Automated Pest Detection in Crop Fields Using Deep Learning Techniques

When it comes to discovering and eliminating pests, traditional methods are typically laborious, time-consuming, and hazardous to the environment. In recent years, deep literacy methods have emerged as interesting and potentially useful technologies for automating the finding of pests in agricultural fields. The purpose of this work is to provide a detailed evaluation of the operation of deep literacy methods in automated pest discovery. These methods include a variety of characteristics such as image processing, point birth, and bracket algorithms. In this paper, we discuss the difficulties and opportunities that are involved with the implementation of deep literacy-based pest discovery systems. These problems and opportunities include dataset reflection, model training, and scalability. In addition to this, we highlight recent developments, case stories, and new exploration directions in this rapidly developing field. Automated pest discovery systems have the potential to enhance agricultural operations by utilizing the powers of deep literacy. This would allow for the prompt and precise identification of pests, which would be necessary for the development of effective pest management techniques. Cutting-edge technology Within the scope of this investigation, Yolov3-Tiny, Yolov4, Yolov4-Tiny, Yolov6, and Yolov8 were utilized for detection. For object detection, each of these models was selected. Images have been marked with Yolo. Yolov8 outperformed previous trials by having the highest mAP of 84.7% and an average loss of 0.7939 for the detection of tiny pests. An Android application that offered real-time insect detection utilized the Yolov8 paradigm. The purpose of this research is to develop and deploy novel deep-learning models, analytical tools, and a procedure for identifying crop pests for pest management.