Scholarlink

<p>Tea is a popular beverage worldwide. It is made from the tea plant, <i>Camellia sinensis</i>. Tea leaf diseases cause significant damage to the leaf quality, thereby affecting tea production. Therefore, prompt and precise detection of infections is essential to improve yields, prevent their spread from one region to another, and minimize financial and economic losses. Methods, including manual observation and image-based, are used for disease detection. Manual observation takes time for analysis, requires human assistance, and carries a risk of human error. Image-based recognition, using Machine Learning (ML) and Deep Learning (DL) techniques, delivers fast, accurate results. In this paper, we discussed various ML and DL image-based techniques introduced in previous research papers on tea leaf disease detection. In future research, this summary will make a positive contribution to the agriculture industry.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Exploring artificial intelligence models for tea leaf disease diagnosis: a review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月10日

查看全文
  • Megha Gupta,
  • Sunaina Garg,
  • Nabamita Deb

摘要

Tea is a popular beverage worldwide. It is made from the tea plant, Camellia sinensis. Tea leaf diseases cause significant damage to the leaf quality, thereby affecting tea production. Therefore, prompt and precise detection of infections is essential to improve yields, prevent their spread from one region to another, and minimize financial and economic losses. Methods, including manual observation and image-based, are used for disease detection. Manual observation takes time for analysis, requires human assistance, and carries a risk of human error. Image-based recognition, using Machine Learning (ML) and Deep Learning (DL) techniques, delivers fast, accurate results. In this paper, we discussed various ML and DL image-based techniques introduced in previous research papers on tea leaf disease detection. In future research, this summary will make a positive contribution to the agriculture industry.