Chusnul Arif

Estimation of soil moisture in paddy field using Artificial Neural Networks

In paddy field, monitoring soil moisture is required for irrigation scheduling and water resource allocation, management and planning. The current study proposes an Artificial Neural Networks (ANN) model to estimate soil moisture in paddy field with limited meteorological data. Dynamic of ANN model was adopted to estimate soil moisture with the inputs of reference evapotranspiration (ETo) and precipitation. ETo was firstly estimated using the maximum, average and minimum values of air temperature as the inputs of model. The models were performed under different weather conditions between the two paddy cultivation periods. Training process of model was carried out using the observation data in the first period, while validation process was conducted based on the observation data in the second period. Dynamic of ANN model estimated soil moisture with R 2 values of 0.80 and 0.73 for training and validation processes, respectively, indicated that tight linear correlations between observed and estimated values of soil moisture were observed. Thus, the ANN model reliably estimates soil moisture with limited meteorological data.

Estimating Crop Coefficient in Intermittent Irrigation Paddy Fields Using Excel Solver

The current study proposes a novel method using Excel Solver to estimate, from limited data, crop coefficient (Kc) in paddy fields under intermittent irrigation (II). The proposed method was examined in a field experiment conducted at Karang Sari Village, Bekasi, West Java, Indonesia during the first rice season of 2007/2008 (December 2007 to April 2008) in the rainy season. As the control, continuous flooding irrigation (CF) was applied to the conventional rice cultivation fields. Based on the observed water storage, Excel Solver was used to estimate crop evapotranspiration. Estimated crop evapotranspiration was used to compute Kc value, then the average Kc values at each growth stage were compared with that for the CF treatment. The estimation method was evaluated by comparing estimated crop evapotranspiration and the crop evapotranspiration derived by the well established FAO procedure. Excel Solver estimated crop evapotranspiration accurately with R2 values higher than 0.81. Accordingly, more than 81% of the FAO crop evapotranspiration was described by the proposed method. Thus, Kc value could be well determined from those estimated crop evapotranspiration. Under the II treatment, the average Kc values were 0.70, 1.06, 1.24 and 1.22 for the initial, crop development, reproductive and late stages, respectively. These values were lower than those under the CF treatment for initial and crop development stages because of a minimal soil evaporation and intense dryness during these stages. However, average Kc values under the II treatment were higher than those under the CF treatment at the reproductive and late stages, indicating that the II treatment promoted more plant activity particularly for dry biomass production as indicated by a greater number of tillers per hill.

Performance of Quasi Real-Time Paddy Field Monitoring Systems in Indonesia

Since 2010 paddy field monitoring systems have been installed in ten locations in Indonesia. Each system performs quasi-real-time monitoring using a FieldRouter equipped with an in situ camera and connected to meteorological and soil data loggers. All parameters are measured and monitored at 30-min intervals. Data and field images are daily transmitted to a remote server through Internet connection. During experiments, field monitoring systems showed good performance in monitoring and transmitting field data. Quasi-real-time monitoring is more power-saving and Internet-cost-effective than real-time monitoring. However, its stability depends on the field solar power supply and Internet connection. If there are any problems with the Internet connection, power supply, or sensors, the field image and the data are lost. For minimizing problems in fields, it is best to involve local residents in maintaining the systems. We plan to develop an advanced data management system for analyzing the data with specific purposes bearing on climate change in the future works. Keywords: monitoring system, paddy fields, system of rice intensification, FieldRouter, quasi real-time.

A Field Monitoring Station Network for Supporting the Development of Integrated Water Resources Management

Field monitoring systems were installed in six locations of interest for field weather and environment monitoring in support of the development of an integrated water resources management system in two watersheds, Saba in Bali province and Jeneberang in South Sulawesi province, Indonesia. The stations were situated in lower, middle, and upper sections of the watersheds, with an intention of obtaining information regarding variation in weather and soil that represents variation in the parameters of the respective watersheds. The systems include an automatic weather station, a soil monitoring system, and a Field Router remote monitoring system that delivers data daily via Internet. Data handling procedures were developed to process the data and calculate the water balance of each field. The result yielded a description of the current condition of each field that can serve as a basis for local field water management assessment. This real-time monitoring network can support water management in watersheds that are facing water-related risks resulting from land-use change and climate change. Keywords: water resources, remote monitoring, climate change, agricultural water management.

Pixel Color Clustering of Multi-Temporally Acquired Digital Photographs of a Rice Canopy by Luminosity-Normalization and Pseudo-Red-Green-Blue Color Imaging

Red-green-blue (RGB) channels of RGB digital photographs were loaded with luminosity-adjusted R, G, and completely white grayscale images, respectively (RGwhtB method), or R, G, and R + G (RGB yellow) grayscale images, respectively (RGrgbyB method), to adjust the brightness of the entire area of multi-temporally acquired color digital photographs of a rice canopy. From the RGwhtB or RGrgbyB pseudocolor image, cyan, magenta, CMYK yellow, black, L*, a*, and b* grayscale images were prepared. Using these grayscale images and R, G, and RGB yellow grayscale images, the luminosity-adjusted pixels of the canopy photographs were statistically clustered. With the RGrgbyB and the RGwhtB methods, seven and five major color clusters were given, respectively. The RGrgbyB method showed clear differences among three rice growth stages, and the vegetative stage was further divided into two substages. The RGwhtB method could not clearly discriminate between the second vegetative and midseason stages. The relative advantages of the RGrgbyB method were attributed to the R, G, B, magenta, yellow, L*, and a* grayscale images that contained richer information to show the colorimetrical differences among objects than those of the RGwhtB method. The comparison of rice canopy colors at different time points was enabled by the pseudocolor imaging method.

Environmental Assessment in Collaboration with Local Residents

Environmental assessment is key in designing a local framework for integrated water resources management dealing with land-use and climate change. We involved local residents in acquiring environmental data to clarify significant land-use and climate changes in watershed and field scales. In the watershed scale, a series of daily climate data was collected from three automatic weather stations, each available in the upstream, midstream, and downstream areas of the Saba Watershed from 2007 to 2014. The annual rainfall pattern changed; it decreased in all the stations after 2010. The annual rainfall downstream was always lower than at the other two stations located at higher elevations. As a consequence, the downstream area had the earliest and the longest dry season compared to the other areas. In the field scale, climate conditions were assessed on the basis of intensive measurements using automatic weather stations and soil sensors in three representative locations. Based on the monitored data in the context of climate change, the optimal planting date during a year was October 15 (first season), February 13 (second season), and June 12 (third season). We estimated that the irrigation water requirement was 108, 283, and 751 mm, respectively. We recommend constructing a rainwater reservoir to store more irrigation water.

Sistem Kontrol Tinggi Muka Air Untuk Budidaya Padi

This research aims to design a control system to keep the water level and soil moisture at a level that is suitable to the plant requirment and determines the optimum water level and soil moisture in each growth phase of paddy field cultivation. The water level control system was formed based on on-off controls system using Arduino Uno ATMega328P microcontroller. When the sensor gives input that the water level is below the set points, then microcontroller will command the irrigation valve to open and the drainage valve to close. The volume and time of irrigation and drainage control are dependent to set point. Set point was controlled based on water regime treatment. Water regime consisted of three treatments, which are wet regime (RB), slightly wet regime (RAB), and dry regime (RK). The research result showed that control system was very effective and efficient in controlling the water regime according to the control algorithms. Besides, the research result showed that the water regimes affected the plant growth, land productivity, and water productivity. Treatment of wet regime (RAB) gave the highest number of tiller (138 tillers), yield 194.7 g/hill (equal to 21 ton/ha with assumption of 30 cm x 30 cm spacing) and water productivity 3.16 kg/m3.