Conventional weed control system is usually used by spraying herbicides uniformly throughout the land. Excessive use of herbicides on an ongoing basis can produce chemical waste that is harmful to plants and soil. The application of precision agriculture farming in the detection process in order to control weeds using Computer Vision On Farm becomes interesting, but it still has some problems due to computer size and power consumption. Raspberry Pi is one of the minicomputer with low price and low power consumption. Having computing like a desktop computer with the open source Linux operating system can be used for image processing and weed fractal dimension processing using OpenCV library and C programming. This research results the best fractal computation time when performing the image with dimension size of 128 x 128 pixels. It is about 7 milliseconds. Furthermore, the average speed ratio between personal computer and Raspberry Pi is 0.04 times faster. The use of Raspberry Pi is cost and power consumption efficient compared to personal computer.
Keywords : Weeds Detection, Computer Vision, Fractal, Raspberry Pi
 Blackmore S. Precision Farming: an overview. Agricultural Engineer. 1994; 49(3): 86-88.
 Kuhar JE. The Precision-Farming: Guide for Agriculturist. Illinois: John Deer Publishing. 1997.
 Shibusawa S, Anom IM, Sasao A, Sakai K, Hache C. Sampling strategiesin soil mapping with realtime soil spectrophotometer. Di dalam: Intelligent Control for Agricultural Application. Proceeding of
2nd IFAC-CIGR Workshop on, Bali Indonesia 22-24 August 2001. Bali: IFAC-CIGR. 2001: 40-43.
 Solahudin M. Pengembangan metode pengendalian gulma pada pertanian presisi berbasis multi
agen komputasional. Disertasi Institut Pertanian Bogor. 2013.
 Mc Bratney, AB, Prongle, MJ. Spatial Variability in Soil Implication for Precision Agriculture.
Proceeding Precision Agriculture, BIOS Scientific Publiser Ltd, Oxford. 1997.
 Fahad Shahbaz Khan, Saad Razzaq, Kashif Irfan, Fahad Maqbool, Ahmad Farid, Inam Illahi and
Tauqeer ul amin. Dr. Wheat: A Web-based Expert System for Diagnosis of Diseases and Pests in
Pakistani Wheat. Proceedings of the World Congress on Engineering WCE. 2008; 1.
 Solahudin, M Seminar, K Astika, W Buono A. Weeds and Plants Recognition using Fuzzy Clustering
and Fractal Dimension Methods for Automatic Weed Control. International Conference, the Quality
Information for Competitive Agricultural Based Production System and Commerce. 2010.
 Kargar BAH, Shirzadifar AM. Automatic Weed Detection System and Smart Herbicide Sprayer Robot
for Corn Fields. Proceeding of the 2013 RSI/ISM International Conference on Robotics and
Mechatronics, Tehran, Iran. 2013.
 Hong, Sung Minzan, Li Zhang, Qin. Detection System of Smart Sprayers: Status, Challenges and
Perspectives. Int J Agric & Biol Eng. 2012; 5(3).
 Pimentel D, McLaughlin L, Zepp A, Lakitan B, Kraus T, Kleinman P, et al. Environmental and
economic effects of reducing pesticide use. Bio Science. 1991; 41(6): 402-409.
 Leach AW, Mumford JD. Pesticide environmental accounting: A method for assessing the external
costs of individual pesticide applications. Environmental Pollution. 2008; 151(1): 139-147.
 McBratney AB, Prongle MJ. Spatial Variability in Soil Implication for Precision Agriculture. Proceeding
Precision Agriculture, BIOS Scientific Publiser Ltd, Oxford. 1997.
 Seminar KB. Paradigma Pendayagunaan Teknologi Informasi untuk Pertanian. Prosiding Seminar
Nasional Seminar Informatika Pertanian Indonesia. 2011: 34-42.
 Steward, B and L Tian. Real-time machine vision weed detection. ASAE paper No. 983033 (UILUENG-98-7006). 1998.
 M Richadson, S Wallace. Getting Started with Raspberry Pi. US: O’Reilly. 2012.
 Rahman A, Mardhani R. High Performance Computing on Cluster and Multicore Architecture.
TELKOMNIKA Indonesian Journal of Electrical Engineering. 2015; 13(4): 1408-1413.
 Albert Sagala, Deni Lumbantoruan, Epelin Manurung, Iroma Situmorang, Adi Gunawan. Secured
Communication among HMI and Controller using RC-4 Algorithm and Raspberry Pi. TELKOMNIKA
Indonesian Journal of Electrical Engineering. 2015; 15(3): 526-533.
 Ali, Murat et al. Technical Development and Socioeconomic implications of the Raspberry Pi as a
Learning Tool in Developing countries. 5th Proceedings of the IEEE Computer Science and
Electronic Engineering Comference (CEEC). 2013: 103-108.