Weed Identification Using Convolution Neural Networks

  • Murali Krishna B Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, India
Keywords: Weeds and Crops Classification, Shape Features, Convolution Neural Network, Contour Property, VGG 19, Machine Learning


Deep learning is the core component of the machine learning field which employs knowledge representation for learning. Learning can be supervised or unsupervised. More deep learning techniques can be used which will contain deep belief, deep neural, recurrent neural networks in it which will be used in many fields. The most commonly used applications in deep learning are vision, audio, video, language processing, social media, medical, gaming and there are so many other programs where this deep learning has already produced very perfect results when compared to other cases and in a very little number of cases with superior to experts i.e. humans. Techno Agriculture is the domain where the farmers will get benefited from these latest improvements in the expert system. One of main objectives is that in order to remove weeds or unwanted plants by reduction in the usage of herbicides and to decrease the pollution in both crop and water. One of the Neural Networks i.e. CNN uses a flexible layer with the function of a ReLU to extract image elements and then uses a high-resolution and fully integrated RELU layer to separate weeds from the plant. The image which was processed previously is used on the convolution neural network which in return gives an image from the Region of Interest (ROI) from where it will extract the image and remove the certain aspects of the image in the training phase, after the training a splitting operation will be performed and the weeds are therefore classified by using the deep learning technique. In this scenario we trained 100 images in order to increase the accuracy of the model.


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How to Cite
B, M. K. (2023). Weed Identification Using Convolution Neural Networks. International Journal of Computer Communication and Informatics, 5(2), 1-11. https://doi.org/10.34256/ijcci2321

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