Weed Detection Using Convolutional Neural Network

Abstract

Precision agriculture relies heavily on information technology, which also aids agronomists in their work. Weeds usually grow alongside crops, reducing the production of that crop. They are controlled by herbicides. The pesticide may harm the crop as well if the type of weed is not identified. To control weeds on farms, it is required to identify and classify them. A convolutional network or CNN, a deep learning-based computer vision technology, is used to evaluate images. A methodology is proposed to detect weeds using convolutional neural networks. There were two primary phases in this proposed methodology. The first phase is image collection and labeling, in which the features for images to be labeled for the base images are extracted. In the second phase, the convolutional neural network model is constructed by 20 layers to detect the weed. CNN architecture has three layers, namely, the convolutional layer, the pooling layer, and the dense layer. The input image is given to a convolutional layer to extract the features from the image. The features are given to the pooling layer to compress the image to reduce the computational complexity. The dense layer is used for final classification. The performance of the proposed methodology is assessed using agricultural dataset images taken from the Kaggle database.