Deeplearning Based Bird Deterrent System for Agriculture

K. Srividya, S. Nagaraj, B. Puviyarasi, T. Kumar, A.Robinson stain Rufus, G. Sreeja
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引用次数: 1

Abstract

This paper addresses the solution for the problems faced by the farmers in daily life. The loss happens when the birds consume the produced crops particularl y at the time of cultivation, by which rate of production decreases. Considering those problems, the proposed system gives the solution by detecting birds automatically using convolutional neural networks. The bird detector will detect the bird when it passes through a particular range and it will compare the data with the trained images. A set of images such as bird, ball, animals and other images are trained using deep learning concept. When both the sample data and trained images are the same, it will provide loud noise. Normally birds has a sensitive hearing range, so that the bird will get disturbed As the birds have a low hearing frequency range,it gets irritated by the loud noise produced by the product and it will move from that place. By this crops can be safe and farmers can increase production rate. This works in the absence of the human.
基于深度学习的农业防鸟系统
本文就如何解决农民在日常生活中面临的问题进行了探讨。损失发生在鸟类消耗生产的作物时,特别是在耕种期间,产量下降。针对这些问题,提出了利用卷积神经网络自动检测鸟类的解决方案。鸟类探测器将在鸟类经过特定范围时检测到鸟类,并将数据与训练后的图像进行比较。利用深度学习概念训练一组图像,如鸟、球、动物等图像。当样本数据和训练图像都相同时,会产生很大的噪声。通常鸟类的听觉范围很敏感,所以鸟类会受到干扰。由于鸟类的听觉频率范围很低,它们会被产品产生的巨大噪音所激怒,然后离开那个地方。通过这种方法,作物可以是安全的,农民可以提高产量。这是在没有人的情况下工作的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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