Dangerous Farm Insects Detection Using Transfer Learning and CNN

In the ever-evolving landscape of machine learning, transfer learning has emerged as a powerful technique that leverages knowledge gained from one task to enhance the performance of another. This research paper delves into the realm of agricultural applications by employing the transfer learning technique on the “Dangerous Farm Insects Image Dataset.” This curated repository contains a diverse array of images depicting 15 distinct types of insects commonly found in agricultural environments, serving as a vital resource for understanding and studying these potentially destructive creatures. The focus of this study revolves around harnessing the pretrained knowledge of deep learning models and applying it to the task of classifying and identifying the various insect species within the dataset. By building upon the foundations laid by these models, we aim to optimize classification accuracy, reduce training time, and pave the way for real-world applications in pest management, crop protection, and beyond.