Improving Transparency within the Tea Industry

AgNext
AgNext
May 8, 2019 · 1 min read
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Tea is the staple beverage for India and accounts for the maximum consumption globally. The industry has grown exponentially which has made India the largest grower and producer in the world. Since 1947, the overall production has increased by ~250% and the land area used for production has increased by ~40%.

Within this voluminous industry, determining the “Fine Leaf Count” and grading the batch quality has been an issue prolonged. This was done manually over centuries, and lead to lot of biased owing to the manual interventions which further resulted in conflicts between the growers and the manufacturers. This raises the need for something that makes this huge revenue generating industry more transparent and upright, particularly for the growers.

Fine Leaf Count in Tea Industry — Prabhat Kamal Bezboruah

AgNext, in collaboration with The Tea Board of India and Tocklai, has brought in advanced technologies just about the right time, to enable the stakeholders to strategic and operational decision making by bettering the quality of the produce and cutting down week long assessment time to a couple of seconds. Further, it makes the industry more favorable for the growers.

Sudeep Vasu
Market Research Analyst at AgNext

AgNext World

AgNext is an AgTech company based in India working on the…

AgNext

Written by

AgNext

AgNext is an Agtech company based in India working on the intersection of hardware, software, & analytics for quality estimation in agriculture and food.

AgNext World

AgNext is an AgTech company based in India working on the intersection of hardware, software, and analytics for quality estimation in agriculture and food.

AgNext

Written by

AgNext

AgNext is an Agtech company based in India working on the intersection of hardware, software, & analytics for quality estimation in agriculture and food.

AgNext World

AgNext is an AgTech company based in India working on the intersection of hardware, software, and analytics for quality estimation in agriculture and food.

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