Estimation of Rice Plant Height from a Low-Cost UAV- Based Lidar Point Clouds

UAV systems are considered effective tools to collect information regarding crops. In this study, the rice growth was observed by a small UAV-based LiDAR system from above. For developing the system, DJI S800 was chosen as a platform on which a non- survey-grade laser scanner HOKUYO UTM30LX-EW was mounted. Field experiments were carried out from late June to late early August 2017 in Nagaoka city, Niigata Prefecture, Japan. Percentile analysis is applied to locate the top and bottom positions of rice plants in three targeted areas. LIDAR-derived plant height is computed by taking the difference between the bottom and the rice plant's top. As a result, the LiDAR-derived canopy height well correlates to rice plant height (R2≥0.86; RMSE <6.0 cm). The small root means square error (RMSE =4.9 cm) is achieved with area 3. In the general case, the RMSE is 5.5 cm (R2=0.88). These results illustrate the capability of estimate plant height before the heading stage from UAV- based LiDAR point clouds without ground surface detection.

Темплатный синтез монодисперсных сферических нанопористых частиц кремния субмикронного размера

Monodisperse spherical nanoporous silicon ( np -Si) particles of submicrometer size are fabricated with mesoporous silica particles as a template. Silicon is synthesized within the mesopores of monodisperse silica particles by the thermal decomposition of monosilane. Then, the template material ( a -SiO_2) is removed by wet etching. The particles obtained have a small root-mean-square size scatter (no more than 10%) and large specific surface area (250 m^2 g^–1) and pore volume (0.5 cm^3 g^–1). It is shown that np -Si particles exhibit photoluminescence in the visible and near-IR (infrared) spectral ranges.

Pemodelan Fuzzy Inference System Tsukamoto Untuk Prediksi Curah Hujan Studi Kasus Kota Batu

The uncertain pattern of rainfall causes apple farmers to be difficult in determining the flowering time that resulted in the apple harvest becomes not maximal. Many methods are used to predict rainfall, one of which is Fuzzy Inference System (FIS) Tsukamoto. Earlier research using this method managed to get a fairly small Root Mean Square Error (RMSE) value. In this research, FIS Tsukamoto method is used to create rainfall prediction modeling in four locations in Batu, East Java with the aim to get a small RMSE also. Tsukamoto's FIS method is used to predict rainfall with time series data from 2005 to 2014. The result of this research is the prototype of Tsukamoto FIS method that can be used to predict rainfall with RMSE error value in Junggo area of 9,196, in Pujon area of 9,407 , in Tinjomulyo area of 8,798, in Ngujung area of 8,825.

Genetic diversity and combining abilities for root traits of sugar beet pollinators

Information about genetic diversity and combining abilities of sugar beet parental components are of a great importance for hybrid creation. The aim of this research was to evaluate genetic diversity among sugar beet pollinators from different breeding programs and their combining abilities for main root traits of sugar beet, root weight, sugar content and sugar yield. As plant material were used eight pollinators originating from three different USDA-ARS breeding programs and four from Institute of field and vegetable crops Novi Sad. The analysis of variance and Duncan?s multiple range test revealed significant differences (p = 0.05) among pollinators for all investigated traits. Pollinator CR10 differs from all others in terms of quantitative traits and in terms of combining ability. Despite small root weight CR10 had significantly positive GCA for that trait and showed that genotypes with small root should not be automatically discarded. Pollinators from Institute of field and vegetable crops used in this research had negative GCA for root weight and should be used only as hosts for introduction of new germplasm in future breeding program.