  The purpose of the Insect Detection System for Crop and Plant Health is to
keep an eye out for and identify insect infestations in farming areas. By
utilizing cutting-edge technology like computer vision and machine learning,
the system seeks to identify hazardous insects early and accurately. This would
enable prompt response to save crops and maintain optimal plant health. The
Method of this study includes Data Acquisition, Preprocessing, Data splitting,
Model Implementation and Model evaluation. Different models like MobileNetV2,
ResNet152V2, Xecption, Custom CNN was used in this study. In order to
categorize insect photos, a Convolutional Neural Network (CNN) based on the
ResNet152V2 architecture is constructed and evaluated in this work. Achieving
99% training accuracy and 97% testing accuracy, ResNet152V2 demonstrates
superior performance among four implemented models. The results highlight its
potential for real-world applications in insect classification and entomology
studies, emphasizing efficiency and accuracy. To ensure food security and
sustain agricultural output globally, finding insects is crucial. Cutting-edge
technology, such as ResNet152V2 models, greatly influence automating and
improving the accuracy of insect identification. Efficient insect detection not
only minimizes crop losses but also enhances agricultural productivity,
contributing to sustainable food production. This underscores the pivotal role
of technology in addressing challenges related to global food security.
