Toward Automated Machine Learning-Based Hyperspectral Image Analysis in Crop Yield and Biomass Estimation

Abstract

The incorporation of autonomous computation and artificial intelligence (AI) technologies into smart agriculture concepts is becoming an expected scientific procedure. The airborne hyperspectral system with its vast area coverage, high spectral resolution, and varied narrow-band selection is an excellent tool for crop physiological characteristics and yield prediction. However, the extensive and redundant three-dimensional (3D) cube data processing and computation have made the popularization of this tool a challenging task. This research integrated two important open-sourced systems (R and Python) combined with automated hyperspectral narrowband vegetation index calculation and the state-of-the-art AI-based automated machine learning (AutoML) technology to estimate yield and biomass, based on three crop categories (spring wheat, pea and oat mixture, and spring barley with red clover) with multifunctional cultivation practices in northern Europe and Estonia. Our study showed the estimated capacity of the empirical AutoML regression model was significant. The best coefficient of determination (R2) and normalized root mean square error (NRMSE) for single variety planting wheat were 0.96 and 0.12 respectively; for mixed peas and oats, they were 0.76 and 0.18 in the booting to heading stage, while for mixed legumes and spring barley, they were 0.88 and 0.16 in the reproductive growth stages. In terms of straw mass estimation, R2 was 0.96, 0.83, and 0.86, and NRMSE was 0.12, 0.24, and 0.33 respectively. This research contributes to, and confirms, the use of the AutoML framework in hyperspectral image analysis to increase implementation flexibility and reduce learning costs under a variety of agricultural resource conditions. It delivers expert yield and straw mass valuation two months in advance before harvest time for decision-makers. This study also highlights that the hyperspectral system provides economic and environmental benefits and will play a critical role in the construction of sustainable and intelligent agriculture techniques in the upcoming years.

Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops

“…The potential for high-throughput pigment phenotyping to evaluate and track growth and development, biophysical and biochemical characteristics, and diseases in crop production has been demonstrated in recent studies [ 1 , 2 , 3 , 8 , 13 ]. Spectral variations in pigment concentrations, including chlorophyll a and b , are closely correlated with differences in agronomic traits, such as plant height, grain yield, growth cycle, photosynthesis, transpiration, and water use efficiency [ 6 , 23 , 30 , 31 ].…”

“…Integrating hyperspectral sensors with chemometric techniques has proven effective for identifying and predicting a range of crop characteristics. For example, many studies have demonstrated the use of hyperspectral analyses to differentiate between livestock-integrated farming systems and indoor and vertical farming productions and to determine crop leaf characteristics [ 6 , 32 , 33 , 34 ]. Thus, our first and second objective proposed methods have also shown potential for increasing yield production in crops such as wheat and canola.…”

“…The Green Revolution of the 1960s, as well as the recent Digital Revolutions 4.0 and 5.0 [ 6 , 7 ], saw technological advancements applied to high-throughput analyses, contributing to an increase in food production for several agronomically important crops. This advancement allows decision-makers to utilize computational intelligence to enhance the classification, monitoring, and nutritional value of crops in fields, resulting in billions of dollars in annual economic benefits [ 13 ].…”

“…The application of remote sensing 5.0 techniques has seen an increase in efforts to estimate the use of machine learning, data mining, and deep learning to classify crops such as corn and soybean [ 23 ], wheat [ 6 ], coffee [ 24 ], and lettuce [ 14 ]. The use of UV–VIS–NIR–SWIR hyperspectral imaging is expected to be highly valuable [ 8 , 25 ].…”

“…The use of UV–VIS–NIR–SWIR hyperspectral imaging is expected to be highly valuable [ 8 , 25 ]. Furthermore, the combination of the speed, precision, and accuracy [ 26 , 27 , 28 ] of hyperspectral equipment [ 23 ] coupled with the capacity of multivariate and artificial intelligence algorithm tools is crucial for pigment profiling crop modeling [ 5 , 6 , 7 ].…”