Agri-tech firm Fasal has raised USD 1.6 million (over Rs 11 crore) from various investors, including Omnivore and Wavemaker, for business expansion.

Founded in 2018 by Ananda Prakash Verma and Shailendra Tiwari, Fasal is an artificial intelligence-powered technology platform for horticulture, which captures real-time data on growing conditions from on-farm sensors and delivers farm-specific, crop-specific actionable advisories to farmers via mobile in vernacular languages.

Fasal has raised USD 1.6 million from Omnivore and Wavemaker Partners. Other investors participating in the round include Mount Parker Ventures and Animoca from Hong Kong, as well as Mistletoe from Japan, via their Gastrotope accelerator, the company said in a statement.

Existing investors Zeroth and Artesian Ventures from Australia also participated in the round, it added.

Fasal will use the funding from its seed round to build artificial intelligence (AI) capabilities for each horticulture value chain where the company is focused. It also plans to launch a lower-cost and instantly installable version of its field sensor array shortly.

The company will scale up its business-to-business-to-farmer sales force, which will partner with aggregators, exporters, and processors in each horticulture value chain.

Fasal is currently working in crops like grapes, pomegranate, mango, citrus, tea, coffee, and chili. It is presently operating in Maharashtra, Karnataka, Chhattisgarh, Madhya Pradesh, Tamil Nadu, and Andhra Pradesh, but plans to expand pan-India and enter select markets across Southeast Asia.

“We are on a mission to help horticulture farmers make data-driven, logical decisions and shift farming to autopilot mode. Farmers using Fasal are seeing major increases in profitability and huge water savings, even in the driest parts of Maharashtra. Ultimately, we will become a full-stack platform for horticulture farmers,” said Ananda Prakash Verma, co-founder and CEO of Fasal.

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Fasal’s field sensor array can be installed by farmers in less than 15 minutes and measures multiple dynamic variables, including micro-climate, soil, and crop conditions.

It leverages machine learning to transform this field sensor data into farm-level predictions, anticipating various risks while helping horticulture farmers to reduce input costs by optimizing crop protection, irrigation, and crop nutrition.

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