WEED DETECTION ON CARROTS USING CONVOLUTIONAL NEURAL NETWORK AND INTERNET OF THING BASED SMARTPHONE

Autor:	Lintang Patria, Aceng Sambas, Ibrahim Mohammed Sulaiman, Mohamed Afendee Mohamed, Volodymyr Rusyn, Andrii Samila
Jazyk:	English<br />Polish
Rok vydání:	2024
Předmět:	weed detection convolutional neural network Internet of Thing smartphone Environmental engineering TA170-171 Environmental sciences GE1-350
Zdroj:	Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, Vol 14, Iss 3 (2024)
Druh dokumentu:	article
ISSN:	2083-0157 2391-6761
DOI:	10.35784/iapgos.5968
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/488dfeb1877b452f8dc4eec5de60e3de Zobrazit plný text záznamu View record in DOAJ