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Artificial Intelligence is getting increasingly sophisticated at doing what humans do, but more efficiently, more quickly and at a lower cost. The potential for artificial intelligence in healthcare is vast, and are increasingly a part of our healthcare eco-system. When many of us hear the term “artificial intelligence”, we imagine robots doing our jobs, rendering people obsolete, and since artificial intelligence-driven computers are programmed to make decisions with little human intervention, some wonder if machines will soon make the difficult decisions we now entrust to our doctors. …

Aim of the project was to develop an animal image classifier in dense forest environments to achieve the desired accuracy, and aid ecologists and researchers in neural network/artificial intelligence & zoological domains to further study and/or improve habitat, environmental, and extinction patterns.

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Block diagram for fauna image classification using convolutional neural network

Problem Statement

Efficient and reliable monitoring of wild animals in their natural habitats is essential to inform conservation and management decisions regarding wildlife species, migration patterns, habitat protection, and is possible, rehabilitation and grouping species of the same animals together. Processing a large volume of images and videos captured from camera traps manually is extremely expensive, time-consuming, and also monotonous. This presents a major obstacle to scientists and ecologists to monitor wildlife in an open environment. …

Image classification refers to a process in computer vision that can classify an image according to its visual content.


Today, with the increasing volatility, necessity and applications of artificial intelligence, fields like machine learning, and its subsets, deep learning and neural networks have gained immense momentum. The training needs softwares and tools like classifiers, which feed huge amount of data, analyze them and extract useful features. The intent of the classification process is to categorize all pixels in a digital image into one of several classes. Normally, multi-spectral data are used to perform the classification and, indeed, the spectral pattern present within the data for each pixel is used as the numerical basis for categorization. The objective of image classification is to identify and portray, as a unique gray level (or color), the features occurring in an image in terms of the object these features actually represent on the ground. Image classification is perhaps the most important part of digital image analysis. Classification between objects is a complex task and therefore image classification has been an important task within the field of computer vision. Image classification refers to the labelling of images into one of a number of predefined classes. There are potentially n number of classes in which a given image can be classified. Manually checking and classifying images could be a tedious task especially when they are massive in number and therefore it will be very useful if we could automate this entire process using computer vision. …


Kavish Sanghvi

I develop and manage software and websites for businesses and individuals to improve performance, productivity, and profitability.

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