用机器学习检测小麦作物的干旱压力检测技术
Ankita Gupta1, Lakhwinder Kaur1, Gurmeet Kaur2
1Computer Science and Engineering, Punjabi University, Patiala, Punjab, India.
PeerJ. Computer science
|June 22, 2023
概括
这项研究开发了一种自动化模型,用于通过先进的图像处理和机器学习来检测小麦作物的水应力. 随机森林算法实现了91.164%的准确性,为精准农业提供了可靠的解决方案.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 小麦作物的水压显著影响产量.
- 准确及时检测水压力对于有效的灌管理至关重要.
- 现有的水应激检测方法可能是劳动密集型,缺乏自动化.
研究的目的:
- 开发一种自动化模型来检测小麦作物的水压.
- 评估各种预处理和细分技术的有效性,以检测水应力.
- 确定最适合的机器学习算法来分类小麦作物水应力.
主要方法:
- 图像预处理使用总变化与L1数据保真度术语 (TV-L1) 无效化和最小至最大对比度拉伸.
- 使用基于曲线匹配的K-平均算法 (Cfit-kmeans) 的小麦树冠细分.
- 通过快速原型,超参数调整和10倍交叉验证来开发机器学习模型,测试九种不同的算法.
主要成果:
- 确定TV-L1无色化和最小最大对比度拉伸是最佳的预处理方法.
- Cfit-kmeans算法在小麦树冠细分方面表现出高准确度.
- 随机森林算法在测试模型中实现了全球最高的诊断准确率91.164%.
结论:
- 开发的自动化模型,特别是使用随机森林算法,非常适合检测小麦作物的水压.
- 集成先进的图像处理和机器学习技术为精密农业提供了强大的解决方案.
- 这项研究为改善灌策略和加强作物管理提供了基础.
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