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

journal contribution
posted on 16.09.2021, 23:49 by Nahina IslamNahina Islam, MD Mamunur RashidMD Mamunur Rashid, Santoso WibowoSantoso Wibowo, Chengyuan XuChengyuan Xu, Ahsan Morshed, Saleh A Wasimi, Steven MooreSteven Moore, Sk Mostafizur Rahman
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.

History

Volume

11

Issue

5

Start Page

1

End Page

13

Number of Pages

13

eISSN

2077-0472

Publisher

MDPI

Additional Rights

CC BY 4.0

Language

en

Peer Reviewed

Yes

Open Access

Yes

Acceptance Date

19/04/2021

Author Research Institute

Centre for Intelligent Systems

Era Eligible

Yes

Journal

Agriculture

Article Number

387