IoT-Fog-enabled robotics-based robust classification of hazy and normal season agricultural images for weed detection

Abstract

Abstract The mechanization of farming is currently the most pressing problem facing humanity and a burgeoning academic field. Over the last decade, there has been an explosion of Internet of Things (IoT) application growth in agriculture. Agricultural robotics is bringing about a new era of farming because they are growing more intelligent, recognizing causes of variation on the farm, consuming fewer resources, and optimizing their efficiency to more flexible jobs. The purpose of this article is to construct an IoT-Fog computing equipped robotic system for the categorization of weeds and soy plants during both the hazy season and the normal season. The used dataset in this article included four classes: soil, soybean, grass, and weeds. A two-dimensional Convolutional Neural Network (2D-CNN)-based deep learning (DL) approach was implemented for data image classification with dataset of height and width of 150 × 150 and of three channels. The overall proposed system is considered an IoT-connected robotic device that is capable of applying classification through the Internet-connected server. The reliability of the device is also enhanced as it is enabled with edge-based Fog computing. Hence, the proposed robotic system is capable of applying DL classification through IoT as well as Fog computing architecture. The analysis of the proposed system was conducted in steps including training and testing of CNN for classification, validation of normal images, validation of hazy images, application of dehazing technique, and at the end validation of dehazed images. The training and validation parameters ensure 97% accuracy in classifying weeds and crops in a hazy environment. Finally, it concludes that applying the dehazing technique before identifying soy crops in adverse weather will help achieve a higher classification score.