An internet of things based smart agriculture monitoring system using convolution neural network algorithm

Abstract

Farming is a crucial vocation for survival on this planet because it meets the majority of people's necessities to live. However, as technology developed and the Internet of Things was created, automation (smarter technologies) began to replace old approaches, leading to a broad improvement in all fields. Currently in an automated condition where newer, smarter technologies are being upgraded daily throughout a wide range of industries, including smart homes, waste management, automobiles, industries, farming, health, grids, and more. Farmers go through significant losses as a result of the regular crop destruction caused by local animals like buffaloes, cows, goats, elephants, and others. To protect their fields, farmers have been using animal traps or electric fences. Both animals and humans perish as a result of these countless deaths. Many individuals are giving up farming because of the serious harm that animals inflict on crops. The systems now in use make it challenging to identify the animal species. Consequently, animal detection is made simple and effective by employing the Artificial Intelligence based Convolution Neural Network method. The concept of playing animal-specific sounds is by far the most accurate execution. Rotating cameras are put to good use. The percentage of animals detected by this technique has grown from 55% to 79%.