Pest Detection Using Improvised YOLO Architecture

Abstract

AbstractThe speedy and reliable classification of plant disease/pest is essential to preventing productivity loss and loss or diminished quantity of agricultural commodities. Machine learning methodology can be used to obtain the solution. Deep learning has achieved significant advancement in the development of image processing in modern years, greatly outperforming previous approaches. Researchers are very interested in understanding how to apply deep learning to swot plant and pests detection. Deep learning, which is extremely popular in image processing, has offered many innovative precision farming applications in recent decades. In this investigation, deep learning models are adapted to the task at hand using transfer learning and deep feature extraction approaches. The given work takes into account the used pre-trained deep models for feature extraction and fine-tuning RCNN (Region with Convolution Neural Network) and YOLO (You Only Look Once) are used to classify the features extracted by deep feature extraction. Improvised YOLO is used which has proven pest prediction of about 95%. The performance of current research is compared, and common datasets are introduced. This paper examines potential obstacles in real-world applications of deep learning-based plant disease and pest detection. Data from genuine infection and pest pictures is used in the investigations. For performance evaluation, the accuracy is computed and compared.KeywordsYOLOCNNRCNNPest