Introduction

Plantos aims to provide small and medium-sized agricultural operations with a user-friendly and low-cost approach to harnessing hardware, data, and AI for farming practices. Precision Agriculture Technologies (PATs) are key to enabling farmers with cost-effective and environmentally sustainable farming practices. With growing populations, finite resources, and dwindling margins, implementing efficient farming practices has become more relevant than ever.

From little things, big things grow.

Why Plantos?

With the global population projected to reach 10 billion by 2050, the farming industry faces a critical challenge. It must adapt significantly to meet the needs of this growing population while simultaneously minimizing environmental impact and maintaining financial viability. With the increase in agricultural output comes a plethora of new obstacles and new ways to solve them. PATs are revolutionizing how farmers can scale to meet population needs, making it easier to optimize inputs, detect diseases, and predict crop yields—all of which serve to reduce input costs and conserve resources.

PATs come with their own set of obstacles that make implementation difficult for a major portion of small to medium-sized farming operations. Primary barriers to entry among small farms include costs, ease of use, and reliability/range. Farmers who are already operating on minimal margins find it difficult to take risks on new technology that has an unproven track record. Accurate and reliable hardware tends to be quite cost-intensive and operationally complex, which is why PATs are historically more frequently deployed in large-scale farming practices. Large swaths of the farming population are missing out on the efficiencies that PATs can offer due to these barriers.

Finding Solutions in Data

Plantos provides an easy to use and low-cost remote sensor for optimizing agricultural inputs crops. The goal is to create a plug-and-play sensor that provides a smooth user experience from onboarding and collecting your first dataset to reading outputs and implementing optimizations.

Combination of two key tech concepts:

1. Remote sensing (RS)
2. Machine Learning (ML)

Internet of Things (IOT) devices provide us with a low-cost method for collecting data in the field and relaying it to a network for analysis. Where low-cost RS lacks in accuracy, ML will be able to standardize data and maintian data integrity. Then predictive models will be able to digest the input data and provide users with actionable information right to their phone or computer.