使用深度学习方法和数学模型监测和预测棉花叶病
Abdul Rehman1, Nadeem Akhtar2, Omar H Alhazmi3
1Faculty of Computing Science, The Islamia University of Bahawalpur, Bahawalpur, Pakistan. rehmanbn@gmail.com.
Scientific reports
|July 2, 2025
概括
本研究介绍了一种正式的建模方法,使用时间行动逻辑 (TLA+) 和深度学习来准确检测棉花作物疾病. 该方法显著提高了识别虫和粉等疾病的可靠性.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 正式方法 正式方法
背景情况:
- 全球棉花生产至关重要,但受到影响产量和经济的疾病的威胁.
- 准确监测和预测棉花作物疾病对于可持续农业和粮食安全至关重要.
研究的目的:
- 开发和验证一种改进棉花作物疾病监测和检测的方法.
- 通过正式方法和深度学习,提高疾病症状识别的可靠性.
主要方法:
- 正式建模和验证使用时间行动逻辑 (TLA+) 来定义和检查疾病症状要求.
- 对TLA+模型进行模型检查,以确保疾病检测属性的正确性.
- 深度学习模型,特别是卷积神经网络 (CNN),用于预测各种棉花疾病.
主要成果:
- TLA+模型检查方法确保了疾病症状检测的可靠性和正确性.
- 在预测棉花疾病方面,CNN模型实现了98.7%的高整体准确性.
- 在所有疾病类别中都获得了高F1分数,这表明了强大的表现 (例如,粉菌的0.90,军虫的0.87).
结论:
- 正式方法和深度学习的综合方法为检测棉花作物疾病提供了可靠的解决方案.
- 这种方法可以通过早期和准确的疾病识别,为可持续的棉花农业做出重大贡献.
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