条件,确定适合特定地块的作物.
主要方法:
- 使用面利技术增强卫星图像,特别是布罗维变换.
- 整合环境数据 (天气,土壤湿度,阳光) 与卫星图像.
- 使用一种生成式预训练过的变压器模型来预测特定土地块的合适作物.
主要成果:
- 拟议的融合方法在建议与印度调查区域的主导作物一致的作物方面实现了80%的准确率.
- 增强的卫星图像提高了陆地数据的解释性.
- 该模型成功地确定了可耕地,并根据综合数据推了适当的作物.
结论:
- 开发的系统展示了一种可行的方法,用于绘制可耕地的地图,并使用先进的图像处理和人工智能优化作物选择.
- 这项技术可以大大提高农业用地的利用率,提高粮食安全.
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