Identifying the Source of Water on Plant Using the Leaf Wetness Sensor and via Deep Learning-Based Ensemble Method

Abstract

Plant disease detection and management is one of the pivotal areas in the agriculture sector, which needs attention to abate crop loss. The recent trends in machine learning and deep learning have played a significant role in reducing crop loss with the help of early plant disease detection. For plant disease detection prior information on soil moisture, ambient temperature, relative humidity, leaf wetness sensor (LWS), rainfall are crucial parameters. In this work, the objective is to identify the source of leaf wetness on the leaf canopy, which can arise due to irrigation, rainfall, or dew. To identify the source of wetness on the leaf canopy, either rainfall or humidity/mist sensors are used, which substantially increases the cost of the system. For this purpose, we have used the LWS, which is deployed in the field and various patterns for the irrigation, rainfall, or dew has been analyzed by using the in-house developed the Internet of Things (IoT)-enabled sensor system. The data collected from the field is used as a learning dataset for the proposed ensemble neural network (NN) developed to identify the source of leaf wetness. Short-time Fourier transform (STFT) has been employed to enhance data representation by transforming numerical data from the LWS into informative images. The provided ensemble model incorporates convolutional NN (CNN) and multilayer perceptron (MLP), which process image and numerical data (ambient temperature, relative humidity, leaf wetness duration, and maximum magnitude of frequency of images) as input. Their outputs combined in an artificial neural network (ANN) sub-model for precise leaf wetness event detection (dew, rainfall, or irrigation). The proposed model achieved an accuracy of 96.13% with average precision, recall, and F1 score for the leaf wetness events is about 84%, 85%, and 83%, respectively.