Early Weed Detection Using Image Processing And ... - IDEAS/RePEc

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Printed from https://ideas.repec.org/a/gam/jagris/v11y2021i5p387-d543131.html My bibliography Save this article Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm

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Author

Listed:

• Nahina Islam

  (School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4700, Australia Centre for Intelligent Systems, School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4700, Australia Institute for Future Farming Systems, Central Queensland University, Bundaberg, QLD 4670, Australia)

• Md Mamunur Rashid

  (School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4700, Australia Centre for Intelligent Systems, School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4700, Australia)

• Santoso Wibowo

  (School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4700, Australia Centre for Intelligent Systems, School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4700, Australia)

• Cheng-Yuan Xu

  (Institute for Future Farming Systems, Central Queensland University, Bundaberg, QLD 4670, Australia School of Health, Medical and Applied Sciences, Central Queensland University, Bundaberg, QLD 4760, Australia)

• Ahsan Morshed

  (School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4700, Australia)

• Saleh A. Wasimi

  (School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4700, Australia)

• Steven Moore

  (School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4700, Australia Centre for Intelligent Systems, School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4700, Australia Institute for Future Farming Systems, Central Queensland University, Bundaberg, QLD 4670, Australia)

• Sk Mostafizur Rahman

  (School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4700, Australia ConnectAuz pty Ltd., Truganina, VIC 3029, Australia)

Registered:

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.

Suggested Citation

Nahina Islam & Md Mamunur Rashid & Santoso Wibowo & Cheng-Yuan Xu & Ahsan Morshed & Saleh A. Wasimi & Steven Moore & Sk Mostafizur Rahman, 2021. "Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm," Agriculture, MDPI, vol. 11(5), pages 1-13, April.

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