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INdica- Intelligent decision support system for rice yield prediction in precision farming

Saad, Puteh and Bakri, Aryati and Kamaruddin, Siti Sakira and Jaafar, Mahmad Nor INdica- Intelligent decision support system for rice yield prediction in precision farming. Project Report. Faculty of Computer Science and Information System, Skudai Johor. (Unpublished)

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Abstract

Indica is an intelligent decision support system for rice yield prediction based on eleven (11) input parameters such as; weeds, rusiga, daun lebar, padi angin, bena perang, worms, rats,bacteria, jalur daun merah, hawar and lodging (kerebahan). The system is ported on a web server and is available freely on the internet. The outstanding feature of this system is the IDSS architecture that incorporates a neural network model as an intelligent component. The outstanding attributes of Indica are that; it is able to predict rice yield faster, easy to use and users can change input parameters easily. This system is useful for; Ministry of Agriculture & Agro-Based Industry, Malaysian Agriculture Development Association (MADA), Malaysian Agricultural Research and Development Institute (MARDI), Lembaga Pertubuhan Peladang (LPP) and private sectors. Ministry of Agriculture & Agro-Based Industry will use it in setting agricultural policy in national planning. MADA will use it to manage the efficiency of water usage in the rice field. MARDI will use it to support Research & Development activities especially in the area of precision farming. LPP will use it to offer advice to paddy farmers to produce improved quality rice with less damage to the environment and better utilization of water. In terms of sosio-economic impact, it will help farmers to produce high quantity of rice yield without jeopardizing the quality. It is anticipated that with the adoption of this system in the farmer’s farming practice will assure that the production of high quality rice will then be sufficient for local consumption as well as to be exported. Thus, per capita income of farmers will be increased.

Item Type:Monograph (Project Report)
Uncontrolled Keywords:Rice yield prediction, IDSS architecture, neural network model
Subjects:Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Divisions:Computer Science and Information System (Formerly known)
ID Code:4385
Deposited By: Azrin Ariffin
Deposited On:25 Jun 2008 03:23
Last Modified:01 Jun 2010 03:17

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