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Volume 74, Issue 4
Displaying 1-6 of 6 articles from this issue
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Full Paper
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Yu ZHANG, Poching TENG, Mitsuko AONO, Yo SHIMIZU, Fumiki HOSOI, Kenj ...2018 Volume 74 Issue 4 Pages 129-139
Published: 2018
Released on J-STAGE: October 10, 2018
JOURNAL FREE ACCESSIn recent decades, some photogrammetric methods for 3D monitoring of plant growth and structure parameters have been studied by the automated feature extraction and matching. In this study on growth analysis of sweet potato (Ipomoea batatas L.) plants at different fertilizer conditions, we proposed a convenient solution of 3D reconstruction by a single camera photography system based on Structure from Motion (SfM) method. Also, we handled effectively the noise problem by minimizing re-projection errors. The results of 3D models demonstrated that the average percentage error was a constant about 4.8% for plant height or decreased from 13% to 8% (leaf area index, LAI=3.5) for leaf number and from 19% to 12% (LAI=3.5) for leaf area with increasing in LAI, although each percentage error fluctuated, especially at low LAI. In contrast, the average percentage error in 2D image processing was 20% to 45% for leaf number and 60% to 90% for leaf area, and the leaf height was immeasurable. Comparing with the errors of 3D results, the errors of 2D images were much larger because 2D imaging had some problems, such as not being robust against occlusion of plant organs, and the ambiguity between object size and distance from the camera. On the other hand, we examined a method to calibrate the estimates from 3D models using the regression model between the measured value and the value estimated from 3D model. The regression models showed the linear and good estimation for leaf height (R2=0.97 and RMSE=0.71 cm), leaf number (R2=0.99 and RMSE=4.03) and leaf area (R2=0.98 and RMSE=0.12 m2), in spite of use of data across a wide range in fertilizer supply and growth stage of sweet potato plants. The results demonstrated that 3D imaging technique in the study has the potential to remotely monitor plant growth status and estimate growth and structure parameters at various environmental factors outdoors.View full abstractDownload PDF (1834K) -
Tsuneo KUWAGATA, Shigenori HAGINOYA, Keisuke ONO, Yasushi ISHIGOOKA, A ...2018 Volume 74 Issue 4 Pages 140-153
Published: 2018
Released on J-STAGE: October 10, 2018
JOURNAL FREE ACCESS
Supplementary materialIn farmland areas, meteorological conditions affect crop growth and physiology. We examined the influence of seasonal changes in local agricultural land cover on meteorological conditions of farmland in a rural area of Tsukuba City, a medium-sized city in Japan, by comparing conditions in the rural area with those in the urban and suburban areas (horizontal scale ~10 km) of Tsukuba City. The daily mean temperature at a rural farmland site (single cropping system: paddy rice in summer, bare soil in winter) was 0−3℃ lower than that at an urban site (observational field at the Meteorological Research Institute, about 7 km from the farmland site), depending on the season (annual mean 1.07℃ during 2004−2006). The daily mean water vapor pressure deficit at the farmland site was also smaller than that at the urban site across all seasons, and it was markedly lower during the rice-growing period (May to mid-September), resulting in a more humid climate at the farmland site during this period. During the paddy rice-growing period, day-to-day differences in daytime temperature and water vapor pressure between the farmland and urban sites tended to increase with daily solar radiation, and were closely related to the day-to-day difference in daytime sensible and latent heat fluxes between the two sites. During the fallow period (late September to April), the difference in daily minimum temperature between the two sites tended to increase with possible radiative cooling and became larger under weak wind conditions.View full abstractDownload PDF (952K) -
Kenta ITAKURA, Fumiki HOSOI2018 Volume 74 Issue 4 Pages 154-161
Published: 2018
Released on J-STAGE: October 10, 2018
JOURNAL FREE ACCESSRecently, structure from motion (SfM), which converts multiple images to a detailed three-dimensional (3D) model, has been used to extract 3D structural information about vegetation. However, multiple still images (e.g., >100 images) are necessary for the 3D reconstruction. This requires multiple shutter releases, but taking many images is time consuming and labor intensive. One possible solution is to take video recordings from which many images can be obtained by dividing the video clips into video frames. However, frames from videos are sometimes blurred owing to camera vibration, which leads to inaccurate construction of the 3D model. Furthermore, their resolution is lower than that of still images, which may lead to inaccurate 3D reconstruction and estimation error of tree trunk diameter, tree height, and the number of trees observable in the 3D images. We propose a method to record videos, remove blurred video frames using machine learning, and construct 3D images. We compare the accuracy of the 3D models reconstructed from video frames with that from the still images. The blurred video frames were classified by a convolutional neural network (CNN) with an accuracy of 97%. The classification to remove these video frames improved the accuracy of the 3D models based on video frames taken at a walking speed of more than 0.5 m/s, which included many blurred ones. There was no significant difference in the accuracy of tree trunk diameter and tree height estimation between the 3D models obtained from the video frames and the still images when using the CNN classification. At a close enough distance (e.g., 20 m), the 3D model reconstructed from video frames was as accurate as the models constructed from still images. Video recording enables effective data collection for SfM, and the present method can be applicable to the 3D reconstruction of trees in various fields.View full abstractDownload PDF (454K) -
Funian ZHAO, Jun LEI, Runyuan WANG, Heling WANG, Kai ZHANG, Qian ...2018 Volume 74 Issue 4 Pages 162-172
Published: 2018
Released on J-STAGE: October 10, 2018
JOURNAL FREE ACCESSAgricultural drought frequently occurs and results in major grain yield loss in semi-arid climate region, but determining it is difficult. This study was conducted to determine agricultural drought for spring wheat (Triticum aestivum L.) in the western Loess Plateau of China. Several statistical models were established and evaluated by long-term data, including soil water in soil layer of 50 cm depth at sowing day, air temperature, precipitation, pan evaporation during spring wheat growing season, and two groups of spring wheat yield (one from field experiments during 1987-2011 and the other from statistical Bureau during 1980-2013). Even though each of water supply factors, precipitation during growing season and the soil water at sowing day, could separately explain no more than 30% variation of the yield, both of them could explain >55% yield variation under dry condition. Average air temperature and precipitation during growing season that displayed two apparent yield categories (drought and normal) could be used to determine agricultural drought by pattern recognition when years with the soil water at sowing day of >98.4 mm were eliminated. Based on long-term meteorological data and the relationship between soil water at sowing day and yield under different growing season moisture conditions, the probability of agricultural drought occurrence in Dingxi for spring wheat was speculated, which nearly corresponds with the observational data during 1980-2013.View full abstractDownload PDF (367K)
Short Paper
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Yuki SAGO, Naoya WATANABE, Yudai MINAMI2018 Volume 74 Issue 4 Pages 173-177
Published: 2018
Released on J-STAGE: October 10, 2018
Advance online publication: September 15, 2018JOURNAL FREE ACCESSTo examine growth conditions that can increase the critical dietary mineral zinc in leaves of baby leaf red oak lettuce, we investigated zinc accumulation under various conditions, including different zinc concentrations in nutrient solution, wind speeds, and root zone temperatures. A high zinc concentration in the nutrient solution led to greatly increased leaf zinc concentration, but also decreased fresh and dry weights of shoots. In addition, necrosis was observed in leaves at a zinc concentration in the nutrient solution of ≥0.15 mM. By contrast, high wind speed and root zone temperature led to increases in leaf zinc concentration, but slightly decreased fresh and dry weights of shoots. In addition, under high wind speed and root zone temperature, no leaf necrosis was observed. The increase in leaf zinc concentration was attributed to increases in zinc absorption, accompanied by increased transpiration, because water absorption rates increased with wind speed and root zone temperature. Therefore, baby leaf red oak lettuce with high leaf zinc concentration can be produced by increasing wind speed and root zone temperature.View full abstractDownload PDF (257K)
Erratum
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Takafumi MIYAMA, Hiroyuki TOBITA, Kentaro UCHIYAMA, Kenichi YAZAKI, Sa ...2018 Volume 74 Issue 4 Pages 178
Published: 2018
Released on J-STAGE: October 10, 2018
Advance online publication: June 24, 2018JOURNAL FREE ACCESSThe Editorial Office of Journal of Agricultural Meteorology wishes to correct the errors in this paper (Miyama et al. 2018). Figure 3 and the caption should be as followed.View full abstractDownload PDF (207K)
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