Nitrogen is an important variable for paddy farming management. The objectives of this study were to develop and test a new method to determine the status of nitrogen and chlorophyll content in rice leaf by analysing and considering all visible bands derived from images captured using a conventional digital camera. The images from the 6-pannel leaf colour chart were acquired using Basler Scout scA640-70fc under light-emitting diode lighting, in which principal component analysis was used to retain the lower order principal component to develop a new index. Digital photographs of the upper most collared leaf of rice (Oriza sativa L.), grown over a range of soils with different nitrogen treatments, were processed into 11 indices and IPCA through six growth stages. Also a conventional digital camera mounted to an unmanned aerial vehicle was used to acquire images over the rice canopy for the purpose of verification. The result indicated that the conventional digital camera at the both leaf (r = −0.81) and the canopy (r = 0.78) scale could be used as a sensor to determine the status of chlorophyll content in rice plants through different growth stages. This indicates that conventional low-cost digital cameras can be used for determining chlorophyll content and consequently for monitoring nitrogen content of the growing rice plant, thus offering a potentially inexpensive, fast, accurate and suitable tool for rice growers. Additionally, results confirmed that a low cost LARS system would be well suited for high spatial and temporal resolution images and data analysis for proper assessment of key nutrients in rice farming in a fast, inexpensive and non-destructive way.
The study was to evaluate SWAT model for flow simulation and forecasting in the Upper Bernam humid tropical river basin, which is the main source of irrigation water supply for a rice granary. Land use in the study area has rapidly changed from the year of 1984 until today. The study was conducted using 27 years of records . Calibration was performed for the period of 1981 through 2004 while, the period of 2005 through 2007 for the validation of both simulation and forecasting of flow. During calibration, the annual and monthly results were 0.82, 0.65, 0.81 and 0.62 for R2 and ENS, respectively and 0.99, 0.93, 0.98 and 0.92, respectively during validation. As for forecasting validation, were 0.88, 0.78, 0.86 and 0.74 for R2 and ENS, respectively. In general model shows good performance in flow simulating as well as forecasting. Five scenarios were performed to identify the individual effect of mixed land use change on stream flow. The scenarios results demonstrate, land use changes are responsible for an increase in the annual flow depth between 8% to 39% while 16% to 59% during high flow months and decreases between 3% to 32% during low flow months. Flow forecasting for the year 2020 using 30 forecasting cycles which found to be the optimal for the study area was performed. The results show decrease by 50% below the monthly irrigation water demand during low flow months, which emphasize the need to include structured best management practices (BMPs) such as ponds to the study area future land development plan to mitigate the future changes in land use on flow quantity. This study showed that SWAT was able to simulate and forecast flow in humid tropical condition successfully and can be used to study the effects of future land use changes on flow.
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