Abstract
The accuracy of detecting the chlorophyll content in the canopy and leaves of citrus plants based on sensors with different scales and prediction models was investigated for the establishment of an easy and highly-efficient real-time nutrition diagnosis technology in citrus orchards. The fluorescent values of leaves and canopy based on the Multiplex 3.6 sensor, canopy hyperspectral reflectance data based on the FieldSpec4 radiometer and spectral reflectance based on low-altitude multispectral remote sensing were collected from leaves of Shatang mandarin and then analyzed. Additionally, the associations of the leaf SPAD (soil and plant analyzer development) value with the ratio vegetation index (RVI) and normalized differential vegetation index (NDVI) were analyzed. The leaf SPAD value predictive model was established by means of univariate and multiple linear regressions and the partial least squares method. Variable distribution maps of the relative canopy chlorophyll content based on spectral reflectance in the orchard were automatically created. The results showed that the correlations of the SPAD values obtained from the Multiplex 3.6 sensor, FieldSpec4 radiometer and low-altitude multispectral remote sensing were highly significant. The measures of goodness of fit of the predictive models were R2=0.7063, RMSECV=3.7892, RE=5.96%, and RMSEP=3.7760 based on RVI(570/800) and R2=0.7343, RMSECV=3.6535, RE=5.49%, and RMSEP=3.3578 based on NDVI[(570,800)(570,950)(700,840)]. The technique to create spatial distribution maps of the relative canopy chlorophyll content in the orchard was established based on sensor information that directly reflected the chlorophyll content of the plants in different parts of the orchard, which in turn provides evidence for implementation of orchard productivity evaluation and precision in fertilization management. Keywords: citrus, remote sensing, bio-sensor, chlorophyll detection, spectrum, ratio vegetation index (RVI), normalized differential vegetation index (NDVI), spatial distribution map DOI: 10.25165/j.ijabe.20181102.3189 Citation: Wang K J, Li W T, Deng L, Lyu Q, Zheng Y Q, Yi S L, et al. Rapid detection of chlorophyll content and distribution in citrus orchards based on low-altitude remote sensing and bio-sensors. Int J Agric & Biol Eng, 2018; 11(2): 164–169.
Highlights
The chlorophyll content of citrus canopy leaves and plants is directly associated with the level of photosynthetic capacity and carbohydrate synthesis of the plants and forms a basic vital condition and nutritional support for high quality tree yields[1,2]
The fluorescent values of leaves and canopy based on the Multiplex 3.6 sensor, canopy hyperspectral reflectance data based on the FieldSpec4 radiometer and spectral reflectance based on low-altitude multispectral remote sensing were collected from leaves of Shatang mandarin and analyzed
The average relative error of the prediction set RE was 9.03%, and the root mean square error (RMSE) was 5.4803. 3.2 SPAD value of the citrus canopy predicted by the Multiplex 3.6 sensor
Summary
The chlorophyll content of citrus canopy leaves and plants is directly associated with the level of photosynthetic capacity and carbohydrate synthesis of the plants and forms a basic vital condition and nutritional support for high quality tree yields[1,2]. The timely diagnosis and investigation of chlorophyll content and its spatial distribution in a citrus orchard are important and necessary for nutrition diagnosis, production capacity evaluation and scientific fertilization decisions. It is difficult to actualize the real-time and dynamic analysis of the change in chlorophyll content in large areas and to obtain the chlorophyll distribution in a tree canopy or individual plants. Easy and accurate acquisition of the chlorophyll content information of different individual plants, or of plants in different parts of a citrus orchard, and understanding the chlorophyll content level and spatial distribution in an entire orchard in time are important for chlorophyll rapid diagnosis and precise plant fertilization in fruit trees. With the availability of practical sensors and general techniques, it has become easy to understand the photosynthesis ability of the plants and evaluate the nutritional level of fruit trees[3]
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