AICU

Technology is booming in this era. Agriculture cannot be left behind. We believe AI should be able to help human society in broader sense. With expertise of real tomato growing experience, plant science (greenhouse horticulture, plant physiology and crop modeling), Algorithm/Software Engineering, Applied Mathematics, Data science, Computer science and AI, our team hopes to improve the efficiency in greenhouse production and find a sustainable way to feed the world.

Our principle of decision-making is based on the real greenhouse growing experience, model simulation result and AI deep learning outcome. By using historical weather data for the period of real challenge, we will first simulate a reasonable crop strategy among the production period. Then we apply the climate and irrigation strategy automatically for the first two or three month according to the combination of weather forecast and expert experience. When after the first few trusses of tomato are harvested, the AI may receive enough data feedback from all source and start to generate outcome. The outcome of AI will be evaluated by human tomato expert and tested by tomato growth model before it validates to the real greenhouse control.

From the experience of previous challenge, we noticed the feedback of plant growth for once a week was too slow. Sensors that may reflect the plant growth dynamically will definitely help with the AI deep learning. We may apply several sensors that related to the plant fresh weight, leaf temperature and micro climate within different part of the plant.