A Comprehensive Survey on IoT-Aided Pest Detection and Classification in Agriculture Using Different Image Processing Techniques

Abstract

Insect and rodents constantly cause trouble to the farmers leading to different kinds of diseases in the crop. Controlling as well as crop maintenance becomes a highly essential task for the farmers to ensure the health of the crop. However, they cause various social as well as environmental issues. Excessive pesticide usage may affect the contamination of soil and water, and also, it becomes highly toxic to plants. Hence, bugs and insects become more cautious against plants along with constant exposure, which pushes the farmer to utilize heavy pesticides. However, genetic seed manipulation is mainly used to provide high robustness against pest attacks, and they are highly expensive for practical execution. Implementation of the Internet-of-Things (IoT) in the agricultural domain has brought an enhanced improvement in on-field pest management. Several pest detections, as well as classification models, have been implemented in prior works, and they are based on effective techniques. The main purpose of this survey paper is to provide a literature review of IoT-aided pest detection and classification using different images. The datasets used in different pest detection and classification, the simulated platforms, and performance measures are analyzed. Further, the recent trends of machine learning and deep learning methods in this field are reviewed and categorized. Thus, the given survey impacts the economy for analyzing pest detection in the early stage, which provides better crop production, and also maximizes the protection of crops. Moreover, it helps to minimize human errors, and also it provides the best efforts to increase the automated monitoring system for large fields.