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Reysan, Jason
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Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Engineering Industrial & Manufacturing Engineering

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Rancang Bangun Sistem Deteksi Hama Tanaman Whiteflies Berbasis Jaringan Sensor Nirkabel dan Aplikasi Web Tamba, Tua Agustinus; Darwin, Stevanus; Solaiman, Gabriela; Reysan, Jason
Jurnal Otomasi Kontrol dan Instrumentasi Vol 16 No 2 (2024): Jurnal Otomasi Kontrol dan Instrumentasi
Publisher : Pusat Teknologi Instrumentasi dan Otomasi (PTIO) - Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/joki.2024.16.2.8

Abstract

One of the main challenges in farming activities is regarding efforts to detect and monitor pests, which can be used as a basis for determining correct pest control efforts. Greenhouse whitefish (GWF) is a dangerous pest that absorbs plant sap, causing plants to weaken or even die due to lack of nutrition. Efforts to detect GWF pests are generally carried out manually using yellow sticky traps (YST), which require routine and repeated checking efforts from farmers with a long time interval between each checking activity. This research is aimed to design an automatic monitoring solution of GWF pests based on the camera image of YST paper. The proposed design method uses a web-based wireless sensor network system equipped with image processing algorithms that operate in a real-time manner. In the proposed method, the camera sensor is used to capture YST images, which are then processed using a website-based application system. The evaluation of the prototype testing suggests that the designed pest detection system functions well and without problems. With regard to the developed hardware, evaluation results show that the camera sensor can be accessed and send data to the developed website application in a real-time manner. Regarding the image processing algorithm, evaluation results show that the developed histogram based image processing technique can detect GWF on plant leaf being considered with accuracy levels up to a range of 94%-100%.
Co-Authors Darwin, Stevanus Solaiman, Gabriela Tua Agustinus Tamba