Abstract
High-throughput imaging technologies have been developing rapidly for agricultural plant phenotyping purposes. With most of the current crop plant image processing algorithms, the plant canopy pixels are segmented from the images, and the averaged spectrum across the whole canopy is calculated in order to predict the plant’s physiological features. However, the nutrients and stress levels vary significantly across the canopy. For example, it is common to have several times of difference among Soil Plant Analysis Development (SPAD) chlorophyll meter readings of chlorophyll content at different positions on the same leaf. The current plant image processing algorithms cannot provide satisfactory plant measurement quality, as the averaged color cannot characterize the different leaf parts. Meanwhile, the nutrients and stress distribution patterns contain unique features which might provide valuable signals for phenotyping. There is great potential to develop a finer level of image processing algorithm which analyzes the nutrients and stress distributions across the leaf for improved quality of phenotyping measurements. In this paper, a new leaf image processing algorithm based on Random Forest and leaf region rescaling was developed in order to analyze the distribution patterns on the corn leaf. The normalized difference vegetation index (NDVI) was used as an example to demonstrate the improvements of the new algorithm in differentiating between different nitrogen stress levels. With the Random Forest method integrated into the algorithm, the distribution patterns along the corn leaf’s mid-rib direction were successfully modeled and utilized for improved phenotyping quality. The algorithm was tested in a field corn plant phenotyping assay with different genotypes and nitrogen treatments. Compared with the traditional image processing algorithms which average the NDVI (for example) throughout the whole leaf, the new algorithm more clearly differentiates the leaves from different nitrogen treatments and genotypes. We expect that, besides NDVI, the new distribution analysis algorithm could improve the quality of other plant feature measurements in similar ways.
Highlights
High-throughput imaging sensor technologies have been well established for measuring crop growth status in fields or indoors
The traditional averaged normalized difference vegetation index (NDVI) values from the leaf images were compared between the The traditional averaged NDVI values from the leaf images were compared between the different different nitrogen treatments and genotypes
AdaBoost, Logistic Regression, Partial Least Squares Regression (PLSR), and Random Forest all had smaller two-sample t-test P-values leaves from different nitrogen treatments
Summary
High-throughput imaging sensor technologies have been well established for measuring crop growth status in fields or indoors. Plant imaging models have been developed to predict plants’. In conventional plant phenotyping analyses, most studies rely on the averaged spectrum across all the segmented plant pixels of the whole plant [2,4,8,9,10,11,12]. Researchers firstly segment the target plant tissue from the image. The averaged spectrum across all the plant pixels is utilized for prediction purposes.
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