深度学习和基于内容的过技术,以改进植物疾病识别和治疗建议:全面审查
Folasade Olubusola Isinkaye1, Michael Olusoji Olusanya1, Pramod Kumar Singh2
1Department of Computer Science and Information Technology, Sol Plaatje University Kimberley, 8301, South Africa.
Heliyon
|May 13, 2024
概括
准确的植物疾病鉴定对于粮食安全至关重要. 将深度学习与基于内容的过集成为可持续农业提供了高效的检测和处理策略.
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:
- 植物病严重影响作物生产和全球粮食安全.
- 传统的植物疾病识别方法面临着许多挑战.
- 有效的疾病管理对于可持续的农业和健康的产量至关重要.
研究的目的:
- 调查植物疾病识别的当前研究格局.
- 确定当前知识和方法的现有差距和局限性.
- 建议未来的研究方向,以改善植物疾病缓解.
主要方法:
- 审查关于检测植物疾病的现有文献.
- 分析深度学习技术的整合.
- 探索基于内容的过在农业中的应用.
主要成果:
- 强调先进技术在疾病识别方面的潜力.
- 确定对更高效,更准确的检测系统的需求.
- 意识到结合深度学习和基于内容的过的好处.
结论:
- 像深度学习和基于内容的过等先进技术可以彻底改变植物疾病管理.
- 综合方法承诺更快,更准确的疾病识别和治疗建议.
- 需要进一步的研究才能充分利用这些技术来实现可持续的粮食生产.
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