Abstract
Water plays an important role in our daily lives and is an essential component of agriculture. One of the main difficulties faced by people is that they do not know when to water the plants and also the amount of water to be poured, resulting in the death of the plant. Current solutions to this problem focus on automatically watering the plants when the soil gets dry or drip irrigation-based solution where the plants are watered continuously. But this solution does not help people whose hobby is gardening and for those who want to take care of their plants by themselves. To tackle this problem, a system is proposed where the time is used to predict when a plant has to be watered in future. Our research is mainly on the deep learning (DL) algorithms, to analyze which would give better performance in this scenario. To achieve this, various sensors to obtain the moisture level of the soil, temperature, and humidity of the air around the plant. The cloud technologies and deep learning techniques are used to store, process, and obtain the optimum time. Since the data received is time-series data, we use LSTM and GRU models to predict the optimum time to water the plants. The results and their comparison are discussed in this article. This value is then sent to the user through the mobile app. This solution will help people to enjoy gardening as they need not worry about their plant’s health.
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Srithar, V.K., Vishal Vinod, K., Mona Sweata, S.K., Karthika Gurubarani, M., Abirami, K. (2021). A Smart Plant Watering System for Indoor Plants with Optimum Time Prediction for Watering. In: Suma, V., Chen, J.IZ., Baig, Z., Wang, H. (eds) Inventive Systems and Control. Lecture Notes in Networks and Systems, vol 204. Springer, Singapore. https://doi.org/10.1007/978-981-16-1395-1_34
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