WEED DETECTION ON CARROTS USING CONVOLUTIONAL NEURAL NETWORK AND INTERNET OF THING BASED SMARTPHONE
Lintang Patria
Universitas Terbuka (Indonesia)
Aceng Sambas
Universitas Muhammadiyah Tasikmalaya (Indonesia)
Ibrahim Mohammed Sulaiman
Universiti Utara Malaysia (Malaysia)
Mohamed Afendee Mohamed
Universiti Sultan Zainal Abidin (Malaysia)
Volodymyr Rusyn
rusyn_v@ukr.netYuriy Fedkovych Chernivtsi National University, Department of Radio Engineering and Information (Ukraine)
https://orcid.org/0000-0001-6219-1031
Andrii Samila
Yuriy Fedkovych Chernivtsi National University (Ukraine)
Abstract
This study proposes a method based on Convolutional Neural Network (CNN) for automated detection of weed in color image format. The image is captured and transmitted to the Internet of Thing (IoT) server following an HTTP request made through the internet which is made available using the GSM based modem connection. The IoT Server save the image inside server drive and the results are displayed on the smartphone (Vision app). The results show that carrot and weed detection can be monitored accurately. The results of the study are expected to provide assistance to farmers in supporting smart farming technology in Indonesia.
Keywords:
weed detection, convolutional neural network, Internet of Thing, smartphoneReferences
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Authors
Lintang PatriaUniversitas Terbuka Indonesia
Authors
Aceng SambasUniversitas Muhammadiyah Tasikmalaya Indonesia
Authors
Ibrahim Mohammed SulaimanUniversiti Utara Malaysia Malaysia
Authors
Mohamed Afendee MohamedUniversiti Sultan Zainal Abidin Malaysia
Authors
Volodymyr Rusynrusyn_v@ukr.net
Yuriy Fedkovych Chernivtsi National University, Department of Radio Engineering and Information Ukraine
https://orcid.org/0000-0001-6219-1031
Authors
Andrii SamilaYuriy Fedkovych Chernivtsi National University Ukraine
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