Early weed detection using image processing and machine learning techniques in an Australian chilli farm

Nahina Islam, Md Mamunur Rashid, Santoso Wibowo, Cheng Yuan Xu, Ahsan Morshed, Saleh A. Wasimi, Steven Moore, Sk Mostafizur Rahman

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Abstract

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

Original languageEnglish
Article number387
JournalAgriculture (Switzerland)
Volume11
Issue number5
DOIs
Publication statusPublished - 2021
Externally publishedYes

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