Abstract
The problem of weeds in crops is a natural problem for farmers. Machine Learning (ML), Deep Learning (DL), and Unmanned Aerial Vehicles (UAV) are among the advanced technologies that should be used in order to reduce the use of pesticides while also protecting the environment and ensuring the safety of crops. Deep Learning-based crop and weed identification systems have the potential to save money while also reducing environmental stress. The accuracy of ML/DL models has been proven to be restricted in the past due to a variety of factors, including the selection of an efficient wavelength, spatial resolution, and the selection and tuning of hyperparameters. The purpose of the current research is to develop a new automated weed detecting system that uses Convolution Neural Network (CNN) classification for a real dataset of 4400 UAV pictures with 15336 segments. Snapshots were used to choose the optimal parameters for the proposed CNN LVQ model. The soil class achieved the user accuracy of 100% with the proposed CNN LVQ model, followed by soybean (99.79%), grass (98.58%), and broadleaf (98.32%). The developed CNN LVQ model showed an overall accuracy of 99.44% after rigorous hyperparameter tuning for weed detection, significantly higher than previously reported studies.