Development of an Artificial Intelligence-Based Smart Greenhouse System to Optimize Vegetable Production
Keywords:
Smart Greenhouse, Artificial Intelligence, Lettuce Growth, Environmental Efficiency, Vegetable ProductionAbstract
Purpose: This study aims to assess the effesctiveness of an Artificial Intelligence (AI)-based smart greenhouse system in improving vegetable growth and yield, particularly lettuce, compared to conventional greenhouse methods. The primary focus of the study is to evaluate the system's effect on microenvironmental stability, vegetative growth, and crop yield.
Subjects and Methods: The research subjects were lettuce plants cultivated for eight weeks in two treatments: an AI-based smart greenhouse and a conventional greenhouse. The smart greenhouse system uses temperature, humidity, and light sensors, as well as AI algorithms to automatically regulate environmental and nutritional conditions. Observed parameters included average temperature and humidity, plant height, leaf number, and wet and dry harvest weights. Data were analyzed descriptively and comparatively by calculating the average, standard deviation, and percentage increase between treatments.
Results: The results showed that the smart greenhouse was able to maintain a more stable temperature (25.8–26.2 °C) compared to the conventional greenhouse (28.3–29.4 °C) and higher humidity (68–69% vs. 58–60%). Lettuce growth was also more optimal, indicated by an average height of 36.2 cm and a leaf count of 29.2 in the smart greenhouse, higher than the conventional greenhouse (28.7 cm and 23.8 leaves). Harvest weight in the smart greenhouse reached 325.7 g (wet) and 47.8 g (dry), higher than the conventional greenhouse (245.3 g and 36.4 g).
Conclusions: AI-based smart greenhouse systems have proven more effective in optimizing vegetable growth and yields. This technology can be a strategic solution for increasing agricultural productivity in a sustainable, efficient, and environmentally friendly manner.
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