A novel hybrid deep network for diagnosing water status in wheat crop using IoT-based multimodal data

Abstract

Automatic detection of plant water status is a significant challenge in agriculture as it is a crucial regulator of growth, productivity, quality, and sustainability. As a result, accurate monitoring of the plant's water condition has become imperative. Internet of Things (IoT) solutions based on specific sensor data acquisition and intelligent processing can assist water users for precise irrigation by providing accurate, consistent, and fast results. This paper aims to present a hybrid deep learning approach based on the combination of a convolutional neural network (CNN) and long short-term memory (LSTM) for automatically identifying the water state of wheat. The intended scheme used IoT-based data transmission devices such as a digital camera, soil moisture, wind speed, air temperature, and relative humidity. These environmental factors (EF) were recorded during the plant image capture. A total of 876 images of wheat plants were collected under different water deficit levels. A data augmentation approach was applied to expand the size of the training dataset to 5256 images. Various types of image color modes for example CMYK (cyan-magenta-yellow-black), HSV (hue-saturation-value), RGB (red-greenblue), and grayscale were evaluated with our proposed methods. The experimental results indicated that the combined CNNRGB-LSTMEF-CNNEF deep network based on features from both RGB images, climatic conditions, and soil moisture performed better than features from individual RGB images. Its outputs of validation accuracy, classification precision, recall, F-measure, and intersection over union are 100% with a loss of 0.0012. The proposed system behavior is very encouraging to develop our methodology with other crops in the future. The designed framework can serve the agricultural community to detect the water stress of plants before the critical level of growth and make timely management decisions.