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Case Study: Eden Grow

The Challenge

A U.S. based agriculture irrigation solutions provider who specializes in custom solutions for medical and recreational cultivation businesses. Optimal crop yield is highly dependent on frequent monitoring and measuring of growth rate responses to environmental conditions. Based on plant growth rates and site inspection results, manual adjustments to the irrigation and feeding systems are made throughout the growing cycle. The observation process and resulting environmental adjustments are labor intensive, time consuming and very subjective.

The Plan

Assemble a team of Unicorns who have the necessary hardware and software technology background combined with years of experience in organic, hydroponic and aquaponic Agrotech solutions. Challenge the team to design and build an agriculture technology solution that maximizes crop yield while reducing the high operational overhead costs associated with frequent onsite agriculture inspections.

The Solution

Cecropia’s engineers developed the industry’s most sophisticated multi-zone intelligent plant irrigation and feeding system. The system architecture includes capabilities that span IoT, Machine Learning, and Embedded Development disciplines. The user portal allows for configuring multiple zones to operate independently with different feeding schedules and recipes that support multiple crop types at different stages of the plant growing cycle. The recipes and feeding rates are automatically collected and analyzed through a plant growth data model that continuously learns the optimal combinations of water, minerals, frequency and duration of feeding times for the different strains of plants. The results are fed back into the automated scheduling and control system creating a continuous feedback loop that optimizes future plant feeding cycles.

The Multiplied Result

By eliminating the ongoing requirement of labor-intensive manual irrigation control system adjustments, the solution reduces operational labor expenses by up to 40 percent. The use of a machine learning data model to perform comparative analysis of feeding formulas versus growth rate and potency now ensures a consistent crop yield of the highest quality.