Soil Moisture and Heat Level Prediction for Plant Health Monitoring Using Deep Learning with Gannet Namib Beetle Optimization in IoT

Abstract

Plant health monitoring is crucial in ensuring a constant food supply to satisfy the growing demand for food. Hence, it is essential to monitor plant health to maximize the yield and minimize the risk of various diseases. Soil moisture and temperature are of critical importance in plant growth, and predicting them enables farmers to take preventive actions, thereby mitigating the issues affecting plant health. This work presents a plant health monitoring approach by forecasting soil moisture and heat levels by collecting data in an Internet of Things (IoT) environment. Here, for transmitting the soil data acquired by the IoT nodes, a cluster head (CH) selection and routing technique using Gannet Namib beetle optimization (GNBO) is used. The data is routed to a prediction module, wherein soil moisture and heat levels are predicted by Convolutional long short term memory (Conv-LSTM). Furthermore, the hyperparameters of the Conv-LSTM are optimized by the GNBO algorithm. The efficiency of the GNBO-Conv-LSTM is examined based on link life time (LLT), energy, delay, distance, negative predictive value (NPV), positive predictive value (PPV), and true negative rate (TNR) and is observed to have achieved values of 0.675, 0.478 J, 0.092 ms, 50.200 m, 0.885, 0.882, and 0.875, correspondingly.

Keywords: Convolutional long short-term memory; Gannet optimization algorithm (GOA); Internet of Things; Mutual information; Namib beetle optimization (NBO).