Abstract
Abstract: The process of weed removal is critical in agricultural fields. The traditional method of weed removal is time consuming and needs more physical labor. The goal is to automatically remove weeds from agricultural fields. Using a deep learning method, the suggested study detects weeds growing between crops and removes them using an automatic cutter. The important characteristics from the agricultural images are analyzed using deep learning. The dataset has been trained to identify weeds and crops. When it comes to deep learning, Convolutional Neural Network (CNN) employs a max-pooling and fully connected layer with ReLU to identify the weed from the crop and uses a convolutional layer with a ReLU function to extract the features of a picture. The CNN network receives the pre-processed picture. From the resultant image, Region Of Interest (ROI) is extracted and also extract some features for training. The categorization is completed after the training. As a result, a deep learning network is used to detect the weed. A total of 100 photos are trained in order to increase accuracy.
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