SMART CROP PROTECTION SYSTEM USING MACHINE LEARNING

MACHINE LEARNING

Nowadays, animal detection has become a difficult task, especially in huge and distant places. Classic methods of animal detection, such as human surveillance and traps, as well as electric fences, are frequently ineffective and can hurt animals. There has been an increase in utilization of employing machine learning techniques for animal detection in recent years. We have analyzed two machine learning algorithms that have been found to be successful for animal detection are convolutional neural networks (CNNs) and support vector machines (SVMs). Using CNNs and SVMs, this study confirms a novel technique to animal detection. CNNs are used to extract features from animal visuals, while SVMs are used to classify the images according to various animal categories. CNNs are an effective technique for detecting animals. On a range of animal detection datasets, they have been confirmed to produce state-of-the-art results. CNNs are projected to being much more potent and effective at spotting animals as they advance. There are various machine learning methods that can be utilized for animal detection in addition to SVMs and CNNs. The field of animal detection is rapidly developing.

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