A Convolutional Neural Network Approach to Recognize the Insect: A Perspective in Bangladesh

Abstract

In Bangladesh huge amount of agricultural products are destroying by the pests every year due to lack of poor knowledge about pest detection. As we know that manually identification is difficult for a farmer. So, classic pest detection and identification can ensure excellent productivity. This would be a fulfil research in the technical area of computer vision. The dataset is typically random cropping of square size images together with grayscale color and brightness shifts are used here. Here Convolutional Neural Network (CNN) will be used to do the image recognition and the algorithm will provide an optimal architecture for image recognition. The big idea behind CNNs is that a local understanding of an image is good enough. The research contains the proportions of validation accuracy of 93.46%. This approach resulted in the agriculture sector that will help a farmer to recognize the insect from harvest. The computer vision and object recognition can be used with image processing to create an interactive and enlarge user experience of the real world. This research aims to demonstrate the possibility and test the performance of the project which only focuses on insect detection in crop plants that recognize the pest which can help a farmer to get immediate solution of harvest problem.