支持人工智能的巴里莱-博尔温-布林德-奥阿萨卡-伯努利深度分类器,用于增强作物产量预测.
Rajesh Kumar Dhanaraj1, Nithya Rekha Sivakumar2, Firoz Khan3
1Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University), Pune, India. sangeraje@gmail.com.
Scientific reports
|July 2, 2025
概括
这项研究引入了一种先进的AI深度学习分类器,用于精确预测作物产量,显著提高准确性并减少错误. 与传统方法相比,这种新方法提高了预测性能和效率.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 准确的作物产量预测对于粮食安全和农业规划至关重要.
- 现有的方法往往在准确性,灵敏性和特异性方面扎,导致错误的阳性和阴性.
- 先进的人工智能 (AI) 和深度学习为增强预测能力提供了潜在的解决方案.
研究的目的:
- 整合先进的AI深度学习方法,以准确预测作物产量.
- 为了提高作物产量预测模型的准确性,灵敏性和特异性.
- 在作物产量预测中尽量减少虚假阳性和虚假阴性病例.
主要方法:
- 开发了一种新型的人工智能支持的巴里莱-盲人-奥阿萨卡-伯努利深度分类器 (BBO-BDC).
- 预处理涉及Barilai-Borwein梯度最小-最大规范化来处理缺失的值.
- 特征选择使用了Blinder-Oaxaca统计分解方法.
- 用人工智能支持的伯努利深信网络进行了作物产量预测.
主要成果:
- BBO-BDC技术提高了准确度高达12%,特异性高达15%,灵敏度高达3%.
- 与传统方法相比,收速度 (29%) 和开销 (51%) 显著降低.
- 各种AI组件的集成在作物产量预测方面表现出卓越的性能.
结论:
- 拟议的BBO-BDC技术为准确的作物产量预测提供了强大而高效的解决方案.
- 人工智能支持的深度学习方法,结合先进的预处理和功能选择,显著提高预测性能.
- 这种方法有望通过精确的产量预测来改善农业管理和决策.
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