Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories

applied sciences

Article Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories

Tiejun Zhao 1,*, Akimasa Nakano 2, Yasunaga Iwaski 3 and Hiroki Umeda 4 1 Department of Agro-Food Science, Niigata Agro-Food University, Faculty of Agro-Food Science, Tainai Campus, Hiranedai 2416, Tainai, Niigata 959-2702, Japan 2 Chiba University Innovation Management Organization, Chiba University, Kashiwano-ha Campus 6-2-1, Kashiwano-ha, Kashiwa-shi, Chiba 277-0882, Japan; [email protected] 3 Tohoku Agricultural Research Center, NARO, 4 Akahira, Shimo-kuriyagawa, Morioka, Iwate 020-0198, Japan; iwasakiy@aﬀrc.go.jp 4 College of Bioresource Sciences, Nihon University, 1866 Kameino, Fujisawa, Kanagawa 252-0880, Japan; [email protected] * Correspondence: [email protected]; Tel.: +81-0254-28-9830

Received: 10 May 2020; Accepted: 2 July 2020; Published: 6 July 2020

Abstract: Irrigation management continues to be an important issue for tomato cultivation, especially in plant factories. Accurate and timely assessment of tomato leaf water status is a key factor in enabling appropriate irrigation, which can save nutrition solution and labor. In recent decades, hyperspectral imaging has been widely used as a nondestructive measurement method in agriculture to obtain plant biological information. The objective of this research was to establish an approach to obtain the tomato leaf water status—specifically, the relative water content (WC) and equivalent water thickness (MC)—for five different tomato cultivars in real time by using hyperspectral imaging. The normalized difference vegetation index (NDVI) and two-band vegetation index (TBI) analyses were performed on the tomato leaf raw relative reflection (RAW), the inversion-logarithm relative reflection (LOG), and the first derivative of relative reflection (DIFF) from wavelengths of 900 nm to 1700 nm. The best regression model for WC assessment was obtained by TBI regression using DIFF at wavelengths of 1410 nm and 1520 nm, and the best regression model for MC assessment was obtained by NDVI regression using RAW at wavelengths of 1300 nm and 1310 nm. Higher model performance was obtained with MC assessment than with WC assessment. The results will help improve our understanding of the relationship between hyperspectral reflectance and leaf water status.

Keywords: hyperspectral imaging; tomato leaf; water status; normalized diﬀerence vegetation index; two-band index

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 [1–3].

Appl. Sci. 2020, 10, 4665; doi:10.3390/app10134665 www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, 4665 2 of 18

Certain properties of the tomato leaf enable the possibility of assessing its water status using HSI. Tomatoes 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/cm2 or g/dm2) 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) λ1/λ2, 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) (λ λ )/(λ + λ ), which is called the normalized difference water 1 − 2 1 2 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]. In addition, new wavelength combinations for leaf water status (WC and MC) are constantly being proposed using the above model expression [2,7]. However, the relationships between the wavelength bands and water status indicators (WC and MC) have not been well investigated to date. Furthermore, the study of different objects requires the use of different wavelength combinations that correspond to them. Based on the aforementioned background, the main objectives of this research were to (1) perform statistical analysis on the leaf water status (WC and MC); (2) investigate the impact of hyperspectral data preprocessing on improving the model accuracy, and identify the best combination of wavelength bands for leaf water status (WC and MC) assessment based on the NDVI and two-band index (TBI); (3) evaluate the performance of the leaf water status model based on various types of indices; and (4) establish an approach for obtaining the tomato leaf water status (WC and MC) in real time using HSI technology for tomato plant irrigation management in plant factories.

2. Materials and Methods

2.1. Research Site and Tomato Cultivation The experiment in this study was conducted in the sunlight-type plant factory of the National Agriculture and Food Research Organization (36.025399◦ N, 140.101125◦ E), Tsukuba, Ibaraki, Japan. The eave height of the plant factory was 5.1 m, and the total area was 2551 m2 (63 m 40.5 m). × The whole building was covered with an ethylene tetrafluoroethylene film (F-clean GR diffused type, AGC Greentech, Tokyo, Japan), including the roof and the glass on the walls. The plant factory was Appl. Sci. 2020, 10, 4665 3 of 18

Appl. Sci. 2020, 10, x FOR PEER REVIEW 3 of 18 divided into eight small compartments to accommodate several hydroponic systems and crops [23]. Oneinto smalleight compartmentsmall compartments with a growingto accommodate area of 162 several m2 (9 hydroponic m 18 m) was system employeds and crops for cultivating [23]. One thesmall × tomatocompartment plants usedwith ina growing this experiment. area of The162 m Ubiquitous2 (9 m × 18 Environment m) was employed Control for System cultivating was adopted the tomato to controlplants used the environment in this experiment. in the growingThe Ubiquitous house. TheEnvironment temperature Control target System was set was between adopted 14 to◦C control and 25the◦ C.environment An automatic in the shade growing curtain house (LS. screen, The temperature XLS16, Seiwa target Co., was Ltd., set Tochigi, between Japan) 14 °C wasand used25 °C. to An shieldautomatic the lightshade when curtain the (LS outdoor screen, solar XLS16, radiation Seiwa was Co., greater Ltd., Tochigi than 1.2, Japan kW m) was2. used to shield the light · − whenThe the tomato outdoor plant solar samples radiation were was selectedgreater than from 1.2 five kW·m tomato−2. cultivars for investigation in this experiment.The tomato They plant included samples “CF Momotarou”, were selected “Rinnka from five 409”, tomato and “DR03-103”, cultivars forwhich investigation are Japanese in this tomatoexperiment cultivars. They that included are produced “CF Momotarou for consumption”, “Rinnka as 409 raw”, food;and “ andDR03 “Tomimaru-103”, which mucyo” are Japanese and “Endeavour”,tomato cultivars which that are are tomato produced cultivars for from consumption the Netherlands as raw that food are; producedand “Tomimaru for processing mucyo and” and cooking.“Endeavour All” of, which the samples are tomato were cultivars seeded onfrom July the 13, Netherlands 2016. After that three are days produced of processing for processing under and a darkcooking condition. All of atthe 28 samples◦C, with were hastening seeded by on sprouting July 13, 2016. equipment, After three all the days tomato of processing plants were under moved a dark tocondition a “Nae Terrace”at 28 °C, with to raise hastening the seedlings by sprouting for 21 equipment, days. They all were the thentomato transplanted plants were in moved sunlight to a in “Nae a plant-factoryTerrace” to raise growing the seedling house ons for August 21 days 12. after They secondary were then raising transplanted of the seedlings in sunlight in thein agrow plant room.-factory Agrowing rock wall house slab on and Aug highust wire 12 after (3.4 m)secondary were adopted raising inof thisthe experiment.seedlings in Thethe grow nutrient room. solution A rock was wall 1 1 initiallyslab and set high to 1.0wire dS /(3.4m− m)and were then adopted gradually in increasedthis experiment to 2.8. dS The/m −nutrient. The fruit solution setting was was initially increased set to by1.0 using dS/m 4-cholorophenoxy−1 and then gradually acetic increased acid (4-CPA), to 2.8 a dS/m plant−1 growth. The fruit hormone setting (0.15%, was increased Ishihara), by until using the 4- thirdcholorophenoxy fruit setting. acetic Then acid the pollination (4-CPA), a was plant conducted growth hormone by bees. (0.15%, Ishihara), until the third fruit setting. Then the pollination was conducted by bees. 2.2. Tomato Leaf Sampling and Leaf Water Content Measurement 2.2. Tomato Leaf Sampling and Leaf Water Content Measurement For nondestructive measurement of the tomato leaf water status using a portable hyperspectral camera,For the nondestructive measuring zone measurement was divided of into the three tomato parts leaf (Figure water1): status the top using leaf positiona portable (LP) hyperspectral (near the growingcamera, the point, measuring 250 cm fromzone thewas ground, divided LP-1), into three middle parts leaf (Figure position 1): (150the top cm leaf from position the ground, (LP) (near LP-2), the andgrowing bottom point, leaf 250 position cm from (60 cmthe fromground the, ground,LP-1), middle LP-3). leaf The position distribution (150 cm diff erencesfrom the of ground the tomato, LP-2), plantsand bottom under aleaf year-round position cultivation(60 cm from condition the ground were, considered.LP-3). The distribution All three plants difference from thes of five the tomato tomato cultivarsplants under described a year above-round were cultivation chosen condition for the tomato were leafconsidered sampling.. All three plants from the five tomato cultivars described above were chosen for the tomato leaf sampling.

FigureFigure 1.1. OverviewOverview of of the the tomato tomato leaf leaf sampling sampling at varying at varying leaf leaf positions positions (LPs) (LPs).. LP-1: LP-1: Top; LP Top;-2: Middle LP-2: ; Middle;LP-3: Bottom LP-3:. Bottom.

TomatoTomato leafleaf samplingsampling waswas conductedconducted on April 6, 2017 (267(267 days after seeding seeding),), for for a a total total of of 45 45samples. samples. Three Three groups groups worked worked as as a a line line operation for leafleaf samplingsampling to to ensure ensure measurement measurement accuracy accuracy andand totomake make all all the the measurements measurements simultaneous. simultaneous Firstly,. Firstly, the the compound compound leaf leaf was was sampled sampled in in accordance accordance withwith thethe didifferentfferent measuring measuring zones. zones. Secondly, Secondly, the the terminal terminal leaflet leaflet was was separated separate fromd from the the compound compound leaf,leaf, and its fresh fresh weight weight (FW) (FW) was was immediately immediately measured measured.. Thirdly, Thirdly, the the HSI HSI data data of the of terminal the terminal leaflet leafletwere wereobtained obtained using using a hyperspectral a hyperspectral camera. camera. The The leaf leaf sampling sampling was was initiatedinitiated at at 10:00 10:00 a.m. a.m. after after irrigationirrigation andand completedcompleted in two hours.hours. All All leaf leaf samples samples were were dried dried in in a a drying drying machine machine at at 105 105 °C◦C for 72 h. Then, the dry weight (DW) of the terminal leaflet was measured. The tomato leaf WC and equivalent water thickness (MC) were calculated in terms of the fresh weight, dry weight, and leaf area (LA) by using Formulas (1) and (2), respectively, as follows [5–7]: Appl. Sci. 2020, 10, 4665 4 of 18 Appl. Sci. 2020, 10, x FOR PEER REVIEW 4 of 18 for 72 h. Then, the dry weight (DW) of the terminal − leaflet was measured. The tomato leaf WC and WC (%) = × 100% , (1) equivalent water thickness (MC) were calculated in terms of the fresh weight, dry weight, and leaf area (LA) by using Formulas (1) and (2), respectively, as follows [5–7]: − MC = . (2) FW DW WC (%) = − 100%, (1) FW × 2.3. Hyperspectral Imaging and Data Analysis FW DW MC = − . (2) 2.3.1. Leaf Hyperspectral Measurement LA 2.3. HyperspectralA portable Imaginghyperspectral and Data camera Analysis (SIS-I, EBA Japan Co., LTD., Tokyo), standard reflector, and halogen lamp (CHP-500, Caster) were employed to obtain the spectral data of the tomato leaves from 2.3.1.the bottom, Leaf Hyperspectral middle, and Measurementtop leaf positions. The wavelength range of the hyperspectral camera is 900 nm Ato 1700 portable nm, which hyperspectral is within the camera near-infrared (SIS-I, EBA(NIR)Japan region. Co., A dedicated LTD., Tokyo), NIR CCTV standard lens was reflector, set up andin front halogen of the lamp camera (CHP-500, using a C Caster) mount. were A standard employed reflector to obtain (White the Balance spectral, X-Rite data) and of thea halogen tomato leaveslamp fromwere theadopted bottom, in this middle, experiment and top to reduce leaf positions. experimental The wavelengtherrors. The hyperspectral range of the image hyperspectral data of camerathe terminal is 900 leaflet nm to were 1700 immediately nm, which is obtained within the after near-infrared measuring the (NIR) FW region.by using A NI dedicatedR Capture NIR software CCTV lens(SIS was-I, EBA set upJapan in front Co., of LTD., the camera Tokyo). usingThe camera a C mount. was set A standardto a frame reflector rate below (White 80 Balance, fps and to X-Rite) the maximum exposure time of 4.5 ms. The spectral resolution and image resolution of the hyperspectral and a halogen lamp were adopted in this experiment to reduce experimental errors. The hyperspectral camera were 10 nm and 400 × 320 pixels, respectively. image data of the terminal leaflet were immediately obtained after measuring the FW by using NIR Capture2.3.2. Image software Processing (SIS-I, EBAfor Hyperspectral Japan Co., LTD., Data Tokyo). The camera was set to a frame rate below 80 fps and to the maximum exposure time of 4.5 ms. The spectral resolution and image resolution of the hyperspectralHSAnalyzer camera (EBA were Japan 10 Co., nm andLtd.) 400 software320 pixels,was employed respectively. to preprocess the hyperspectral data, whose format was NIR and which were based× on the band interleaved by the line data arrangement 2.3.2.format. Image Further Processing processing for and Hyperspectral data analysis Data were conducted using a MATLAB (MathWorks) program, which was developed for this study. Each leaf image was extracted from the hyperspectral data by imageHSAnalyzer processing (EBA with Japan MATLAB Co., Ltd.). To determin softwaree wasthe employed leaf morphological to preprocess chara thecteristics, hyperspectral a region data, of whoseinterest format (ROI was) was NIR selected and which, and werea single based wavelength on the band of 1390interleaved nm was by used the line to dataconstruct arrangement a two- format.dimensional Further image processing (Figure and 2). data analysis were conducted using a MATLAB (MathWorks) program, whichTo was produce developed a red for–green this study.–blue (RGB) Each leafformat image image was file, extracted wavelengths from the of 1000 hyperspectral nm, 1430 nm, data and by image1530 processingnm were respec with MATLAB.tively chosen To as determine the special the wavelengths leaf morphological to produce characteristics, the individual afake region colors of of interest red, (ROI)green, was and selected, blue. Then, and the a single combined wavelength RGB image of 1390 was nmtransformed was used into to constructa Lab (CIELAB) a two-dimensional color space imageimage (Figure to reduce2). the noise by threshold filtering. Subsequently, the grayscale image (Lab) was converted into a binary image to obtain binary statistics, which were used to calculate the LA (Figure 3).

FigureFigure 2. 2.Hyperspectral Hyperspectral image at a 1390 1390 nm nm wavelength wavelength..

To produce a red–green–blue (RGB) format image ﬁle, wavelengths of 1000 nm, 1430 nm, and 1530 nm were respectively chosen as the special wavelengths to produce the individual fake colors of red, green, and blue. Then, the combined RGB image was transformed into a Lab (CIELAB) color space image to reduce the noise by threshold ﬁltering. Subsequently, the grayscale image (Lab) was converted into a binary image to obtain binary statistics, which were used to calculate the LA (Figure3). Appl. Sci. 2020, 10, 4665 5 of 18 Appl. Sci. 2020, 10, x FOR PEER REVIEW 5 of 18

Appl. Sci. 2020, 10, x FOR PEER REVIEW 5 of 18

(a) (b)

FigureFigure 3. Hyperspectral 3. Hyperspectral image image processing processing and and data data analysis. analysis. ( a()a) Extraction Extraction ofof thethe leafleaf imageimage ((R:R: 10001000 nm, nm, G: 1430 nm, B: 1530 nm). (b) Leaf area calculation. G: 1430 nm, B: 1530 nm). (b) Leaf area calculation.

2.3.3.2.3.3. Hyperspectral Hyperspectral Data Data Analysis Analysis(a) andand Waveband Selection Selection (b) TheTheFigure tomato tomato 3. leafHyperspectral leaf ROI ROI was was 15image 15 ×15 processing15 pixels pixels (225(225 and pixels)pixeldata sanalysis.) and and it it was( wasa) Extraction selected selected fromof fromthe the leaf the tip image tipof each of (R: each terminal1000 terminal leafletnm. For, G: each 1430 nmsample,, B: 1530 the nm spectral).× (b) Leaf data area w calculationere calculated. using the average ROI of the spectral data. leaﬂet. For each sample, the spectral data were calculated using the average ROI of the spectral data. The relative reflectance (Figure 4) and the standardized spectrum of the tomato leaf were calculated for Theeach relative2.3.3. leaf Hyperspectral reﬂectancesample by using (FigureData FAnalysisormulas4) and and the(3) andWaveband standardized (4), respectively Selection spectrum [ 10]. of the tomato leaf were calculated for each leaf sample by using Formulas (3) and (4), respectively [10]. The tomato leaf ROI was 15 × 15 pixels (225 pixels) and it was selected from the tip of each terminal = , (3) leaflet. For each sample, the spectral data were calculatedrlea f using the average ROI of the spectral data. = The relative reflectance (Figure 4) and the standardizedR spectrum, of the tomato leaf were calculated for (3) = rre f er( ) . (4) each leaf sample by using Formulas (3) and (4), respectively [10].

where = 1 , (3) Ni = log( ). (4) : Spectrum of the tomato leaf sample, raw spectral10 R data; : Spectrum of the standard reflector; where = ( ) . (4) R: Relative reflectance of the standard reflector ; rlea f :where Spectrum : Standardized of the tomato spectrum leaf of sample, the tomato raw leaf spectral (i = 1– data;81 wavelength bands). rre f er: Spectrum: Spectrum of the standardof the tomato reflector; leaf sample, raw spectral data; R : Relative reflectance: Spectrum1 of of thethe standardstandard reflector reflector;; R: Relative0.9 reflectance of the standard reflector; Ni : Standardized spectrum of the tomato leaf (i = 1–81 wavelength bands). : Standardized0.8 spectrum of the tomato leaf (i = 1–81 wavelength bands). 0.7 1 0.6 0.9 0.5 0.8 eflectance 0.4 0.7

ive r ive 0.3 0.6 0.2 Relat 0.5 0.1 eflectance 0.4 0

ive r ive 0.3 900 930 960 990

0.2 1020 1050 1080 1110 1140 1170 1200 1230 1260 1290 1320 1350 1380 1410 1440 1470 1500 1530 1560 1590 1620 1650 1680 Relat 0.1 Wavelength (nm)

0 Figure 4. Relative reflectance of the tomato leaf samples. 900 930 960 990 1020 1050 1080 1110 1140 1170 1200 1230 1260 1290 1320 1350 1380 1410 1440 1470 1500 1530 1560 1590 1620 1650 1680 Wavelength (nm)

Figure 4. Relative reﬂectance of the tomato leaf samples. Figure 4. Relative reflectance of the tomato leaf samples. Appl. Sci. 2020, 10, 4665 6 of 18

The first-order derivative of spectral reflectance (DR) and second-order derivative of spectral reflectance (SR), based on the raw reflectance, were calculated using Formulas (5) and (6) with the Norris gap method [24]. ρ(λi+1) ρ(λi 1) ρ0(λ ) = − − , (5) i 2∆λ

ρ(λi+1) 2ρ(λi) + ρ(λi 1) ρ00 (λi) = − − . (6) ∆λ2 where ρ is the spectral reﬂectance, λi denotes the wavelength of each band, and ∆λ represents the interval from λi 1 to λi. − Partial least squares regression analysis was employed to assess the tomato leaf water status (WC and MC) by using all wavelengths from 900 nm to 1700 nm. For this purpose, Unscrambler X software (CAMO Software Inc., Oslo, Norway) was used because it is eﬀective for multivariate analysis. Kernel partial least squares and cross-validation were employed in the regression analysis. NDVI and TBI were also adopted to assess the tomato leaf water status by using Formulas (7) and (8), respectively.

λ λ NDVI = 1 − 2 , (7) λ1 + λ2

λ + λ TBI = 1 2 . (8) λ λ 1 − 2 where λ1 is the wavelength of Band 1, from 900 nm to 1700 nm, and λ2 denotes the wavelength of Band 2, from 900 nm to 1700 nm. In the wavelength range from 900 nm to 1700 nm, 81 bands were identiﬁed by the 10 nm spectral resolution. All 6561 combinations of λ1 with λ2 were calculated for NDVI and TBI modeling by using MATLAB. Autocorrelation analysis was employed to improve the model accuracy and ensure model stability.

3. Results and Discussion

3.1. Tomato Leaf Water Status In the agricultural production of tomato, the increase of plant stress, especially water stress, is widely used to improve the tomato fruit quality. However, it is difficult to accurately control the amount of plant stress given. The desired effect cannot be achieved with insufficient plant stress; on the other hand, excessive increase of plant stress will cause damage to the plant, even if the amount of plant stress is slightly larger than optimal. Tomato leaf water status has been adopted as an index to evaluate plant stress because leaves are the most susceptible organs to water stress compared with other organs, such as the fruit, as noted earlier. Although the leaf water potential can be a strong indicator for assessing the tomato leaf water status, WC was employed as an indicator in this experiment because it is easy to measure and is a stable parameter [25]. The tomato leaf from Leaf Position 1 (LP-1) (Table1) has a significantly lower FW than those from Leaf Position 2 (LP-2) and Leaf Position 3 (LP-3). This is considered to be owing to the LP-1 tomato leaf being a new leaf and being located much closer to the growth point. There are significantly different WCs among the LP-1, LP-2, and LP-3 tomato leaves, the averages of which are 86.88%, 89.12%, and 90.25%, respectively. The higher the leaf position is, the smaller the leaf area (LA). The fresh weight per unit leaf area (FW/LA) and equivalent water thickness (MC) in LP-1 are significantly smaller than those in LP-2 and LP-3. Appl. Sci. 2020, 10, x FOR PEER REVIEW 7 of 18 Appl. Sci. 2020, 10, 4665 7 of 18 Table 1. Characteristic differences of the tomato leaves according to height layers.

Table 1. Characteristic diﬀerences of the tomato leaves according to height layers. FW (g) WC (%) LA (cm2) FW/LA (g/dm2) MC (g/dm2)

FW (g) WC (%) LA (cm2) FW/LA (g/dm2) MC (g/dm2) LP-1 1.19 b 86.88 c 48.96 b 2.30 b 2.00 b LP-1LP-2 1.191.89 b a 86.8889.12 c b 48.9667.31 b a 2.302.75 ba 2.48 2.00 a b LP-2 1.89 a 89.12 b 67.31 a 2.75 a 2.48 a LP-3LP-3 2.022.02 a a 90.2590.25 a a 73.1173.11 a a 2.79 aa 2.49 2.49 a a FW: fresh weight, WC: water content, LA: leaf area, FW/LA: fresh weight per unit leaf area; MC: FW: fresh weight, WC: water content, LA: leaf area, FW/LA: fresh weight per unit leaf area; MC: equivalent water ethickness.quivalent Values water with thickness. different Values letters arewith significantly different letters different are according significantly to Tukey’s different test at accordingp < 0.05. to Tukey’s test at p < 0.05. For the different tomato cultivars (Figure5), “DR03-103” shows a higher leaf FW and leaf area than For the different tomato cultivars (Figure 5), “DR03-103” shows a higher leaf FW and leaf area than the other tomato cultivars. Differences in WC are observed among the different cultivars. The highest the other tomato cultivars. Differences in WC are observed among the different cultivars. The highest leaf WC is observed in the typical Japanese cultivar “CF Momotarou”, with a 3.77% difference between leaf WC is observed in the typical Japanese cultivar “CF Momotarou”, with a 3.77% difference between LP-3 (87.58%) and LP-1 (91.35%). The lowest leaf WC is observed in the Dutch “Endeavour” tomato LP-3 (87.58%) and LP-1 (91.35%). The lowest leaf WC is observed in the Dutch “Endeavour” tomato cultivar, with a difference of 2.80% between LP-3 (86.12%) and LP-1 (88.92%). The equivalent water cultivar, with a difference of 2.80% between LP-3 (86.12%) and LP-1 (88.92%). The equivalent water thickness (MC) shows almost the same value in LP-2 and LP-3, whereas the WC in the LP-3 leaf is thickness (MC) shows almost the same value in LP-2 and LP-3, whereas the WC in the LP-3 leaf is on on average 1.13% higher than the WC in the LP-2 leaf. This difference is considered to be due to the average 1.13% higher than the WC in the LP-2 leaf. This difference is considered to be due to the influence of the water potential, which may depend on the height difference of the leaf position. influence of the water potential, which may depend on the height difference of the leaf position. 3 92 91 2.5 90 2 89

(g) 88 1.5 87 FW

1 WC (%) 86 85 0.5 84 0 83 E R T Y Z E R T Y Z

(a) (b)

100 3 90 80 2.5 70 ) ) 2

2 2 60 50 1.5 40 LA (cm

30 MC(g/dm 1 20 10 0.5 0 0 E R T Y Z E R T Y Z

FigureFigure 5 5.. TomatoTomato leaf leaf character characteristicsistics of of five ﬁve cultivars cultivars under under different diﬀerent height height layer layers.s. (a ()a Fresh) Fresh weight weight;; (b) 2 w(bater) water content content;; (c) l (eafc) leaf area area;; (d) ( de)quivalent equivalent water water thickness thickness (g/dm (g/dm).2 ).E: E: Endeavour Endeavour;; R: R: Rinnka409 Rinnka409;; T: TomimaruT: Tomimaru mucyo mucyo;; Y: Y:CF CF Momotarou Momotarou;; Z: Z:DR03 DR03-103.-103.

Appl. Sci. 2020, 10, 4665 8 of 18

3.2. Relationship between Individual Wavelength and Water Status Appl. Sci.Many 2020, 10 di, xff FORerent PEER types REVIEW of random noise exist in an HSI system, including a dark current8 of 18 from the 3.2.camera, Relationship light between from the Individual environment, Wavelength noise and fromWater digitizationStatus owing to analog to digital conversion, and so on. These noise values will obviously impact the results obtained from subsequent image Many different types of random noise exist in an HSI system, including a dark current from the analysis [26]. To improve the assessment accuracy of the leaf water status, the spectral derivative camera, light from the environment, noise from digitization owing to analog to digital conversion, and technique and the logarithmic transformation (log (1/R)) of the inverse of the reflectance method were so on. These noise values will obviously impact the results obtained from subsequent image analysis [adopted26]. To improve for proper the assessment HSI preprocessing. accuracy of the The leaf study water reported status, the in spectral [13] showed derivative that technique low-order and diff erential theprocessing logarithmic of thetransformation spectrum has (log a (1/R)) low sensitivity of the inverse to noise of the and reflectance is more emethodffective were in practical adopted applications.for properThus, HSI first-order preprocess diffingerential. The stud processingy reported technology in [13] showed was that adopted low-order in this differential study toprocessing remove partof of the thelinear spectrum or near-linear has a low background sensitivity to and noise the and influence is more ofeffective the noise in practical spectrum applications. on the target Thus, spectrum first- [2,24]. orderThe logarithmicdifferential processing transformation technology (log was (1/ adoptedR)) of the in this inverse study of to theremove reflectance part of the method linear or was near employed- linearto address background the nonlinear and the problem. influence Theof the logarithmic noise spectrum transformation on the target of the spectrum reciprocal [2,24 of]. The the spectral logarithmicreflectance transformation tends to not only (log (1/R)) enhance of the the inverse spectral of the diff reflectanceerence in method the visible wasand employed infrared to address region, but also the nonlinear problem. The logarithmic transformation of the reciprocal of the spectral reflectance tends reduce the effects of multiplicative factors caused by changes in lighting conditions [11]. to not only enhance the spectral difference in the visible and infrared region, but also reduce the effects To determine the correlation coefficient between the tomato leaf water status (WC and MC) and of multiplicative factors caused by changes in lighting conditions [11]. individualTo determine wavelengths the correlation of the rawcoefficient relative between reflection the tomato (RAW), leaf the water first derivativestatus (WC ofand relative MC) and reflectance individual(DIFF) and wavelength the inverse-logarithms of the raw relative relative reflection reflectance (RAW), (LOG) the first were derivative calculated of relative from 900reflectance nm to 1700 nm (DIFF)(Figure and6). the inverse-logarithm relative reflectance (LOG) were calculated from 900 nm to 1700 nm (Figure 6). (a) (b)

0.8 0.8

0.6 0.6

0.4 0.4

0.2 0.2

0 0

- 0.2 - 0.2

- 0.4 - 0.4 0 0 Raw Raw - 0.6 Diff - 0.6 Diff Log Log - 0.8 - 0.8 900 1000 1100 1200 1300 1400 1500 1600 1700 900 1000 1100 1200 1300 1400 1500 1600 1700 Wavelength Wavelength

Figure 6. Correlation coefficient between the leaf water status and individual wavelength. (a) Leaf WC; Figure(b) equivalent 6. Correlation water coefficient thickness between (g/dm the2). RAW:leaf water raw status relative and reflectance;individual wavelength. DIFF: first derivative(a) Leaf WC of; relative (b) equivalent water thickness (g/dm2). RAW: raw relative reflectance; DIFF: first derivative of relative reflectance; LOG: inverse-logarithm relative reflectance (1og(1/R)). reflectance; LOG: inverse-logarithm relative reflectance (1og(1/R)). The maximum negative correlation coefficient between the raw relative reflectance and WC is The maximum negative correlation coefficient between the raw relative reflectance and WC is observed at 1410 nm, where the coefficient value is r = 0.3334. Regarding the relationship between the observed at 1410 nm, where the coefficient value is = −0−.3334. Regarding the relationship between theraw raw relative relative reflectance reflectance and and MC,MC, thethe maximum negative negative correlation correlation coefficient coefficient is obtained is obtained at 1410 at 1410 nm nmwith with the the coe coefficientfficient value value of ofr == −0.68030.6803.. TheThe maximum positive positive correlation correlation coefficient coefficient between between the − theinverse-logarithm inverse-logarithm relativerelative reflectance reflectance and and WC WC is observed is observed at 14 at20 1420nm with nm a with low coefficient a low coe valuefficient value ofof r==0.0.27952795. With. With regard regard to the to relationship the relationship between between the inverse the-logarithm inverse-logarithm relative reflectance relative and reflectance MC,and the MC, maximum the maximum positive positivecorrelation correlation coefficient is coe obtainedfficient at is 14 obtained20 nm, and at the 1420 coefficient nm, and value the coeis fficient value= 0.5745 is r.= For0.5745 the .relationship For the relationship between the between first derivative the first of derivative relative reflectance of relative and reflectance WC, the and WC, maximum positive correlation coefficient value is = 0.4694 at 1430 nm. Moreover, the maximum the maximum positive correlation coefficient value is r = 0.4694 at 1430 nm. Moreover, the maximum negative correlation coefficient is obtained at 1140 nm with = −0.7298, and the second largest negative correlation coefficient is obtained at 1140 nm with r = 0.7298, and the second largest correlation coefficient value is obtained at 1430 nm with = 0.6457. − correlationIt should coe beffi notedcient that value, as isindicated obtained by atPu 1430 [2], compared nm with rto= other0.6457. plant biochemical parameters, water Ithas should several be obvious noted absorption that, as indicated bands at by 970, Pu 1200, [2], compared1400, and 1940 to othernm. Of plant these biochemical bands, 1400 nm parameters, water has several obvious absorption bands at 970, 1200, 1400, and 1940 nm. Of these bands, 1400 nm Appl. Sci. 2020, 10, 4665 9 of 18 and 1900 nm are considered the main bands that characterize water absorption [9]. The maximum negative or positive correlation coefficient in this experiment was thus concentrated in the range from 1410 nm to 1430 nm owing to the high water absorption in this region. Recent studies have revealed that reflectance is related to changes in MC rather than to changes in WC. It is considered that WC depends on two independent leaf variables: the leaf equivalent water thickness and leaf dry mass area, both of which affect the leaf optical properties [6,7,27]. The correlation value between MC and wavelength was always observed to be higher than that between WC and wavelength in the present experiment.

3.3. Partial Least-Squares Regression Applied to Wavelength and Water Status Partial least squares (PLS) regression is an alternative statistical regression technique that employs data compression to reduce the number of independent variables. PLS regression, followed by a calibration regression stage consisting of a least-squares ﬁt of the parameters to the obtained regression factors [2,28], was employed to calibrate the relationships between the spectral variables derived from the hyperspectral data (RAW, DIFF, and LOG) and the tomato leaf water status (WC and MC). To evaluate the effectiveness of the PLS regression model, cross-validation was employed in this experiment. Moreover, the root-mean-square error (RMSE) was calculated by using the following formula: r 1 Xn 2 RMSE = (yi yˆi) . (9) n i=1 −

The largest coefficient of determination of PLS regression for the wavelength and water status was obtained by the analysis of DIFF and MC, as shown in Figure7f, where the coe fficient of determination for calibration is R2 = 0.5822 and that for the validation is r2 = 0.5144. In addition, the two wavelength bands are the only two factors adopted in this model. The use of fewer factors will ensure model stability in practical applications. This is because the use of fewer factors will decrease the noise that always accompanies wavelength acquisition. The PLS regression results show a higher coefficient of determination for MC than that for WC. Some conclusions can be drawn in terms of the effect of hyperspectral data preprocessing before PLS regression: (1) Inverse-logarithm relative reflectance transformation helped improve the model performance, enabling a higher coefficient of determination and lower RMSE compared with RAW in WC modeling. Furthermore, the opposite effect was observed in MC modeling. (2) The first derivative of relative reflectance transformation degraded the model performance, leading to a lower coefficient of determination and higher RMSE compared with RAW in WC modeling. Meanwhile, the opposite effect was observed in MC modeling. The aforementioned aspects are considered the differences between WC and MC in terms of physics and the applicability of the data processing methods. Appl. Sci. 2020, 10, x FOR PEER REVIEW 10 of 18

mathematical expressions, where 휆1 /휆2 [6,14–17,32] and (휆1 − 휆2)/(휆1 + 휆2) [6,18–20,33,34] have been employed. In the present experiment, NDVI and TBI were adopted to establish the regression model. The results are shown in Figures 9 and 10, respectively.

3.5.1. NDVI With regard to WC assessment, a region corresponding to a higher coefficient of determination is

observed with 휆1 near 1400 nm and with 휆2 at approximately 1450–1650 nm (Figure 9). Better NDVI regression model performance is obtained with LOG, where the model is 푦 = 87.995푥 + 85.649, the coefficient of determination for calibration is 푅2 = 0.6080 , the coefficient of determination for validation is 푟2 = 0.3696 , and the root-mean-squared error is 푅푆푀퐸 = 1.44 (Table 2) at the Appl.wavelength Sci. 2020, 10s, 4665휆1 = 1370 nm and 휆2 = 1610 nm . For MC assessment, better NDVI (Figure 9)10 of 18 regression model performance is obtained with RAW, when 휆1 = 1300 nm and 휆2 = 1310 nm, where the model is 푦 = 317.11푥 − 0.3247 (푅2 = 0.8042, 푟2 = 0.6467, RSME = 0.2689).

(a) (b)

Appl. Sci. 2020, 10, x FOR PEER REVIEW 11 of 18 (c) (d)

(e) (f)

FigureFigure 7. 7.PLS PLS regression regression for for wavelength wavelength and and water water status. status. (a) RAW (a) RAWand WC and; (b) WC; RAW ( ban)d RAW MC; ( andc) LOG MC; (c) LOGand and WC; WC; (d) (LOGd) LOG and andMC; MC; (e) DIFF (e) DIFF and WC; and WC;(f) DIFF (f) DIFFand MC. and RMSE: MC. RMSE:root mean root squared mean squarederror. R- error. Square: coefficient of determination (blue denotes the calibration and red represents the validation). R-Square: coeﬃcient of determination (blue denotes the calibration and red represents the validation).

3.4. Autocorrelation Coeﬃcient of Wavelength Bands Hyperspectral data include extensive image and spectral information. However, not only do these spectra contain a considerable amount of redundant information, but a strong autocorrelation exists among diﬀerent wavelength bands. To reasonably increase the amount of useful information

(a)

(b) Appl. Sci. 2020, 10, x FOR PEER REVIEW 11 of 18

94 4 93

91 3

88 2

Appl. Sci.872020, 10, 4665 11 of 18

and optimum85 index factors for modeling, a spectral autocorrelation1 calculation was employed to 85 86 87 88 89 90 91 92 93 94 95 1 2 3 4 assist in selecting theReference characteristic Y (WC(%), Factor-2) spectra (Figure 8). For RAW andReference LOG, twoY (MC(g/dm^2), strong Factor-2) autocorrelation zones are observed in the(e) wavelength bands of 950–1350 nm and 1400–1700(f)nm. In contrast with the autocorrelationFigure 7. PLS coe regressionfficient for of RAW,wavelength the inverse-logarithm and water status. ( processinga) RAW and enhancesWC; (b) RAW the correlationand MC; (c) betweenLOG theand wavelength WC; (d) LOG bands. and Nevertheless,MC; (e) DIFF and first-order WC; (f) DIFF diff erentialand MC.processing RMSE: root decreasesmean squared the error. correlation R- betweenSquare wavelengths: coefficient of in determination most wavelength (blue denotes bands. the calibration and red represents the validation).

(a)

(b)

Figure 8. Cont. Appl.Appl. Sci. Sci. 20202020, 10, 10, x, 4665FOR PEER REVIEW 1212 of of 18 18

(c)

Figure 8. Autocorrelation coefficient coeﬃcient of wavelengths wavelengths.. ( (a)) RAW; RAW; (b) LOG; (c) DIFF.

3.5.2.3.5. ApplicationTBI of NDVI and TBI to Wavelength and Water Status ComparedFor leaf water with statusNDVI, assessment, almost the same studies characteristic have been wavelength conducted to bands establish were aobtained regression for modelthe TBI ofusing the wavelength diﬀerent wavelength and water status bands. (Figure In addition 10). Better to performance the multiple-band of the TBI models regression employed model in for some WC wasstudies obtained [14,29 with–31], DIFF, the two-band where the model model hasis been= −56 the.752 focus + 109 of many.12, the studies coefficient in applications of determination with fordi ﬀcalibrationerent mathematical is = 0 expressions,.6369, the coefficient where λ1 /ofλ2 determination[6,14–17,32] and for( λvalidation1 λ2)/(λ is1 + λ2=) 0[6.4057,18–20, and,33,34 the] − roothave-mean been- employed.square error In theis RMSE present= experiment,1.39 at the wavelengths NDVI and TBI were = 1620 adopted nm toand establish = 1390 the regressionnm. Better performanceAppl.model. Sci. 20 The20, 10 results of, x theFOR are PEER TBI shown regressionREVIEW in Figures model9 and for 10 MC, respectively. was observed with RAW at wavelengths13 of 18 = 1310 nm and = 1300 nm, where the model is = 0.0202 + 4.7953 ( = 0.7886, = 0.6389, RMSE = 0.2720). Overall, for WC assessment, the spectral preprocessing affected the model accuracy to varying degrees. The best model was obtained using TBI with DIFF (Table 2). It is considered that the combination of first-order differential processing and TBI helped improve the model performance. For MC assessment, the best model performance was obtained using NDVI with RAW; LOG and DIFF preprocessing did not help improve the regression model performance.

(e) (f)

Figure 9. Normalized difference vegetation index (NDVI) of tomato wavelength and leaf water status. (a) RAW and WC; (b) RAW and MC; (c) LOG and WC; (d) LOG and MC; (e) DIFF and WC; (f) DIFF and MC. Appl. Sci. 2020, 10, x FOR PEER REVIEW 13 of 18

Appl. Sci. 2020, 10, 4665 13 of 18 (a) (b)

(e) (f)

FigFigureure 9. 9. NormalizedNormalized difference diﬀerence vegetation index (NDVI)(NDVI) ofof tomatotomato wavelengthwavelength and and leaf leaf water water status. status. ((aa)) RAW RAW and and WC; WC; ( (bb)) RAW RAW and and MC; MC; (c ()c )LOG LOG and and WC; WC; (d ()d LOG) LOG and and MC; MC; (e) ( eDIFF) DIFF and and WC; WC; (f) (DIFFf) DIFF and Appl. Sci.MCand 20. MC.20, 10, x FOR PEER REVIEW 14 of 18

(a) (b)

Figure 10. Cont.

(e) (f)

Figure 10. Two-band index (TBI) of tomato wavelength and leaf water status. (a) RAW and WC; (b) RAW and MC; (c) LOG and WC; (d) LOG and MC; (e) DIFF and WC; (f) DIFF and MC.

For WC assessment, one of the two characteristic wavelengths for NDVI or TBI was concentrated at 1390 nm, very close to 1400 nm, which is well known as the main water absorption band of the leaf [9]. Another characteristic wavelength was concentrated at 1600 nm, which is a wavelength band that is also often chosen by researchers. By using the wavelengths of 1600 nm and 819 nm, Hardinsky et al. [19] and Hunt and Rock [17] developed a normalized difference infrared index (NDII) model and moisture stress index (MSI) model for detecting variations in the leaf WC with different respective Appl. Sci. 2020, 10, x FOR PEER REVIEW 14 of 18

Appl. Sci. 2020, 10, 4665 14 of 18

(a) (b)

(e) (f)

FigureFigure 10. 10. TTwo-bandwo-band index index (T (TBI)BI) of of tomato tomato wavelength wavelength and and leaf leaf water water status status.. (a) ( aRAW) RAW and and WC; WC; (b) RAW(b) RAW and andMC; MC; (c) LOG (c) LOG and andWC; WC; (d) LOG (d) LOG and andMC; MC; (e) DIFF (e) DIFF and and WC; WC; (f) DIFF (f) DIFF and and MC. MC.

3.5.1.For NDVI WC assessment, one of the two characteristic wavelengths for NDVI or TBI was concentrated at 1390 nm, very close to 1400 nm, which is well known as the main water absorption band of the leaf With regard to WC assessment, a region corresponding to a higher coefficient of determination [9]. Another characteristic wavelength was concentrated at 1600 nm, which is a wavelength band that is observed with λ1 near 1400 nm and with λ2 at approximately 1450–1650 nm (Figure9). Better is also often chosen by researchers. By using the wavelengths of 1600 nm and 819 nm, Hardinsky et al. NDVI regression model performance is obtained with LOG, where the model is y = 87.995x + 85.649, [19] and Hunt and Rock [17] developed a normalized difference infrared index (NDII) model and the coefficient of determination for calibration is R2 = 0.6080, the coefficient of determination for moisture stress index (MSI) model for detecting variations in the leaf WC with different respective validation is r2 = 0.3696, and the root-mean-squared error is RSME = 1.44 (Table2) at the wavelengths λ1 = 1370 nm and λ2 = 1610 nm. For MC assessment, better NDVI (Figure9) regression model performance is obtained with RAW, when λ1 = 1300 nm and λ2 = 1310 nm, where the model is y = 317.11x 0.3247 (R2 = 0.8042, r2 = 0.6467, RSME = 0.2689). − Appl. Sci. 2020, 10, 4665 15 of 18

Table 2. Model performance by using NDVI and TBI according to wavelength and water status.

WC (%) MC (g/dm2) Regression Models Data Sets Wavelength Wavelength R2 r2 RMSE R2 r2 RMSE Bands (nm) Bands (nm) RAW 1390, 1600 0.6060 0.3672 1.44 1300, 1310 0.8042 0.6467 0.2689 NDVI LOG 1390, 1600 0.6080 0.3696 1.44 950, 1350 0.7550 0.5699 0.2967 DIFF 1370, 1610 0.5643 0.3185 1.49 1310, 1410 0.7733 0.5980 0.2869 RAW 1400, 1570 0.5950 0.3540 1.45 1300, 1310 0.7886 0.6389 0.2720 TBI LOG 1390, 1620 0.5851 0.3430 1.47 950, 1350 0.7500 0.5707 0.2971 DIFF 1410, 1520 0.6369 0.4057 1.39 1310, 1400 0.7716 0.5972 0.2878 R2: coeﬃcient of determination for calibration, r2: coeﬃcient of determination for validation, RMSE: root-mean-square error.

3.5.2. TBI Compared with NDVI, almost the same characteristic wavelength bands were obtained for the TBI of the wavelength and water status (Figure 10). Better performance of the TBI regression model for WC was obtained with DIFF, where the model is y = 56.752x + 109.12, the coefficient of determination − for calibration is R2 = 0.6369, the coefficient of determination for validation is r2 = 0.4057, and the root-mean-square error is RMSE =1.39 at the wavelengths λ1 = 1620 nm and λ2 = 1390 nm. Better performance of the TBI regression model for MC was observed with RAW at wavelengths λ1 = 1310 nm 2 2 and λ2 = 1300 nm, where the model is y = 0.0202x + 4.7953 (R = 0.7886, r = 0.6389, RMSE = 0.2720). Overall, for WC assessment, the spectral preprocessing affected the model accuracy to varying degrees. The best model was obtained using TBI with DIFF (Table2). It is considered that the combination of first-order differential processing and TBI helped improve the model performance. For MC assessment, the best model performance was obtained using NDVI with RAW; LOG and DIFF preprocessing did not help improve the regression model performance.

3.5.3. Characteristic Wavelength Bands Numerous studies have demonstrated that the wavelengths that are the most sensitive to leaf water status are in the 900–2500 nm range, which includes four water absorption bands for all angiosperm leaves around 970 nm, 1200 nm, 1400nm, and 1900 nm [2,8]. Nevertheless, owing to equipment limitations in the present experiment, wavelengths ranging from 1700 nm to 2500 nm were not addressed. Only several different characteristic wavelength bands in the 900–1700 nm range were employed to establish models based on spectral preprocessing for different water statuses (Table2). For WC assessment, one of the two characteristic wavelengths for NDVI or TBI was concentrated at 1390 nm, very close to 1400 nm, which is well known as the main water absorption band of the leaf [9]. Another characteristic wavelength was concentrated at 1600 nm, which is a wavelength band that is also often chosen by researchers. By using the wavelengths of 1600 nm and 819 nm, Hardinsky et al. [19] and Hunt and Rock [17] developed a normalized difference infrared index (NDII) model and moisture stress index (MSI) model for detecting variations in the leaf WC with different respective expressions. All of the above proved that the wavelengths around 1400 nm and 1600 nm have a strong correlation with leaf WC. However, on account of the different characteristics of the leaf itself, the measuring equipment, and other factors, the characteristic wavelength selection may be slightly offset in the actual application. The best model for WC assessment was obtained in this experiment using TBI with DIFF and the 1410 nm and 1520 nm wavelengths. Similar results were demonstrated by Cao et al. [35]. To identify the best indices for relative water content (RWC) and equivalent water thickness (EWT) based on the first-derivative reflectance, they chose the wavelengths of 1415 nm and 1530 nm for RWC by using the same NDVI expression, and 1530 nm and 1895 nm for EWT by using a simple ratio expression. For MC assessment, several characteristic wavelengths exist for NDVI or TBI models with high performances in this experiment. They are concentrated around 950 nm, 1300 nm, 1350 nm, and 1400 nm. The wavelength bands of 950 nm and 1400 nm are very close to or equal the central wavelengths of Appl. Sci. 2020, 10, 4665 16 of 18 the water absorption bands of 970 nm and 1400 nm [2,8]. It is well explained why these two bands are chosen as characteristic wavelengths for high performance modeling. Regarding the wavelength of 1350 nm, in 2007, Jose et al. developed a simple ratio vegetation index using wavelengths of 1070 and 1340 nm, and a moisture stress index using wavelengths of 870 and 1350 nm, to detect the MC for grape leaves, and both quantities exhibited excellent correlations with MC, with R > 0.90 [36]. It is known that obvious high reflectance occurs at 1300 nm for a leaf with sufficient moisture [37]. However, few studies have adopted 1300 nm as the characteristic wavelength band in a model, except Seelig et al. [38], who reported the plant WC parameter and the remote sensing leaf water index by using 1300 nm and 1450 nm wavelengths. Furthermore, many studies have focused on estimating the leaf water status using a leaf dehydration dataset. The difference in leaf water status can assist in easily selecting the wavelength bands that correspond to strong absorption by water. However, in the present experiment, all leaf samples were obtained under normal conditions with sufficient moisture but without water stress. The fewer differences in leaf water status among the samples resulted in the decreased influence of water absorption on wavelength, duo to which the wavelengths of 1300 nm and 1350 nm, both of which are not water absorption bands, were chosen in this experiment. Meanwhile, the smaller leaf water status difference makes modeling much more difficult. In this experiment, the best model for MC assessment was attained using NDVI with RAW and 1300 nm and 1310 nm wavelengths. Previous studies have shown that the NIR plateau between 800 nm and 1300 nm, as well as water absorption bands above 1300 nm, are common characteristics of reflectance spectra of all healthy green plants. The high reflectivity in the NIR region of 800 nm to 1300 nm is due to the porous plant leaf structure (tissues and cells), whereas the low reflectivity above 1300 nm is caused by water absorption [3,39]. The 1300 nm wavelength is the dividing line of the histological structures and the specific components of plant leaves, both of which are the main factors affecting the reflectance spectral characteristics of the leaves. That causes the reflectance of tomato leaf to start changing from the reflectivity peak to the absorption valley at 1300 nm. This may also have resulted in 1300 nm and 1310 nm being adopted in modeling as the characteristic wavelength bands. In addition, a higher autocorrelation coefficient exists in the 1300 nm and 1310 nm wavelengths (Figure8). Although this combination is not suitable for a model, non-linear stretching by using NDVI enhances the contrast ratio of these two bands, which may assist in modeling.

4. Conclusions Irrigation management is an important issue in tomato cultivation, especially in plant factories. Accurate and timely tomato leaf water status assessment is essential for determining appropriate irrigation management. The aim of this study is to establish an approach to obtain tomato leaf water status in real time by using HSI technology. The results demonstrate the strong potential of using HSI technology for tomato leaf water status monitoring in plant factories, the application of MC assessment showed higher model performance compared with WC, and the best model for MC assessment was observed with NDVI–RAW in this study. In a subsequent study, other formulas will be used to achieve a higher prediction accuracy in tomato leaf water status assessment. To ensure the accuracy of that assessment, the optimal combination with the fewest wavelength bands will be selected. Accordingly, the applicability of the model for assessing diﬀerent plants under various environmental conditions will be enhanced by directly using a portable hyperspectral camera. This approach is expected to reduce equipment costs and improve equipment stability in the development of visualization equipment in the future.

Author Contributions: Conceptualization, T.Z., A.N., Y.I. and H.U.; methodology, T.Z., A.N., Y.I. and H.U.; software, T.Z.; validation, T. Z. and A.N.; formal analysis, T.Z.; investigation, T.Z., A.N., Y.I. and H.U.; resources, T.Z., A.N., Y.I. and H.U.; data curation, T.Z., A.N., Y.I. and H.U.; writing—original draft preparation, T.Z., and A.N.; writing—review and editing, T.Z., and A.N.; visualization, T.Z., A.N., Y.I. and H.U.; supervision, T.Z., A.N., Y.I. and H.U.; project administration, A.N.; funding acquisition, T.Z., A.N., Y.I. and H.U. All authors have read and agreed to the published version of the manuscript. Appl. Sci. 2020, 10, 4665 17 of 18

Funding: This research received no external funding. This work were supported by Cabinet Office, Government of Japan, Cross-ministerial Strategic Innovation Promotion Program (SIP), and MAFF with the funding number is ‘16932971’. Acknowledgments: We would like to thank Hiroki KURIBARA and Hikaru OKUCHI (NARO) for support in the implementation of this project. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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