<p>Weather monitoring and forecasting are very important in agricultural sectors. There are several data need to be collected in real-time to support weather monitoring and forecasting systems, such as temperature, humidity, air pressure, wind speed, wind direction, and rainfall. The purpose of this research to develop a real-time weather monitoring system using a parallel computation approach and analyze the computational performance (i.e., speed up and efficiency) using the ARIMA model. The developed system wireless has been implemented on sensor networks (WSN) platform using Arduino and Raspberry Pi devices and web-based platform for weather visualization and monitoring. The experimental data used in our research work is a set of weather data acquired and collected from January until March 2017 in Bogor area. The result of this research is that the speed up of the using eight processors computation three times faster than using a single processor, with the efficiency of 50%.</p>
Increasing of economic development is generally followed by the change of landuse from agriculture to other function. If it occurs in large frequency and amount, it will threaten national food security. Therefore, it is necessary to monitor the agricultural land, especially paddy fields regarding to changes in landuse and global climate. Utilization and development of satellite technology is necessary to provide more accurate and independent database for agricultural land monitoring, especially paddy fields. This study aims to develop a utilization model for LAPAN-IPB satellite (LISAT) and other several satellites data that have been used for paddy field monitoring. This research is conducted through 2 stages: 1) Characterization LISAT satellite data to know spectral variation of paddy field, and 2) Development method of LISAT data fusion with other satellites for paddy field mapping. Based on the research results, the characteristics Red and NIR band in LISAT data imagery have a good correlation with Red and NIR band in LANDSAT 8 OLI data imagery, especially to detect paddy field in the vegetative phase, compared to other bands. Observation and measurement of spectral values using spectroradiometer need to be conducted periodically (starting from first planting season) to know the dynamics of the change related to the growth phase of paddy in paddy field. Pre-processing of image data needs to be conducted to obtain better LISAT data characterization results. Furthermore, it is necessary to develop appropriate algorithms or methods for geometric correction as well as atmospheric correction of LISAT data.
A Greenhouse has a different microclimate compared to the outside field. Climate parameters such as solar radiation and air temperature are important parameters that affect plant growth and productivity. This research aims to understand the relation of climate factors in the inside and outside Greenhouse, the effect of microclimate on evapotranspiration and to predict the amount og evapotranspiration inside the Greenhouse. Microclimate analysis was held in two stages, the firststage was from February 5 to March 21, 2018 and the second stage from March 19 to April 29, 2019 at the Department of Civil and Environmental Engineering, IPB University. Primary data was measured by the Decagon sensor. Solar radiation was collected using the Decagon PYR Pyranometer sensor and air temperature using the Decagon VP-4 sensor. Based on the result, the daily air temperature inside the Greenhouse was higher than that of the outside. The inside solar radiation was lower than that of outside the Greenhouse. The relative humidity fluctuated, and the air pressure was higher inside the Greenhouse. Evapotranspiration inside the Greenhouse was lower than outside and solar radiation was the most determining factor of evapotranspiration.
Spatiotemporal analysis of MODIS Vegetation Index Imagery widely used for vegetation seasonal mapping b oth on forest and agricultural site. In order to provide a long -terms of vegetation characteristic maps, a wide time-series images analysis is needed which require high -performance computer and also consumes a lot of energy resources. Meanwhile, for agriculture monitoring purpose in Indonesia, that analysis has to b e employed gradually an d endlessly to provide the latest condition of paddy field vegetation information. This research is aimed to develop a method to produce the optimized solution in classifying vegetation of paddy fields that diverse b oth spatial and temporal characteristics . The time-series EVI data from MODIS have b een filtered using wavelet transform to reduce noise that caused b y cloud. Sequential K-means and Parallel K-means unsupervised classification method were used in b oth CPU and GPU to find the efficient and the rob ust result. The developed method has b een tested and implemented using the sample case of paddy fields in Java Island. The b est system which can accommodate of the extend-ab ility, affordab ility, redundancy, energy-saving, maintainab ility indicators are ARM-b ased processor (Raspb erry Pi), with the highest speed up of 8 and the efficiency of 60% .
Faktor manusia sebagai tenaga kerja memiliki peranan penting dalam meningkatkan produktivitas beras Organik. Namun, sampai saat ini upah standar untuk pekerja pertanian belum ditentukan oleh lembaga pemerintah, sehingga bisa dikatakan bahwa peran pemerintah terhadap pekerja pertanian masih sangat kurang. Penelitian ini bertujuan untuk membuat formulasi berdasarkan analisis ekonomi ergonomika. Berdasarkan produktivitas kerja saat ini, pendekatan ekonomi ergonomika membuktikan bahwa upah yang diperoleh pekerja masih jauh dari standar UMR. Dari dua skenario optimasi yang digunakan, ditemukan bahwa perancangan penambahan mekanisasi secara selektif menggunakan mesin milik sendiri mampu memberikan upah yang optimal karena sudah memenuhi standar UMR Kabupaten Bogor.
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