Ryoichi Doi

Simple luminosity normalization of greenness, yellowness and redness/greenness for comparison of leaf spectral profiles in multi-temporally acquired remote sensing images

Observation of leaf colour (spectral profiles) through remote sensing is an effective method of identifying the spatial distribution patterns of abnormalities in leaf colour, which enables appropriate plant management measures to be taken. However, because the brightness of remote sensing images varies with acquisition time, in the observation of leaf spectral profiles in multi-temporally acquired remote sensing images, changes in brightness must be taken into account. This study identified a simple luminosity normalization technique that enables leaf colours to be compared in remote sensing images over time. The intensity values of green and yellow (green + red) exhibited strong linear relationships with luminosity (R2 > 0.926) when various invariant rooftops in Bangkok or Tokyo were spectral-profiled using remote sensing images acquired at different time points. The values of the coefficient and constant or the coefficient of the formulae describing the intensity of green or yellow were comparable among the single Bangkok site and the two Tokyo sites, indicating the technique’s general applicability. For single rooftops, the values of the coefficient of variation for green, yellow, and red/green were 16% or less (n = 6 − 11), indicating an accuracy not less than those of well-established remote sensing measures such as the normalized difference vegetation index. After obtaining the above linear relationships, raw intensity values were normalized and a temporal comparison of the spectral profiles of the canopies of evergreen and deciduous tree species in Tokyo was made to highlight the changes in the canopies’ spectral profiles. Future aspects of this technique are discussed herein.

Discriminating crop and other canopies by overlapping binary image layers

Abstract. For optimal management of agricultural fields by remote sensing, discrimination of the crop canopy from weeds and other objects is essential. In a digital photograph, a rice canopy was discriminated from a variety of weed and tree canopies and other objects by overlapping binary image layers of red-green-blue and other color components indicating the pixels with target canopy-specific (intensity) values based on the ranges of means ±(3×) standard deviations. By overlapping and merging the binary image layers, the target canopy specificity improved to 0.0015 from 0.027 for the yellow 1× standard deviation binary image layer, which was the best among all combinations of color components and means ±(3×) standard deviations. The most target rice canopy-likely pixels were further identified by limiting the pixels at different luminosity values. The discriminatory power was also visually demonstrated in this manner.

Feasibility of system of rice intensification practices in natural and socioeconomic contexts in Thailand

The system of rice intensification (SRI) is a set of practices in rice cultivation known for higher yields, lower water consumption, and environmentally friendly effects. However, few Thai farmers have adopted SRI even though Thailand is one of the largest rice producing nations. In this study, rice farmers in the northeastern, central, and southern regions of Thailand provided open-ended responses to descriptions of SRI prepared by the Thai government. Of the 66 rice farmers sampled, 11 positive comments were made, while 93 critical comments were directed at the time-critical and labor intensive practices necessary for SRI. Comments were especially critical of the labor intensiveness of the whole system, the slow response by rice to organic matter application, and the necessity of manual transplanting. Based on these results, it was concluded that changing the water regime would be the most feasible among the SRI practices for the farmers. The research incorporates a discussion of the natural and socioeconomic contexts in which the labor-intensive and time-consuming practices are criticized by the rice farmers.

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.

Quantification of leaf greenness and leaf spectral profile in plant diagnosis using an optical scanner

Observation of leaf spectral profile (color) enables suitable management measures to be taken for crop production. An optical scanner was used: 1) to obtain an equation to determine the greenness of plant leaves and 2) to examine the power to discriminate among plants grown under different nutritional conditions. Sweet basil seedlings grown on vermiculite were supplemented with one-fifth-strength Hoagland solutions containing 0, 0.2, 1, 5, 20, and 50 mM NH4+. The 5 mM treatment resulted in the greatest leaf and shoot weights, indicating a quadratic growth response pattern to the NH4+ gradient. An equation involving b*, black and green to describe the greenness of leaves was provided by the spectral profiling of a color scale for rice leaves as the standard. The color scale values for the basil leaves subjected to 0.2 and 1 mM NH4+ treatments were 1.00 and 1.12, respectively. The other treatments resulted in significantly greater values of 2.25 to 2.42, again indicating a quadratic response pattern. Based on the spectral data set consisting of variables of red-green-blue and other color models and color scale values, in discriminant analysis, 81% of the plants were correctly classified into the six NH4+ treatment groups. Combining the spectral data set with the growth data set consisting of leaf and shoot weights, 92% of the plant samples were correctly classified whereas, using the growth data set, only 53% of plants were correctly classified. Therefore, the optical scanning of leaves and the use of spectral profiles helped plant diagnosis when biomass measurements were not effective.

Red-and-green-based pseudo-RGB color models for the comparison of digital images acquired under different brightness levels

Digital images of crops and other plants are useful in plant management although the brightness of digital images varies with time. Brightness adjustment of the entire area of color digital images has been quite difficult due to the absence of correlations among color components and brightness. Redness and greenness best correlate with brightness. Hence, red- and green-based pseudo-color models could enable the adjustment of brightness of the entire area of color digital images. A pseudo-color model, a red–green–white mat model, lost most of the information on yellowness when tested with a color gamut. However, a red–green-inverted [red + green] model largely retained the original information. In this color model, the intensity of blue becomes the indicator of darkness. As a complementary color model, a red–green–[red + green] color model was examined. The color model provided pseudo-color images carrying information on redness, greenness, and yellowness. By comparing grayscale images of a single color component such as key black for the pseudo-color models, the pseudo-color models were shown to provide measures that are dimensionally independent of one another. Thus, together with the original red–green–blue (RGB) color image, the pseudo-color images almost triple the amount of information carried by the grayscale images. These multidimensional pieces of information are expected to facilitate the observation of colors of plants through digital image acquisition.

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.

Precise micromolar-level glucose determination using a glucose test strip for quick and approximate millimolar-level estimation

A variety of test strips for the quick and rough estimation of analytes are commonly and reasonably available. Although the quickness is one of the advantages of test strips, the detection limit can be assumed to be much greater than the actually achievable values, because the quickness suggests the possibility of increasing the sensitivity by extending the reaction time. In this study, a home-use test strip product for urinary glucose detection was examined. When the reaction time of 30 seconds indicated in the instructions was extended to be three hours, the sensitivity increased 56-fold. A detection limit of 3.7 μM glucose was achieved, while the lowest concentration for colour production shown in the instructions was 2.78 mM. The sensitivity is moderately good among the various methods for glucose determination and is comparable to that of widely used high performance liquid chromatography and capillary electrophoresis and recently emerging non-enzymatic sensors. The application was demonstrated by determining glucose generated by the hydrolysis of cellulose in soil–water–cellulose suspensions. The sensitivity-increasing utilization of the convenient, reagentless, safe, cost-effective, and thus highly practical product by extending the originally-instructed reaction time is reported herein.

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.