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

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  • 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)

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.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:5:p:387-:d:543131
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    References listed on IDEAS

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    1. Nahina Islam & Md Mamunur Rashid & Faezeh Pasandideh & Biplob Ray & Steven Moore & Rajan Kadel, 2021. "A Review of Applications and Communication Technologies for Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) Based Sustainable Smart Farming," Sustainability, MDPI, vol. 13(4), pages 1-20, February.
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    3. Haotian Pei & Youqiang Sun & He Huang & Wei Zhang & Jiajia Sheng & Zhiying Zhang, 2022. "Weed Detection in Maize Fields by UAV Images Based on Crop Row Preprocessing and Improved YOLOv4," Agriculture, MDPI, vol. 12(7), pages 1-18, July.
    4. Sidrah Mumtaz & Mudassar Raza & Ofonime Dominic Okon & Saeed Ur Rehman & Adham E. Ragab & Hafiz Tayyab Rauf, 2023. "A Hybrid Framework for Detection and Analysis of Leaf Blight Using Guava Leaves Imaging," Agriculture, MDPI, vol. 13(3), pages 1-22, March.
    5. Benjamin Costello & Olusegun O. Osunkoya & Juan Sandino & William Marinic & Peter Trotter & Boyang Shi & Felipe Gonzalez & Kunjithapatham Dhileepan, 2022. "Detection of Parthenium Weed ( Parthenium hysterophorus L.) and Its Growth Stages Using Artificial Intelligence," Agriculture, MDPI, vol. 12(11), pages 1-23, November.
    6. Xianguo Ren & Haiqing Tian & Kai Zhao & Dapeng Li & Ziqing Xiao & Yang Yu & Fei Liu, 2022. "Research on pH Value Detection Method during Maize Silage Secondary Fermentation Based on Computer Vision," Agriculture, MDPI, vol. 12(10), pages 1-17, October.
    7. Shirin Ghatrehsamani & Gaurav Jha & Writuparna Dutta & Faezeh Molaei & Farshina Nazrul & Mathieu Fortin & Sangeeta Bansal & Udit Debangshi & Jasmine Neupane, 2023. "Artificial Intelligence Tools and Techniques to Combat Herbicide Resistant Weeds—A Review," Sustainability, MDPI, vol. 15(3), pages 1-18, January.
    8. Xinle Zhang & Jian Cui & Huanjun Liu & Yongqi Han & Hongfu Ai & Chang Dong & Jiaru Zhang & Yunxiang Chu, 2023. "Weed Identification in Soybean Seedling Stage Based on Optimized Faster R-CNN Algorithm," Agriculture, MDPI, vol. 13(1), pages 1-16, January.
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    10. Benjamin T. Fraser & Christine L. Bunyon & Sarah Reny & Isabelle Sophia Lopez & Russell G. Congalton, 2022. "Analysis of Unmanned Aerial System (UAS) Sensor Data for Natural Resource Applications: A Review," Geographies, MDPI, vol. 2(2), pages 1-38, June.
    11. El-Sayed M. El-Kenawy & Nima Khodadadi & Seyedali Mirjalili & Tatiana Makarovskikh & Mostafa Abotaleb & Faten Khalid Karim & Hend K. Alkahtani & Abdelaziz A. Abdelhamid & Marwa M. Eid & Takahiko Horiu, 2022. "Metaheuristic Optimization for Improving Weed Detection in Wheat Images Captured by Drones," Mathematics, MDPI, vol. 10(23), pages 1-30, November.

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