Exploring artificial intelligence models for tea leaf disease diagnosis: a review
Year: 2026
Type: article
Abstract: 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.
Source: Discover Artificial Intelligence
Authors: Megha Gupta, Sunaina Garg, Nabamita Deb
Institutions: RIMT University, Gauhati University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $1,590
OpenAlex ID: W7160765416