# Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories
## 1 Introduction  
Plant factories are widely used for tomato cultivation. The corresponding automation and precise management are also becoming increasingly important issues. In particular, precise irrigation management is a challenge. To ensure that the plants have adequate moisture, irrigation management is improved by making adjustments considering the growth stage and status of the tomato; however, the solution waste rate remains at approximately 20-30%, which causes considerable wastage of nutrition solution and labor. To achieve more precise irrigation management, many studies have focused on the accurate monitoring the leaf water status dynamics in real time through nondestructive measurements. In recent decades, with the advancements in spectroscopy and imaging technologies, hyperspectral imaging (HSI) technology has emerged as a highly efficient nondestructive measurement method. This technology was originally developed for remote sensing and is widely used in resource management, agriculture, mineral exploration, and environmental monitoring [1; 2; 3]. Certain properties of the tomato leaf enable the possibility of assessing its water status using HSI. Tomato lose more than 90% of the water they absorb by transpiration from their leaves [4]; therefore, the leaves are more susceptible to water stress than other organs, such as the fruit. Leaf water stress is usually indicated by the leaf water status, including the leaf water content (WC) and equivalent water thickness (MC) [5; 6]. There are differences in terms of the physics between WC and MC, where WC is a percentage, which can be calculated based on the fresh and dry weights of a tomato leaf (FW and DW, respectively), while MC has units of weight per area (g/cm\({}^{2}\) or g/dm\({}^{2}\)) and is calculated based on the weight of the water contained in the leaf and the leaf area (LA) [7]. Previous studies have shown that the histological structures and specific components of plant leaves are the main factors affecting the reflectance spectral characteristics. The high reflectance in the near-infrared band of 780-1300 nm is due to the structure of plant tissues and cells. The low reflectance above a wavelength of 1300 nm is due to the absorption of water. Furthermore, numerous studies have indicated that there are four water absorption bands for all angiosperm leaves, which occur at wavelengths of approximately 970, 1200, 1400, and 1920 nm [2; 3; 8; 9]. These discoveries enable water status assessment using HSI. In HSI, information from across the electromagnetic spectrum is collected and processed, and conventional imaging and spectroscopy are integrated to obtain both spatial and spectral information from an object. This information can be used to characterize objects with great precision and detail. However, preprocessing of hyperspectral data is necessary to reduce random noise and improve the accuracy of the assessment model. The preprocessing methods employed for this purpose include normalization processing [2; 10]; the spectral derivative technique, including first-order, second-order, and fractional differential processing [2; 11; 12]; and the logarithmic transformation of the inverse of the reflectance [10; 12; 13], among others. To establish a water status assessment model based on hyperspectral data, numerous researchers have focused on hyperspectral indices using two wavelength bands, such as (1) \(\lambda_{1}/\lambda_{2}\), which is also called the water index [14], sample ratio water index [15], simple ratio water index [16], or moisture stress index (MSI) [17]; and (2) \((\lambda_{1}-\lambda_{2})/(\lambda_{1}+\lambda_{2})\), which is called the normalized difference water index (normalized difference vegetation index (NDVI) [18], normalized difference infrared index (NDII) [19], normalized difference water index [20], normalized multi-band drought index [21], or short-wave infrared water stress index [22].