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Open AccessArticle

Stress Distribution Analysis on Hyperspectral Corn Leaf Images for Improved Phenotyping Quality

Department of Agricultural and Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USA
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Sensors 2020, 20(13), 3659; https://doi.org/10.3390/s20133659
Received: 9 June 2020 / Revised: 25 June 2020 / Accepted: 28 June 2020 / Published: 30 June 2020
(This article belongs to the Section Sensing and Imaging)
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. View Full-Text
Keywords: corn leaf; hyperspectral imaging; plant phenotyping; leaf stress distribution; machine learning algorithms corn leaf; hyperspectral imaging; plant phenotyping; leaf stress distribution; machine learning algorithms
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Ma, D.; Wang, L.; Zhang, L.; Song, Z.; U. Rehman, T.; Jin, J. Stress Distribution Analysis on Hyperspectral Corn Leaf Images for Improved Phenotyping Quality. Sensors 2020, 20, 3659.

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