Improved Artificial Ecosystem Optimizer with Deep-Learning-Based Insect Detection and Classification for Agricultural Sector

Abstract

The agricultural industry has the potential to meet the increasing food production requirements and supply nutritious and healthy food products. Since the Internet of Things (IoT) phenomenon has achieved considerable growth in recent years, IoT-based systems have been established for pest detection so as to mitigate the loss of crops and reduce serious damage by employing pesticides. In the event of pest attack, the detection of crop insects is a tedious process for farmers since a considerable proportion of crop yield is affected and the quality of pest detection is diminished. Based on morphological features, conventional insect detection is an option, although the process has a disadvantage, i.e., it necessitates highly trained taxonomists to accurately recognize the insects. In recent times, automated detection of insect categories has become a complex problem and has gained considerable interest since it is mainly carried out by agriculture specialists. Advanced technologies in deep learning (DL) and machine learning (ML) domains have effectively reached optimum performance in terms of pest detection and classification. Therefore, the current research article focuses on the design of the improved artificial-ecosystem-based optimizer with deep-learning-based insect detection and classification (IAEODL-IDC) technique in IoT-based agricultural sector. The purpose of the proposed IAEODL-IDC technique lies in the effectual identification and classification of different types of insects. In order to accomplish this objective, IoT-based sensors are used to capture the images from the agricultural environment. In addition to this, the proposed IAEODL-IDC method applies the median filtering (MF)-based noise removal process. The IAEODL-IDC technique uses the MobileNetv2 approach as well as for feature vector generation. The IAEO system is utilized for optimal hyperparameter tuning of the MobileNetv2 approach. Furthermore, the gated recurrent unit (GRU) methodology is exploited for effective recognition and classification of insects. An extensive range of simulations were conducted to exhibit the improved performance of the proposed IAEODL-IDC methodology. The simulation results validated the remarkable results of the IAEODL-IDC algorithm with recent systems.