Unbiasing the Estimation of Chlorophyll from Hyperspectral Images: A Benchmark Dataset, Validation Procedure and Baseline Results

Abstract

Recent advancements in hyperspectral remote sensing bring exciting opportunities for various domains. Precision agriculture is one of the most widely-researched examples here, as it can benefit from the non-invasiveness and enormous scalability of the Earth observation solutions. In this paper, we focus on estimating the chlorophyll level in leaves using hyperspectral images—capturing this information may help farmers optimize their agricultural practices and is pivotal in planning the plants’ treatment procedures. Although there are machine learning algorithms for this task, they are often validated over private datasets; therefore, their performance and generalization capabilities are virtually impossible to compare. We tackle this issue and introduce an open dataset including the hyperspectral and in situ ground-truth data, together with a validation procedure which is suggested to follow while investigating the emerging approaches for chlorophyll analysis with the use of our dataset. The experiments not only provided the solid baseline results obtained using 15 machine learning models over the introduced training-test dataset splits but also showed that it is possible to substantially improve the capabilities of the basic data-driven models. We believe that our work can become an important step toward standardizing the way the community validates algorithms for estimating chlorophyll-related parameters, and may be pivotal in consolidating the state of the art in the field by providing a clear and fair way of comparing new techniques over real data.

Keywords

• hyperspectral imaging
• machine learning
• chlorophyll estimation
• benchmark
• validation