A state-of-the-art aquaponics system-based organic farm producing lettuces, microgreens and other such produce was looking for a solution that would help them track their produce through different growth stages, shipping and inventory, and help them determine the optimal greenhouse settings and nutrient quantities required to produce maximum output.
Aquaponics is a growing technique that utilizes the abundant and natural nutrients discharged by fish to grow plants. Water from the fish tanks serves as organic fertilizer for the produce, while the plants’ roots clean the water before it is return back to the tanks.
This complex system has numerous parameters such as temperature, light settings, germination time, pH settings, etc. that impact the quality and output produced by the farm. So, in addition to tracking the produce they were looking for a machine learning-based technique to optimize these parameters for maximum throughput under various weather conditions.