Judul: Characterizing Temporal Dynamic of Weather Variability to Support Decision Making on Weed Control
Abstrak: 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
Keyword: DSS, herbicides, spray drift, weather pattern, weed control

Judul: Development of weed control method in precision farming based on multi agent computing
Abstrak: Weed control practices commonly performed on crop farming are preemergence during land preparation and after planting. Weed control in land preparation activities are carried out with a view to facilitate the work of machinery and equipment, while post emerge applications done with a view to ensure main crops can survive in the critical period which the competition of main crop and weeds tend to be very tight. Machine vision system allows monitoring of plant diseases or nutrient deficiencies for proper treatment. Selection of weed control scenarios in agricultural land varies considerably depending on several factors. Failure of weeds control is not only caused by errors in identifying weed species, density and selection of herbicides attack but also weather conditions such as wind, temperature, relative humidity and rainfall affect the effectiveness of spraying applications and potential waste by run-off and drift. Supervisory system allows users to select the type of technology and the best weed control scenario. The purpose of this research are : 1) to design supervisory system to determine the type of technology and rate of the herbicide applicator and spray the control precision farming practices based on multi-agent systems and 2) to design the method to identify main plants and weeds, and weeds attack density analysis using the parameters contained in the image. The supervisory system design consists of user preference and dialogue, knowledge module and an inference engine. Knowledge modul contains a collection of several modules related to weed control including the basis of intelligent systems, technologies database, and parallel computing base. Multi intelligent agents on the basis of intelligent system modules were involved in weed control activities in accordance with the needs on the field. Forward velocity target is achieved by operating multiple processors in parallel where each processor is dedicated to perform a particular job through an intelligent agents. Location accuracy and spraying dosage accuracy are designed by dividing the image into several segments in accordance with the size of the applicator width. Image partition method gives an application map that will guide the equipment to distribute the herbicide more precisely in therm of dose and location. In a pre-emerge application during land preparation, weed control system with 4 agents showed speed up values of 3.775 on 100 000 to unlimited the number of jobs, with an efficiency of 93.88%. While on post emerge application, weed control system with 4 agents shows the value of speed up reaches 3.814 on 100 000 to unlimited the number of jobs with an efficiency of 95.35% .
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Judul: Embriogenesis ikan buta (Astyanax fasciatus)
Abstrak: Ikan buta Astyanax fasciatus adalah spesies ikan unik dari famili Characidae yang menempati perairan dalam gua di Wilayah La Cueva Chica-San Luis Potasi-Mexico. Ikan ini memiliki mata yang tereduksi (Blind Cavefish) dan warna kulit merah muda, pucat serta mengkilap.
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