Advanced Convolutional Neural Network System for Automated Deer Species Identification and Classification

Abstract

For ecological research and conservation, wildlife species identification and classification are essential. Conventional manual techniques require a lot of work and are prone to errors. Automated species identification is now possible because to recent developments in machine learning and image processing. A convolutional neural network (CNN)-based method for automatic deer species identification is presented in this paper. In order to gather a dataset of high-resolution deer photos— 800 for training and 200 for testing—we first build a Python-based web crawler. Preprocessing is applied to the photos to ensure consistency. The CNN model is trained to discriminate between different kinds of deer and other similar animals. It is well-known for its effectiveness in image classification. The model outperforms conventional techniques in terms of effectiveness and accuracy as demonstrated by performance criteria like precision, recall, and F1 score. This creative method provides a useful resource for scientists studying wildlife, environmental agencies.

Specification

Description:The proposed patent mainly focusing on wildlife monitoring and conservation, specifically for automatically identifying deer species from digital images using machine learning and image recognition technologies. Traditional methods involve manual identification, which is labour-intensive, prone to human error, and inefficient for large-scale monitoring. Using a Convolutional Neural Network (CNN) model trained on a large dataset of deer images. The system comprises a web-based image collection tool, an image preprocessing module, and a trained CNN model capable of classifying deer species with high accuracy.
Detailed Description

1. Image Collection:
 A Python-based web crawler is used to collect a diverse dataset of deer images from various sources. These images are then labelled according to species for training the model.
2. Image Preprocessing:
 The collected images undergo preprocessing steps such as resizing, normalization, and data augmentation to ensure they are suitable for training the CNN model.
3. Model Training:
 A Convolutional Neural Network (CNN) architecture is designed and trained on the pre-processed dataset. The model is optimized for accuracy in classifying different deer species, with performance validated using standard metrics such as accuracy, precision, recall, and F1-score.
4. System Deployment:
 The trained model is integrated into a web-based application, allowing users to upload deer images for real-time identification. The system can be used in field applications, research studies, or integrated into larger wildlife monitoring systems.
5. Wildlife Expert's
 The expertise of wildlife professionals is vital for correctly labelling species in the training data. Their deep understanding of species characteristics, habitats, and behaviours enhances the reliability of the model.
 Continuous feedback from these experts helps refine the model, contributing to higher precision and fewer misclassifications.
6. End User's
 The system provides quick, real-time identification of species, allowing end users to make timely decisions in the field.
 With the web-based interface, end users can easily upload deer images and obtain species identifications
 The automated system can handle large volumes of data, making it an ideal tool for large-scale wildlife monitoring projects.
, Claims:We Claim

1. A data acquisition module that is configured to capture one or more characteristics of a deer, wherein the characteristics include at least one of physical attributes, genetic markers, behavioural patterns, or geographical context. This dataset shall be used for training the machine learning algorithm to improve identification accuracy.

2. A communication interface that is configured to transmit the species identification signal wirelessly to a mobile device and capturing one or more characteristics of a deer using a data acquisition module by analysis of the captured characteristics and comparing with a pre-existing database of known deer species.

3. As claimed in claim 1, the method generating a species identification signal indicative of the specific deer species based on the comparison; and transmitting the species identification signal to a user interface or external system and updating the pre-existing database with new species data based on user inputs or newly identified species.

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