智能农业:利用机器学习和深度学习来识别农作物的干旱压力
Tariq Ali1, Saif Ur Rehman1, Shamshair Ali1
1University Institute of Information Technology, PMAS-Arid Agriculture University, Rawalpindi, Pakistan.
Scientific reports
|December 3, 2024
概括
人工智能 (AI) 显著推进了植物减压,以实现可持续农业. 机器学习模型,特别是长期短期记忆 (LSTM),准确地识别作物压力,帮助全球粮食安全.
科学领域:
- 农业科学 农业科学
- 计算生物学 计算生物学
- 植物生理学 植物生理学
背景情况:
- 植物压力,特别是干旱,对农业和全球粮食安全构成重大威胁.
- 了解植物对压力的生理反应对于开发缓解策略至关重要.
- 人工智能 (AI) 提供了新的方法来分析复杂的生物数据和预测压力事件.
研究的目的:
- 研究机器学习和深度学习算法的应用,用于检测和分类植物应激反应.
- 分析特定蛋白质域 (TYRKC,RBR-E3) 在植物应激信号中的作用.
- 评估各种人工智能模型在识别作物压力的准确性和可靠性.
主要方法:
- 利用来自UniProt和SMART数据库的数据来检测作物生化特性和信号蛋白域.
- 应用机器学习 (支持向量机,梯度提升) 和深度学习 (循环神经网络,LSTM) 算法.
- 执行严格的度量评估和消去分析以评估算法性能.
主要成果:
- 长期短期记忆 (LSTM) 在压力事件分类中实现了97%的准确性,梯度增强96%,循环神经网络 (RNN) 94%.
- 支持矢量机 (SVM) 显示了82%的准确性.
- 该研究证实了AI在识别植物对压力的生理反应方面的有效性.
结论:
- 人工智能,特别是像LSTM和渐变增强这样的先进算法,显示出在农业中准确分类植物应激的巨大潜力.
- 这些发现支持人工智能的整合,以提高资源效率和精准农业.
- 克服人工智能采用的挑战需要跨学科的合作,以推进可持续农业和粮食安全.
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