FloraMood: Enhancing Plant Growth Through Automation and User Emotional Feedback

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Authors: Iuliana Marin, Ana-Maria Comeagă, Ioannis Kotrotsios
Cited by: 0
References: 41

Abstract

Cultivating plants can be challenging due to the subtle and varied needs of different species. The proposed smart pot offers an efficient solution by automating key maintenance tasks, including watering, light exposure, and soil moisture management. Ideal for individuals with busy lives or limited plant care experience, the smart pot also supports sustainable practices. It features an interactive display that conveys the plant’s needs through messages and emotions, making care intuitive. An automatic watering system activates only when soil moisture falls below a set threshold, maintaining optimal conditions while minimizing water waste. To evaluate its performance, data on temperature, humidity, and light intensities (natural, blue, green, red, and white) were collected over six months, along with soil moisture, temperature, and light sensor data, which an embedded controller automatically processed. The dataset was refined using Python’s Pandas library to ensure statistical analysis accuracy. Key metrics such as mean, standard deviation, and correlation coefficients revealed significant patterns. For instance, temperature showed a positive correlation with red light intensity and an inverse correlation with white light intensity in roses. These relationships varied in rice, highlighting the system’s adaptability to different environmental and experimental conditions. Correlation matrices visualized complex interactions, such as competition between natural and blue light intensities in roses, which were less pronounced in rice. Overall, roses showed up to a 70% reduction in flower production under heat stress, and automated irrigation maintained soil humidity within the 40-70% optimal range, validating system efficiency. These insights underscore the smart pot’s ability to support diverse plant species while promoting sustainability. The system’s effectiveness in optimizing plant growth lays a strong foundation for advancements in interactive gardening technology.

Fields of Study & Subjects

Smart Agriculture and AI Plant and Biological Electrophysiology Studies Greenhouse Technology and Climate Control

Publication details

Journal / source
IEEE Access
Volume
14
Pages
3577–3592
Publication date
1 January 2026