Autonomous Nutrient Controller System for Hydroponic Honey Melon Based on the Integration of Artificial Intelligence Algorithms According to Planting Time
Keywords:
Artificial Intelligence, Convolutional Neural Network (CNN), Fuzzy Logic, Hydroponic, Nutrient ControllingAbstract
The honeydew melon cultivation model using the hydroponic greenhouse method has been widely applied due to its ease in controlling nutrients and the environment. However, complaints from farmers regarding the inaccuracy of nutrient levels and the dynamic environmental changes, that hinder plant growth and fruit quality, have surfaced. The development of autonomous control technology is crucial as a strategic solution to this issue since the quality of honeydew melon management lies in achieving precise and accurate nutrient levels. On the other hand, managing standardized nutrient composition often becomes a challenge for farmers as the needs constantly change over time. Conventional systems are not yet capable of accurately measuring nutrient levels in line with the plant’s growth stages. According to the objectives of this study, which is to improve the productivity and quality of honeydew melons based on the increase in the sweetness index, the development of an autonomous nutrient control system is proposed. This system integrates artificial intelligence algorithms, namely CNN and Fuzzy Logic, to process plant height image data and multisensor data for system control processes. The research findings that applying this integrated technique has resulted in a sweetness increase of 11.7%, or from the previous value of 15 brix to 17 brix. Even a one-point increase in the brix value leads to a sugar increase of 1 gram per 100 gram of liquid content in the fruit, contributing significantly to the market value. These results indicate that AI-supported agricultural management can be realized in future modern farming practices.
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