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Weed Control Decision Support System Based on Precision Agriculture Approach

Journal from gdlhub / 2016-11-12 07:31:23
Oleh : Rizky Mulya Sampurno, Kudang Boro Seminar, Yuli Suharnoto, Telkomnika
Dibuat : 2014-06-01, dengan 1 file

Keyword : DSS, precision agriculture, weed control, herbicides, spray drift, weather pattern, temporal data
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/62

Herbicides have been widely used for weed control in modern agriculture. However the use of herbicides is potentialy introducing negative impact to the environment due to excessive use of herbicides. Based on precision agriculture principles, unique and precise treatment of herbicide supply for a particular area for crop production must be performed. The objective of this research is to develop a decision support system (DSS) for schedulling of weed spraying and for selecting the proper nozzle size of the sprayers that introduce minimum negative impact to the environment. The main set of data required for our proposed system includes the set of 10 years weather data series acquired from remote sensing (NOAA and TRMM) and a set of vegetation index from MODIS EVI. The weather data set is utilized to determine the planting time period of paddy crop and to determine the proper size of the sprayers for weed spraying. Our DSS prototype has been implemented and tested with real data set in Jonggol district, West Java, Indonesia. The implementation, testing results, and future enhancement of our system are discussed in this paper.

Deskripsi Alternatif :

Herbicides have been widely used for weed control in modern agriculture. However the use of herbicides is potentialy introducing negative impact to the environment due to excessive use of herbicides. Based on precision agriculture principles, unique and precise treatment of herbicide supply for a particular area for crop production must be performed. The objective of this research is to develop a decision support system (DSS) for schedulling of weed spraying and for selecting the proper nozzle size of the sprayers that introduce minimum negative impact to the environment. The main set of data required for our proposed system includes the set of 10 years weather data series acquired from remote sensing (NOAA and TRMM) and a set of vegetation index from MODIS EVI. The weather data set is utilized to determine the planting time period of paddy crop and to determine the proper size of the sprayers for weed spraying. Our DSS prototype has been implemented and tested with real data set in Jonggol district, West Java, Indonesia. The implementation, testing results, and future enhancement of our system are discussed in this paper.

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