Hyperspectral reflectance sensing for quantifying leaf chlorophyll content in wasabi leaves using spectral pre-processing techniques and machine learning algorithms

Changes in chlorophyll content can be a good indicator of disease as well as nutritional and environmental stresses on plants. Several pre-processing techniques have been proposed for reducing noise from spectral data to identify vegetation properties such as chlorophyll content. Machine learning algorithms have also been applied to assess biochemical properties; however, an approach integrating pre-processing techniques and machine learning algorithms has not been fully evaluated. Therefore, this study evaluates the effectiveness of five pre-processing techniques used in conjunction with five machine learning algorithms for estimating chlorophyll content in two wasabi cultivars. Overall, incorporating pre-processing techniques was effective for obtaining estimated values with high accuracy. Analyses utilizing both pre-processing and machine learning performed best in 88 of 100 repetitions. The kernel-based extreme learning machine (KELM) and Cubist algorithms yielded the highest performance and achieved the highest accuracies in 54 and 26 of 100 repetitions, respectively.

Automated summary

This study evaluated the effectiveness of five pre-processing techniques combined with five machine learning algorithms for estimating chlorophyll content in two wasabi cultivars. The integration of pre-processing techniques was found to be effective in obtaining estimated values with high accuracy, with analyses utilizing both pre-processing and machine learning performing best in 88 of 100 repetitions. Kernel-based extreme learning machine (KELM) and Cubist algorithms yielded the highest performance and achieved the highest accuracies in 54 and 26 of 100 repetitions, respectively.