Classification of Weeds Detection Control Management Using Artificial and Deep Convolutional Neural Networks

Abstract

Weeds are a major threat to crop production, and their identification and classification are important tasks for farmers, agriculturalists, and scientists. This paper proposes a method for classifying weed detection using artificial neural networks (ANNs). The authors first collected weed images from multiple sources and augmented the dataset with additional data, such as background images, to create a comprehensive dataset for training. Then, the authors pre-processed the data by normalizing, segmenting, and resizing the images. The pre-processed images were then used to train the ANNs. The ANNs are trained using a dataset of weed images and features and are used to identify different weed species. To categorize the weeds in the dataset, the authors used convolutional neural networks (CNN) and deep neural networks (DNN). The authors tested their model on a validation set and obtained a 98.3% accuracy. The authors concluded that their model was able to accurately detect and classify weeds. This paper provides a method for accurately classifying weeds using ANNs with a high accuracy rate. the project will also explore the potential of using DCNNs to identify weed control methods such as manual weeding and chemical herbicides. The results are compared with other methods of weed detection, such as manual identification and image processing. The potential of ANNs for weed classification and detection in the agricultural sector is discussed in the final section. The results of this research will offer important insights into the creation of more effective and affordable weed control methods.