Abstract
Improving the speed and accuracy of chlorophyll (Ch1) content prediction in different light areas of apple trees is a central priority for understanding the growth response to light intensity and in turn increasing the primary production of apples. In vitro assessment by wet chemical extraction is the standard method for leaf chlorophyll determination. This measurement is expensive, laborious, and time-consuming. Over the years, alternative methods—both rapid and nondestructive—were explored, and many vegetation indices (VIs) were developed to retrieve Ch1 content at the canopy level from meter- to decameter-scale reflectance observations, which have lower accuracy due to the possible confounding influence of the canopy structure. Thus, the spatially continuous distribution of Ch1 content in different light areas within an apple tree canopy remains unresolved. Therefore, the objective of this study is to develop methods for Ch1 content estimation in areas of different light intensity by using 3D models with color characteristics acquired by a 3D laser scanner with centimeter spatial resolution. Firstly, to research relative light intensity (RLI), canopies were scanned with a FARO Focus3D 120 laser scanner on a calm day without strong light intensity and then divided into 180 cube units for each canopy according to actual division methods in three-dimensional spaces based on distance information. Meanwhile, four different types of RLI were defined as 0–30%, 30–60%, 60–85%, and 85–100%, respectively, according to the actual division method for tree canopies. Secondly, Ch1 content in the 180 cubic units of each apple tree was measured by a leaf chlorophyll meter (soil and plant analyzer development, SPAD). Then, color characteristics were extracted from each cubic area of the 3D model and calculated by two color variables, which could be regarded as effective indicators of Ch1 content in field crop areas. Finally, to address the complexity and fuzziness of relationships between the color characteristics and Ch1 content of apple tree canopies (which could not be expressed by an accurate mathematical model), a three-layer artificial neural network (ANN) was constructed as a predictive model to find Ch1 content in different light areas in apple tree canopies. The results indicated that the mean highest and mean lowest value of Ch1 content distributed in 60–85% and 0–30% of RLI areas, respectively, and that there was no significant difference between adjacent RLI areas. Additionally, color characteristics changed regularly as the RLI rose within canopies. Moreover, the prediction of Ch1 content was strongly correlated with those of actual measurements (R = 0.9755) by the SPAD leaf chlorophyll meter. In summary, the color characteristics in 3D apple tree canopies combined with ANN technology could be used as a potential rapid technique for predicting Ch1 content in different areas of light in apple tree canopies.
Highlights
Photosynthesis is one of the most important biochemical processes on the planet, allowing life to survive on Earth through the production of light carbon reactions under visible light irradiation [1]
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All linear best fits (R = 0.9755) between the predicted results and actual values measured with the SPAD leaf chlorophyll meter showed that the effective Ch1 content was accurately predicted by this artificial neural network (ANN) network through the error back-propagation that was trained by the Levenberg–Marquardt method [27]
Summary
Photosynthesis is one of the most important biochemical processes on the planet, allowing life to survive on Earth through the production of light carbon reactions under visible light irradiation [1]. Leaf Ch1 content is conventionally measured directly through wet chemical methods, such as pigment extraction in an organic solvent and spectrophotometric determination of absorbance in the chlorophyll solution [6]. These laboratory-based methods are accurate in obtaining Ch1 content, they have limitations. The very high price and time-consuming nature of these methods have prevented their widespread application Handheld devices such as SPAD [7] are some of the most useful contact sensors for rapid and non-destructive determination of Ch1 content in many plants. New approaches are highly desirable for determining Ch1 content with non-invasive measurements of apple trees under different light intensities within canopies [8]
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