Automated Insect Detection and Classification using Pelican Optimization Algorithm with Deep Learning on Internet of Enabled Agricultural Sector

aspg-31-3481-1791687015 20261011025015 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Advances in Applied Computational Intelligence IJAACI 2833-5600 2025 7 1 Automated Insect Detection and Classification using Pelican Optimization Algorithm with Deep Learning on Internet of Enabled Agricultural Sector Karla Zayood Online Islamic University, Department of Science and Information Technology, Doha, Qatar Rama Asad Nadweh Online Islamic University, Department of Science and Information Technology, Doha, Qatar Recently, the combination of Deep Learning (DL) methods within the Internet of Things (IoTs) has developed in the agricultural field, especially in the domain of pest management. This study considers the implementation and development of an innovative method for Insect Detection and Classification using DL within the environment of the IoTs in agriculture. The developed system advantages advanced DL approaches for analysing images captured by IoT-enabled devices, enabling real-time identification and categorization of insect pests. By continuously incorporating these technologies, these research goals to increase the efficiency and precision of pest monitoring, finally providing to sustainable agricultural technologies and increased crop yield. This study presents an Automated Insect Detection and Classification using Pelican Optimization Algorithm with Deep Learning (AIDC-POADL) technique on Internet of Enabled Agricultural Sector. The main objective of the AIDC-POADL system is to identify and categorize various types of insects exist in the agricultural field. In the primary stage, the AIDC-POADL technique involves DenseNet-121 model to learn complex features in the input images. Also, the hyperparameter choice of the DenseNet-121 algorithm developed by the POA. At last, multilayer perceptron (MLP) model can be applied to discriminate the insects into various classes. To validate the enhanced performance of the AIDC-POADL algorithm, a series of simulations are involved. The experimental outcomes stated that the AIDC-POADL technique offers enhanced recognition results over other approaches. 2025 50 62 3481 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJAACI.070104 https://www.americaspg.com/journal/31/article/3481 https://www.americaspg.com/storage/71737171166.pdf