Testing the suitability of automated machine learning, hyperspectral imaging and CIELAB color space for proximal in situ fertilization level classification

Abstract

Automated machine learning (AutoML) is considered the next advancement in artificial intelligence due to its commitment to delivering high-performance end-to-end machine learning pipelines with minimal user input. Although AutoML has demonstrated significant potential in computer vision tasks, as far as current knowledge extends, there is currently no study that has applied AutoML specifically to hyperspectral imaging for fertilization level classification. To address this information gap, the use of AutoML for classifying fertilization levels using a hyperspectral and CIELAB color space dataset was investigated. A comparative analysis was conducted between the performance of an open-source AutoML framework, PyCaret, and traditional machine learning using the PLS-DA algorithm. PyCaret achieved the highest accuracy (1.00) in classifying different fertilization levels using the hyperspectral dataset, while PLS-DA attained an accuracy of 0.91. However, the CIELAB dataset was not as effective for this classification task, achieving an accuracy of only 0.72. It is worth noting that the hyperspectral dataset outperformed the CIELAB dataset in both AutoML and PLS-DA analyses. Finally, the findings suggest that AutoML holds substantial potential to enhance the use of hyperspectral imaging in agriculture, particularly for fertilization tasks.

Keywords

• Hyperspectral Imaging
• AutoML
• Broccoli
• Fertilization
• Artificial intelligence
• CIELAB color space