Description:Field of the Invention
The suggested innovation talks about combining artificial intelligence deep learning and image processing techniques to predict birds, analyze endangered species, and protect birds to maintain ecological balance. Our primary goal is to develop bird identification technology to preserve a bird species database as a gallery for future generations, as information about our ancestors' histories was previously only available in the form of books and papers. To provide this information to the next generation, however, we must now use technology.
OBJECTIVE OF THE INVENTION
The main goal of advancing the notion of bird species prediction Our model seeks to find new bird species and recognize those that are in danger of extinction. Machine learning algorithms have made it possible to identify bird species more effectively than ever before. In particular, by utilizing convolutional neural networks (CNNs), which are capable of finding patterns within huge data sets, resulting in improved outcomes over time. This novel strategy, which makes use of AI-powered technology, has the potential to significantly advance our understanding of avian ecology while enabling researchers to gather data on bird behavior. This would increase overall productivity levels, which would be advantageous to all parties involved—including the teams and individuals involved—and would ultimately lead to better results being achieved faster than anticipated.
Background of the Invention
According to recent research performed by the American Museum of Natural History, the planet we live on is home to close to 18,000 different bird species. birds that resemble one another or are believed to have interbred but are separate species. But due to observer limitations including location, distance, and equipment, identifying birds with the naked eye is based on fundamental physical traits, and appropriate based on different aspects is generally considered as tiresome classification. Bird classification can be performed manually by subject matter specialists, but the expanding volume of data makes the procedure time-consuming. Due to complex fluctuations and object fringes, the process of detecting object pieces later on is difficult.
The CNN method and deep residual neural networks were presented by John Martinsson et al. (2017) [1] to detect a picture in two ways, based on feature extraction and signal classification. They conducted an experimental investigation for datasets made up of various image types. They neglected to take into account the background species, though. Larger training data sets, which might not be available, are needed to identify the background species. Larger volumes of training data are necessary to identify the background species, however, these may not be available.
A convolutional neural network trained using deep learning techniques for image categorization was proposed by Juha Niemi, Juha T Tanttu, et al. (2018) [2]. It also suggested a technique for data augmentation in which photos are changed and rotated to match the desired color. Based on a combination of information provided by the radar and other sources, the final identification
Based on the study of picture attributes, Li Jian, Zhang Lei, et al. (2014) [3] suggested an efficient automatic bird species recognition method. Using the similarity comparison method and the library of common photos.
A software program created by Madhuri A. Tayal, Atharva Magrulkar, et al. (2018) [4] is used to streamline the identification of birds. This software for identifying birds accepts an image as input and outputs the bird's name. Transfer learning and MATLAB are the technologies employed for the identifying process.
In their 2013 paper, Andreia Marini, Jacques Facon, and colleagues proposed a novel method based on color features extracted from unconstrained images, using a color segmentation algorithm to separate candidate regions where the bird might be visible in the image. The number of bins in the histograms was reduced to a set number via aggregate processing. The CUB-200 dataset was used as a testing ground by the authors of this research, and the results indicate that this technique is more accurate.
The automatic identification of bird species from their audio-recorded song was the topic of Marcelo T. Lopes, Lucas L. Gioppo et al.'s 2011 study. By combining signal processing and machine learning methods with the MARSYAS feature set, the authors of this paper addressed the problem of bird species identification. presented a series of tests carried out on a database made up of the songs of 75 different bird species, where 12 species had performance issues.
Acoustic modeling for the identification of bird species from audio field recordings was studied by Peter Jancovic and Munevver Kokuer et al. in 2012, the creation of a hybrid deep neural network hidden Markov model (DNN-HMM). The created models were used for bird species identification, species detection, and identifying various bird species that were vocalizing in a given recording. In this study, the authors were able to identify objects with an accuracy of 98.7% and recognize objects with an accuracy of 97.3%.
Deep convolutional neural networks and data augmentation methods for audio-based bird species identification were introduced by Mario Lasseck et al. in 2013. The Xeno-Canto collection of bird species audio recordings was utilized by the author in this work.
Summary of the Invention
Numerous websites use various techniques to identify the different species of birds. But the outcomes are inaccurate. Let's say that when we enter information into those websites and Android applications, we get many answers rather than just the name of the bird. It displays all of the bird names that share comparable qualities. Therefore, our goal was to create a project that would result in better and more precise findings. We have classified the bird species using Convolutional Neural Networks to accomplish this.
Identification of bird species is essential for conservatism, ecological research, and monitoring of biodiversity. Automated approaches for identifying bird species have drawn considerable attention as a result of advances in machine learning. This literature review seeks to provide an overview of the current state of research in bird species detection using machine learning approaches, including the datasets, procedures, and performance metrics used in different investigations. To identify bird species, several datasets have been used. This has made it possible for researchers to efficiently train and test machine learning models. The most widely used dataset is the eBird dataset from the Cornell Lab of Ornithology, which contains large records of bird sightings and related metadata. Additionally, datasets from XenoCanto, BirdCLEF, and BirdNET have been used, each of which contains a variety of distinct bird species.
Brief Description of Drawings
The invention will be described in detail with reference to the exemplary embodiments shown in the figure wherein:
Figure 1: Diagrammatic representation of how identification and classification of bird species can be achieved

Detailed Description of the Invention
Although there are many ways for recognizing a bird through image capturing there are very a smaller number of ways to classify bird species and to get more details about the bird species. Many birds travel long distances during their hibernation period and migrate to different places around the world. For example, there are many lakes like kolleru lake, pullicat Lake that gives shelter to various bird species. Studies have shown that every year the number of birds that reach these lakes are being decreased and even some of the bird species are disappeared. The ornithologists are unable to find out the reason behind this sudden disappearance. So, using these CNN networks we can also store the characteristics of the bird species such as how it is adapting to the climatic and even weather conditions, what kind of food it consumes, and what measures to be taken to increase their population. We can monitor the bird species with more accuracy.
Sensing clues and Open acoustic devices are used to record the minute sounds and can pick up everything including birds chirping. In the back end, we can even identify the bird species and classify it based on the sound it makes.
The proposed model provides very important data like the species of bird travelling to the location, duration of which it stays and if it’s returning back to the same location again. This not only helps in determining the problems in the location but helps narrowing down on what makes the birds not return to the same location when they are supposed to.
The model's capacity to differentiate between several bird species is also revealed via confusion matrices and receiver operating characteristic (ROC) curves. Some research has also examined how well models can be applied to various geographical locations and seasons.
To create a model which provides the data necessary to help us find out why birds are not coming back, we follow:
Identification: We identify bird species by using audio and video sensors combined with numerous machine-learning techniques and methodologies. Convolutional neural networks (CNNs) are a common technique that has demonstrated impressive performance in image-based classification problems. Convolutional neural networks (CNNs)are a class of deep neural networks that are primarily employed in the analysis of visual images in deep learning.To identify different bird species, CNN-based architectures including ResNet, VGG, and Inception have been modified and improved.
Audio-based classification and bird sound analysis have also been done using other methods, such as support vector machines (SVMs), random forests, and hidden Markov models (HMMs). To obtain the most pertinent information from bird species data, features must be represented and extracted. Spectrogram representations, color histograms, and local binary patterns (LBPs) have all been used to capture the visual properties of birds in image-based methods.
Melfrequency cepstral coefficients (MFCCs), mel-spectrograms, and wavelet transform features have been employed in audio-based techniques to extract pertinent acoustic data. To increase the precision of bird species identification, hybrid techniques that integrate optical and auditory information have also been investigated. Various performance criteria are used to evaluate bird species detection programs. The categorization models’ effectiveness is frequently evaluated using accuracy, precision, recall, and F1-score.
Classification: After finding out the species of birds we need to get all the information regarding that bird species and this is achieved by sending the obtained data through “aiforbirds” software that gives all kinds of information of that particular species like their lifespan, areas they usually migrate to. Satellites and geolocators are some techniques to study the migration of different species of birds. We can find a sample of the software on https://www.aiforbirds.com website. This software matches the obtained data with the datasets present in its database to provide the most accurate prediction.
Obtaining data: Once we find out the species of birds and all the information related to them, we can narrow down the problems with the location (like sanctuaries, forests etc.) based on what the bird needs and if the location is offering those necessities or if the location is harming the bird in any way. This helps us understand what is actually going wrong in that location. As we know all the details of the detected bird, we will know what kind of environment the bird is likely to adapt and cherish. So, taking all of the factors into account, we can start finding out the problems faced by that particular species of birds in that particular location.
Once we narrow down the problems, we start working towards solving those problems which helps the birds return to the same location every time as they are supposed to. We do that by co-relating and cross-referencing the necessities of birds with what the location has to offer, this process will ensure in accurately predicting what the problems are. Using the same audio and video sensors, satellites and geolocators present in various locations. We can track the migratory patterns of different species of birds to obtain data on their endangered status. We get to know if the bird is endangered, invulnerable or extinct. This data can be used to make sure these birds stay safe by using our model. Once we have all this data, we can create an AI algorithm to automate most of what is happening to ensure less man power and have more accurate predictions by using different machine learning techniques. This
Advantages of the proposed model,
• Compiles information on birds that migrate to various locations, including whether or not they return to the same place.
• Less oversight by humans
• Information gathered for this project will be useful for future studies
• Users will learn enough about every species of bird that we have in our database.
• Our method is set up to make every common user understandable without any confusion regarding the description of the bird.
• Accurate justification is also given for the facts about extinct birds.
• The scope of this project expands significantly as it fulfills its objectives. This idea can be used in wildlife study and monitoring.
• Used in camera traps to preserve a record of wildlife behavior and movement in a particular habitat.
3 Claims & 1 Figure , Claims:The scope of the invention is defined by the following claims:
Claims:
1. The proposed invention comprising prediction of birds and analysis of endangered bird species,
a) Creating a fresh approach that is more effective than conventional survey techniques for gathering thorough information on bird behavior.
b) Allowing researchers to more accurately track changes in bird populations over time using real-time insights produced by integrated AI-powered technologies from many sources, including acoustic signals, images/videos, etc.
c) By offering cutting-edge technology that can be used to evaluate the effects of environmental factors on avian biodiversity, such as habitat loss or climate change, defensive efforts can be made to address the concerns highlighted by those who are concerned.
2. As per claim 1, AI algorithm suggests a suitable habitat for the survival of bird species, then the bird sanctuaries can be modified according to the AI suggestions. The methodology is not just confined to image recognition but also sound recognition.
3. As per claim 1, the new developments can maintain a record of the vast number of bird species including the rarest species that exist in the world in the dataset. The development of machine learning methods has created new opportunities for more accurate bird species detection than ever before.