Development of an Artificial Intelligence-Based Smart Greenhouse System to Optimize Vegetable Production
Keywords:
Artificial Intelligence , Smart Greenhouse IoT, Sustainable Agriculture , Vegetable ProductionAbstract
Purpose: Artificial Intelligence (AI) in agriculture has become a potential opportunity to enhance resource use and crop output, and especially in controlled-environment cropping. This paper introduces an intelligent, greenhouse prototype and its modelling and its testing in order to maximize the production of vegetable crops in the tropical world.
Subjects and Methods: It consists of the system that combines Internet of Things (IoT) with machine learning algorithms, including Random Forest and Artificial Neural Networks (ANN), engineered to control microclimatic factors including temperature and humidity, soil moisture, and intensity of light.
Results: An experimental work was implemented on a model crop lettuce wherein the prototypical greenhouse was piloted in a 4 x 6-meter of lettuce grown in a chamber system under a duration of 45 days. According to the findings, greenhouse managed by AI allowed the reduction of water consumption by 39.6 percent and energy consumption by 12.7 percent in comparison with the traditional control, and increased the fresh weight and the number of leaves in crops by 28.4 percent.
Conclusions: These results emphasize the usefulness of AI in the attainment of sustainable farming systems through increased productivity but low use of inputs. The paper comes to the conclusion that AI-based smart greenhouse systems show great potential to be adopted in tropical areas, but additional studies are necessary to verify scalability, crop variety, and economic viability.
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