| dc.description.abstract | Herbicide is the dominant tool used for weed control in modern agriculture. Although herbicide has positive benefit in killing the target weeds, it potential becomes negative impact to the environment if some remains in the air and drift. Spray drift can happen due to unsuitable weather. The knowledge of weather condition will help farmer and decision maker to decide the appropriate technology and method for eradicating weed which minimize drift and other potential waste. The progress of information technology has been applied widely in agriculture such as precision agriculture. Solahudin (2013) developed a weed control method using multi-agents based. That system has two functions i.e. consultation function by giving recomendation before spraying (off-farm) and controlling during spray application (on-farm) using multi-intelligent agents which applied for groundnut farming. Relationship with this research is the system built by Solahudin (2013) needs to be improved in knowledge that related with weather. Weather data can be used to optimize spray scheduling on weed control activities that safe to the environment and for setup the equipment and machinery which prepared earlier before application day. The spatial and temporal variability weather conditions are important sources for agricultural activities such as spray application. Integration meteorological satellite with numerical weather prediction (NWP) product is promising in find timely weather variables as input for decision making to resolve problems in spray application especially for area which sparse coverage of weather stations. The objective of this research is to develop a decision support system (DSS) for schedulling of weed control 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 such as the National Oceanic & Atmospheric Administration (NOAA) and the Tropical Rainfall Measuring Mission (TRMM) and a set of vegetation index from the MODerate Resolution Imaging Spectroradiometer (MODIS). The vegetation index data utilized to determine the planting period of paddy and weather data set utilized to determine spray schedule and to determine the proper size of the sprayers for weed control. In precision farming, weed control is done two times i.e. pre-planting and post-emergence. To know these times, we used planting time as reference. We used paddy for this study. Paddy planting time is easy to identify through multi-temporal analysis of vegetation index. Enhanced vegetation index (EVI) of paddy field in study area shows the annual paddy growth cycle, it representing intensive cropping with multiple harvests. Based on analysis of EVI, we estimated the spraying times for paddy are April to May and October to November for cycle 1 and cycle 2, respectively. We study weather pattern in Jonggol during ten years. Every parameter have own characteristic and generally in same fluctuated pattern form. Generally, during ten years rainfall is high in year end to early year while low in mid-year. Wind speed is fluctuates. Wind is high in year end to January every year about more than 10 km/s. For ten years, minimum and maximum temperatures are 23.57°C and 30.5°C. Relative humidity decreased when air temperature increase. It is about 67.5 – 95.5%, high in year end to early year, and lowers in middle year. Farmer or decision maker can use information from past weather data to find out optimal time for scheduling, preparing machinery and sprayer. Optimal week for weed control determined from interval time for spraying both in crop cycle 1 and crop cycle 2. We developed application to determine the proper nozzle size for sprayer based on weather condition. Knowledge to determine nozzle size is acquired from previous research. Rainfall is the first parameter which decides do spray or do not spray, because spray application will not conducted in rainy day and herbicide particles will run off along with rain water. Wind becomes second parameter, following by temperature and humidity. Weather parameters can be inputted manually or can use weather data taken from the past datasets which stored in database. The DSS for weed applications has been developed and tested with a real data set acquired from remote sensing devices. The developed system can generate optimal spray scheduling and recommend the proper size of nozzles used for spray application on paddy crops based on the weather condition, and thus minimizing spray drift and bad environmental impact. This method could be implemented both for low-land crop and high-land crop. | en |
