Farmdar is an agritech that uses AI and space technology to create crop insights at scale. These insights are not only highly accurate, but also designed to be actionable by agribusinesses across the world, to enable better decision-making through robust data and AI insights.

Farmdar helps agribusinesses, and agricultural organizations such as sugar mills, input companies like fertilizer, seed and crop protection businesses, and food companies, plan better and grow better by transforming satellite and field data into actionable intelligence.

Headquartered in Singapore, with offices in Thailand, Pakistan and Brazil. Farmdar operates globally, with teams and customer deployments across APAC, Latin America and Africa.

Farmdar combines multiple types of satellite data, weather data, soil data, field data, and proprietary AI models to create actionable crop intelligence at scale.

Farmdar consistently achieves 90-95% field-validated accuracy across crop identification, yield prediction, harvest monitoring, and weekly crop monitoring insights.

Yes. Farmdar's technology is built for scalable, multi-country deployment and has analyzed 500 million+ acres using AI and satellite-based analytics.

Farmdar combines AI, satellite technology, agronomy expertise, and prioritises customer success to deliver actionable intelligence at scale, rather than isolated data or tools.

Farmdar provides an enterprise solution and acts as a technology partner (and often training and capacity building partner) to leading agricultural businesses, enabling impact, digital transformation and AI adoption across operations.

Farmdar is on a mission to become the world's most trusted crop insights platform. How we think and execute is built on actionable innovation, customer-centricity, and excellence at scale, ensuring that AI insights are practical and usable in real-world operations.

To become the world's most trusted crop intelligence platform and enable better agricultural decisions through actionable innovation and data.

Improved crop productivity; Reduced input waste; Better supply visibility; Stronger farmer engagement; More confident decision-making.

After establishing contact with Farmdar through the many channels available, the first step is a use case assessment with Farmdar's Business Team. We understand that Farmdar's technology may be a new piece of an organization's digital transformation puzzle, and therefore, the focus in the early engagements is to make sure we understand the problem at hand, and ensure our solution really can address the problem. For us, real-world impact is the end goal. Next, there is the step of requirements gathering, and after a brief commercial exchange, Farmdar's globally trained Customer Success teams will onboard and lead the customer and their organization through the technology deployment process throughout the year. Our mutual success lies in ensuring that customer teams become fluent with our technology and platform, and customer specific training programs may also be rolled out.

Yes - enterprise-grade encryption, access control, and governance.

Defined through client agreements.

CropScan - Large-scale crop intelligence; YieldPro - Field-level crop monitoring; AgriChain - Digitization of agricultural operations.

CropScan provides large-scale crop intelligence, helping agribusinesses optimize planning, pricing, procurement, and supply chain decisions through accurate crop data.

YieldPro enables field-level crop monitoring, helping farmers and agribusinesses optimize inputs, increase yields, and reduce waste through precise, data-driven insights.

AgriChain helps agribusinesses digitize farming operations and enables financial institutions to provide fair financing to farmers through structured field and operational data.

CropScan delivers large-scale crop data including hectarage estimation for multiple crops simultaneously, yield prediction, sowing time analysis, harvest monitoring, detecting water bodies, barren land, available arable land and historical comparisons.

YieldPro enables field-level monitoring through historical productivity zone mapping, germination monitoring, plant health and stress monitoring, nitrogen uptake reporting, irrigation scheduling and soil organic matter comparison.

AgriChain digitizes farm operations through field data logging, boundary mapping, workflows, approvals, and media uploads, supporting structured farm management. The Firebird module effectively delineates fields and creates digital boundaries using AI with a very high accuracy of 98% and can be deployed across vast areas.

Farmdar uses a combination of proprietary machine learning and deep learning models trained on millions of ground data points, satellite data from +200 constellations, weather, soil, and field data to generate crop intelligence.

Farmdar's AI provides insights such as: Crop identification and classification; Yield prediction; Sowing and harvest detection; Plant stress and nitrogen analysis; Productivity zones and irrigation requirements.

Farmdar provides end-to-end crop intelligence - from understanding where crops are and how they are performing, to helping organizations plan, monitor, and act with clarity. Our platform converts satellite, weather, soil, and field signals into practical insights that improve decision-making across agriculture.

Farmdar helps organizations clearly understand what is growing, where it is growing, and how it is changing over time. Capabilities include: Crop type identification and classification; Crop acreage / hectarage estimation at scale; Digital field boundary mapping and field identification; Detection of missing, newly cultivated, or abandoned fields; Identification of arable land, barren land, and water bodies; Landscape-level crop distribution visibility.

Farmdar tracks crop development throughout the season, helping teams understand crop progress and identify issues early. Sowing date and planting window detection; Germination and early crop establishment monitoring; Crop stage tracking across the full crop cycle; Growth consistency and uneven crop development detection; Harvest readiness and harvest detection; Multi-season crop performance comparison.

Farmdar identifies early signs of crop stress before they become visible on the ground, enabling timely interventions. Early plant stress detection; Crop health monitoring across the season; Detection of abnormal growth patterns; Vegetation health tracking (NDVI and related indicators); Identification of weak, stressed, or declining crop zones; Detection of uneven crop development within fields.

Farmdar helps agronomy and field teams act with precision and focus. Field prioritization for agronomy teams; Early identification of intervention opportunities; Detection of underperforming zones for targeted action; Input optimization guidance (fertilizer, irrigation, field focus); Crop performance trend analysis over time; Support for structured, data-led agronomic workflows.

Improves planning, forecasting, pricing, and supply reliability through crop intelligence.

Improves cane supply forecasting, crop visibility, productivity monitoring, and procurement planning.

Enables crop risk assessment, acreage validation, monitoring, and data-driven financing decisions.

Helps target interventions, improve farmer outcomes, and optimize product deployment.

Supports food security, crop monitoring, and agricultural policy decisions.

By combining satellite data, weather data, soil data, and field intelligence using proprietary AI models.

90-95% field-validated accuracy across crop identification, yield prediction, and crop monitoring.

Over 500 million acres globally.

No. Farmdar works primarily using satellite and AI-based intelligence.

Based on geography, acreage, modules selected, data frequency, and customization.

Yes - pilots are often used to validate use-case impact.