Subcritical water (SW) is produced from organic matter decomposed under high temperature and high pressure. Development of technique to use it for fertilizer contributes to recycling organic waste from agriculture and other human activities. Research was conducted to analyze the fertilizer characteristics of SW and its practical use in field. SW contains much organic nitrogen, and organic acid inhibits crop growing. Therefore, crop growth were compared in the following treatments, 1) to evaluate the influence of SW on crop growth compared with chemical fertilizer (CF) or no fertilizer (NF), 2) to analyze the effect of soil temperature difference covering soil with white (W) or clear (C) plastic mulch before the transplanting, and 3) to relieve the inhibitions and increase the effects of SW fertilizing on -1 or -21 DAT. Ten plots were made in the combination of these three types of treatments, and crops were grown until 32 DAT. Growth was promoted in the plots of fertilized SW on -21 DAT comparing with -1 DAT. Covering with C mulch, increased soil temperature more than W one, and enhanced nitrification. Crops in the plot of SW, -21 DAT and C mulch showed the largest grown of all SW plots on 32 DAT. However, the large inhibition from 1 to 4 DAT in its plot was estimated, because its leaf area on 32 DAT was smaller than all CF plots despite higher growth in the SW from 4 to 32 DAT than the plot of CF, -21 DAT and W mulch.
Remote sensing technology for monitoring plant trains has a huge potential to accelerate breeding process. In this paper, we have studied on remote sensing of using an unmanned aerial vehicle (UAV) system for plant traits phenotyping in rice. The images of rice canopy were taken by a RGB camera from the UAV at three growing stages; Vegetative (VG), Flowering (FW) and Grain filling (GF). Typical color indices (r, g, b, INT, VIG, L*, a*, b*, H) were calculated by image processing. Single regression analysis was conducted between rice plant traits (leaf area index (LAI), grain yield, above ground biomass, plant height, panicle length, grain filling rate, tiller number) and color indices. The index a* at FW and GF had close liner relationships with LAI (the coefficient of determination R2 > 0.70) and grain yield (R2 > 0.50). Moreover, a* and g at FW and GF showed high R2 with plant height and grain filling rate (R2 > 0.50). The R2 between grain yield and color indices increased above 0.5 for about 40% of models at three growing stages by multiple regression analysis. In particular, the models of H and INT and of H and L* at VG were closely related (R2 > 0.70). Our findings show the analysis of color images taken by UAV remote sensing is useful to assessing four rice traits; LAI, grain yield, plant height and grain filling rate at early stage, and especially more available for grain yield estimation.
In the present study, a method of tree measurement based on SLAM was proposed. In this method, a light weight portable scanning lidar that can scan an object two-dimensionally was used. An operator held the lidar in hand and moved along the targeted area on foot. Each scan data within the obtained lidar data was co-registered based on the corresponding points, resulting in generation of a complete three-dimensional point cloud image of the targeted area. Point cloud images of the target trees were picked out from the whole image of the targeted area and tree trunk diameters and heights were estimated and the errors were derived. The error estimation was conducted in several conditions, in which lidar inclination angle and walking speed were varied. As a result, root mean square errors for estimating trunk diameters and heights were 6 mm and 18.8 cm respectively with laser inclination angle of 45° and walking speed of 0.9 m/s. These results showed that this method is accurate enough for practical use. The measurement time was about 7 minutes for the area of 285 m×183 m, much shorter than one in the conventional portable lidar. This showed that the method would be applicable to large area tree measurements.