International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023
p-ISSN: 2395-0072
www.irjet.net
CROP PROTECTION AGAINST BIRDS USING DEEP LEARNING AND IOT Akash Prajapati1, Utkarsh Sawant2, Harshal Ubhare3, Rasika Shintre4 1,2,3B.E. Student, professor Department of Computer Engineering 4Vice Principal, Smt. Indira Gandhi College of Engineering Navi Mumbai, Maharashtra, India
---------------------------------------------------------------------***--------------------------------------------------------------------systems, it is now possible to perform computer assisted Abstract: Air as an empty space is in many ways a commercial use. These make it free for all animals to use. But sometimes these terms are used by a handful of birds to hurt farmers, so work in this area to save crops. Visual and spatial perception is one of the most important applications of computer vision. This is a comparison of deep learning in the state. Bird detection is an important issue for many applications such as aviation safety, bird protection and the ecological science of migratory birds. In this study, a system has been developed to detect birds in high-definition video. Requirements to consider are Convolutional Neural Networks (CNN), background visualization, contour detection and classification confusion matrix. Findings include, but are not limited to, the following, using PCA in deep features not only reduces size and thus reduces training/testing time, but also improves recognition accuracy, especially when using neural network classifiers. Keywords: authentication, identification, image detection, biometrics, image recognition.
Keywords: Authentication,
Recognition, detection, Biometrics, Image recognisation.
Image
1. INTRODUCTION 1.1 Bird detection Bird detection is an important issue in a variety of applications, such as aviation safety, the ecological science of birds and migratory birds. Due to the increasing number of flying vehicles, bird detection plays an important role in protection from all kinds of dangers and threats. Thousands of bird strikes are reported each year, many of which result in takeoffs, engine stalls, and other negative consequences. According to the International Civil Aviation Organization (ICAO),there were more than 25,000 bird strikes reported by civil aviation between 1988 and 1992. Bird shooting is also a big problem for soldiers. In 2006, the US Air Force reported more than 5,000 bird strikes.
2. THE LITERATURE SURVEY One of the newest technologies is computerized automatic bird detection. According to Dominique Chatbot, bird studies are organized using aerial photographs and video rather than audiences. Even a short examination of the image takes a long time. Thanks to advances in digital cameras and image recognition © 2023, IRJET
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Impact Factor value: 8.226
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bird detection in the highest quality images. The visuals of the research methods were given and the data collected on this subject were evaluated. Birds focusing on a gray background are often affected by this perception, which requires a large visual field.
Some of the methods used to measure prey can be used for birds, but low resolution bird detection using thermal infrared imaging is generally somewhat used for large mammals. With continued advances in camera and drone technology, birdwatchers can reduce the time and resources spent watching birds by using automatic bird trackers. In other methods, according to Jeongjin Jo, the problem of collision between airplane and bird has been studied in different ways. Deep learning techniques are currently used in image recognition research. This article describes how to process images and capture birds in multiple dynamic environments using a convolutional neural network (CNN). Dynamic background is removed from body movement by prioritization and disease movement is isolated from it. The learning model was created based on the input data of the bird images in the background before processing. The authors hope to improve the accuracy of small objects using the Inception-v3 neural network model, history subtraction is a method for moving objects for viewing. Picardie examines and categorizes various ways to perform extraction based on speed, need and accuracy. Among the methods, a combination of Gaussian (MOG) and Kernel Density Estimation (KDE) works better. Model precision. Due to KDE's memory requirements, MOG is suitable for low memory devices. An improved background subtraction, which is a better model for both simple static and complex dynamic scenes. To identify background birds, true bird detection based on background subtraction is recommended. They capture the motion of the Gaussian Mixture Model (GMM). As mentioned earlier, MOG-based background subtraction is often used for motion detection. However, it shows some shortcomings when applied to dynamic backgrounds. Therefore, it has been proposed to use deep learning for bird detection for classification. ISO 9001:2008 Certified Journal
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