Towards Food Security through Artificial Neural Network

Q4 Mathematics
Pratibha Phaiju
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引用次数: 1

Abstract

The detection of plant disease is a very important factor to prevent serious outbreak. The Outbreak of disease in paddy plant could cause severe losses in yield leading to insecurity of food security. To achieve automatic diagnosis of paddy disease this research aims to develop a system for detection of Blast disease in paddy leaf. The disease identification is achieved through Image Processing technique and Back Propagation Neural Network. Features of images are extracted through binning pixels into eight Attribute Bins. Training of Neural Network is achieved by feed forwarding these features to neural network. The error generated is back propagated in order to adjust the weights of neural network. Images of the diseased leaves are identified with accuracy. Thus fast and accurate diagnosis of paddy disease could timely control outbreak leading the path towards ensuring food security. This research could be enhanced through implementation of Deep Learning Neural Network, further contributing the Smart Agriculture.
通过人工神经网络实现粮食安全
植物病害的检测是防止严重病害发生的重要因素。水稻病害的发生会造成严重的产量损失,从而导致粮食安全的不安全。为了实现水稻病害的自动诊断,本研究旨在开发水稻叶片稻瘟病的检测系统。通过图像处理技术和反向传播神经网络实现病害识别。通过将像素分成8个Attribute bin提取图像特征。神经网络的训练是通过将这些特征转发给神经网络来实现的。对产生的误差进行反向传播,以调整神经网络的权值。病变叶片的图像被准确地识别出来。对水稻病害进行快速、准确的诊断,可以及时控制病害的发生,确保粮食安全。该研究可以通过深度学习神经网络的实施来加强,进一步为智慧农业做出贡献。
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来源期刊
AIUB Journal of Science and Engineering
AIUB Journal of Science and Engineering Mathematics-Mathematics (miscellaneous)
CiteScore
1.00
自引率
0.00%
发文量
3
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