Prediction of Timing and Amount of Houseplants Watering by an Echo State Network on Jetson
Wataru Yoshimura, Koshun Arimura, Ryohei Kobayashi, Akinobu Mizutani, Tomoaki Fujino, Yuichiro Tanaka, Tomomi Sudo, Naoto Ishizuka, Keitaro Ito, Hakaru Tamukoh · Proceedings of International Conference on Artificial Life and Robotics · 2025
Cultivating of houseplants in biophilic designed spaces requires appropriate timing and amount of watering.However, determining them is challenging, as fluctuations in ambient temperature can influence these factors.We develop a system capable of predicting ambient temperature changes and determining the appropriate timing and amount of watering.The system acquires ambient data using sensors connected to a Jetson Nano and processes the data using a neural network for the prediction and determination.We adopt an echo state network, a lightweight neural network, enabling a power-efficient system capable of running on edge devices.Additionally, we implement a function to notify the user of the timing and amount of watering via a chat service whenever the soil moisture content drops below a predefined threshold.