Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm

This paper explores the potential of machine learning algorithms for weed and crop classification from UAV images. The identification weeds in crops is a challenging task that has been addressed through orthomosaicing images, feature extraction labelling images to train algorithms. In this paper, performances several algorithms, random forest (RF), support vector (SVM) k-nearest neighbours (KNN), are analysed detect using collected chilli field located Australia. evaluation metrics used comparison performance were accuracy, precision, recall, false positive rate kappa coefficient. MATLAB simulating algorithms; achieved detection accuracies 96% RF, 94% SVM 63% KNN. Based on study, RF efficient practical use, can be implemented easily detecting