# Feasibility of Combining Deep Learning and RGB Images Obtained by Unmanned Aerial Vehicle for Leaf Area Index Estimation in Rice

^{*}

## Abstract

**:**

^{2}= 0.963 and RMSE = 0.334) was higher than those of the PCA (R

^{2}= 0.934 and RMSE = 0.555) and the regression models based on CIs (R

^{2}= 0.802-0.947 and RMSE = 0.401–1.13), and comparable to that of the regression models based on VIs (R

^{2}= 0.917–0.976 and RMSE = 0.332–0.644). Therefore, our results demonstrated that the estimation model using DL with an RGB camera on a UAV could be an alternative to the methods using PCA and a multispectral camera for rice LAI estimation.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Experimental Design and Data Acquisition

^{2}was applied as a basal on 23 May, and 2 g/m

^{2}was applied twice as top dressing on 20 June and 17 July) were set in a split-plot design with three replications with the fertilizer treatment as the main plot. In addition, 10 g/m

^{2}of P

_{2}O

_{5}and K

_{2}O were applied as a basal on May 15. Transplanting was carried out on 22 May with a planting density of 22.2 hills/m

^{2}(30 cm × 15 cm) with 3 plants per hill.

#### 2.2. Image Processing

#### 2.2.1. Generation of Ortho-Mosaic Images

#### 2.2.2. Calculation of Vegetation Indices and Color Indices

#### 2.3. Estimation Model Development and Accuracy Assessment

## 3. Results

#### 3.1. Variations of the Ground-Measured Leaf Area Index

#### 3.2. Regression Models Using Each of VIs and CIs

^{2}= 0.976 and RMSE = 0.332) followed by NDVI (NIR, Red) (R

^{2}= 0.959 and RMSE = 0.475) and SAVI (NIR, Red) (R

^{2}= 0.959 and RMSE = 0.478) (Figure 8 and Figure 9a,d,j). VEG showed the highest accuracy of all CIs (R

^{2}= 0.947 and RMSE = 0.401) followed by E × G (R

^{2}= 0.937 and RMSE = 0.440) and GLA (R

^{2}= 0.935 and RMSE = 0.444) (Figure 8 and Figure 10b,g,i).

#### 3.3. Estimation Models by Machine-Learning Algorithms Other Than Deep Learning

^{2}= 0.940 and RMSE = 0.401, R

^{2}= 0.939 and RMSE = 0.422 and R

^{2}= 0.945 and RMSE = 0.399, respectively). RF achieved the highest accuracy when the input data was nine types of CIs and RGB images, which was the highest accuracy in all combinations (R

^{2}= 0.957 and RMSE = 0.342).

#### 3.4. Estimation Models by Deep Learning

^{2}= 0.900 and RMSE = 0.605 for CIs, R

^{2}= 0.979 and RMSE = 0.280 for images and R

^{2}= 0.989 and RMSE = 0.203 for CIs + images, respectively (Table 6). The coefficient of determination ranged from 0.946 to 0.964, and RMSE ranged from 0.322 to 0.434. The estimation model using nine types of CIs as input data underestimated the ground-measured LAI; the estimation accuracy of this model was lower than those of the other two estimation models and there was no improvement from the regression model of VEG, which achieved the highest accuracy in all CIs (R

^{2}= 0.946 and RMSE = 0.434) (Figure 11a). Higher accuracy was achieved in the estimation model using RGB images as input data (R

^{2}= 0.963 and RMSE = 0.334) (Figure 11b), and little improvement was observed in the estimation model using nine types of CIs and RGB images as input data, with values of R

^{2}= 0.964 and RMSE = 0.322 (Figure 11c). These two models containing RGB images as input data achieved almost the same accuracy as the regression model of SR (NIR, Red), which achieved the highest accuracy in VIs (Figure 9d).

#### 3.5. Plant Canopy Analyzer

^{2}= 0.934 and RMSE = 0.308 without significant difference in variety and fertilization level (Figure 12). However, PCA underestimated the ground-measured LAI by 12% (Figure 12).

## 4. Discussion

^{2}= 0.976 and RMSE = 0.332), and VEG was the most accurate of the CIs (R

^{2}= 0.947 and RMSE = 0.401) (Table 4, Figure 8 and Figure 13). Although the estimation accuracy varied depending on the index, the VIs obtained from the multispectral camera generally performed better than the CIs obtained from the RGB camera (Table 4, Figure 8), which agreed with the results of Gupta et al. [19]. The reflectance of near-infrared light is more responsive to an increase in leaf area than the reflectance of visible light, because the former is more easily affected by changes in the vegetation structure [37]. Therefore, it is considered that the VIs including the reflectance of near-infrared light acquired from a multispectral camera showed relatively high estimation accuracy.

^{2}= 0.947 and RMSE = 0.401), the estimation model developed by RF using nine types of CIs and images as input data showed an improvement (R

^{2}= 0.957 and RMSE = 0.342) (Figure 10 and Figure 13, Table 5). Several existing researches have indicated that RF is an ideal algorithm to improve the estimation accuracy of LAI [21,22,25], and the results of this study was consistent with these reports.

^{2}= 0.963 and RMSE = 0.334), and its accuracy was comparable to that of SR (NIR, Red) (R

^{2}= 0.976 and RMSE = 0.332), which showed the highest estimation accuracy among the VIs acquired from the multispectral camera (Figure 9d, Figure 11b and Figure 13). The results suggested that although the RGB camera is inferior when using only CIs, it can be made to achieve high performance equivalent to that of the multispectral camera simply by constructing an estimation model by DL with the images incorporated as input data. In the conventional machine-learning algorithms, the features must be specified in advance. In contrast, DL has the major advantage of being able to identify the characteristics of the images automatically [55]. In this research, since the training data in DL included images with a resolution of 100 × 100 pixels, which contained much more information than the CIs, the characteristics of plant morphology were recognized in greater detail. These factors were considered to be the reason for the achievement of a high estimation accuracy by DL with images.

^{2}= 0.934 and RMSE = 0.555, this was lower than the accuracies by a multispectral camera (the regression model based on SR (NIR, Red): R

^{2}= 0.976 and RMSE = 0.332) and an RGB camera (the estimation model developed by DL using RGB images: R

^{2}= 0.963 and RMSE = 0.334) (Figure 9d, Figure 11b, Figure 12 and Figure 13). This is because plants other than the sampled eight hills got into the view of the PCA sensor, even though a view cap was installed. In addition, PCA led to 12% underestimation of the ground-measured values (Figure 12). This result was consistent with the previous studies by Maruyama et al. [57] and Fang et al. [58], which reported that PCA underestimates the LAI measurements of rice canopy throughout the growth stage. LAI estimation with PCA is based on the assumption that the leaves are randomly distributed in space. For this reason, two factors have been reported to affect PCA-based measurement of LAI: the first is clamping, which means that parts of a plant are concentrated in one place, thereby undermining the random distribution and causing underestimation of LAI; and the second is the entry of plant components other than leaves into the field of the sensor, which causes LAI overestimation [59]. Especially in the case of rice canopies, leaf overlap [57] and the presence of stems, which were originally spatially aggregated [58], have been reported as the factors leading to clamping, and these factors are considered to be the main cause of the underestimation in this study. This underestimation could be mitigated by using four-ring data instead of five-ring data of PCA [58]. In any case, PCA can measure the canopy LAI non-destructively and rapidly, and will certainly be a useful ground-truth acquisition tool. In order to use the PCA effectively, sufficient attention should be paid to the correspondence of PCA with measured values.

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Lahoz, W. Systematic Observation Requirements for Satellite-based Products for Climate, 2011 Update, Supplemental Details to the Satellite-Based Component of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (2010 Update); World Meteorological Organization (WMO): Geneva, Switzerland, 2011; p. 83. [Google Scholar]
- Asner, G.P.; Scurlock, J.M.O.; Hicke, J.A. Global synthesis of leaf area index observations. Glob. Chang. Biol.
**2008**, 14, 237–243. [Google Scholar] [CrossRef][Green Version] - Stark, S.C.; Leitold, V.; Wu, J.L.; Hunter, M.O.; de Castilho, C.V.; Costa, F.R.C.; Mcmahon, S.M.; Parker, G.G.; Shimabukuro, M.T.; Lefsky, M.A.; et al. Amazon forest carbon dynamics predicted by profiles of canopy leaf area and light environment. Ecol. Lett.
**2012**, 15, 1406–1414. [Google Scholar] [CrossRef] [PubMed][Green Version] - Duchemin, B.; Maisongrande, P.; Boulet, G.; Benhadj, I. A simple algorithm for yield estimates: Evaluation for semi-arid irrigated winter wheat monitored with green leaf area index. Environ. Model. Softw.
**2008**, 23, 876–892. [Google Scholar] [CrossRef][Green Version] - Fang, H.; Liang, S.; Hoogenboom, G. Integration of MODIS LAI and vegetation index products with the CSM-CERES-Maize model for corn yield estimation. Int. J. Remote Sens.
**2011**, 32, 1039–1065. [Google Scholar] [CrossRef] - Brisson, N.; Gary, C.; Justes, E.; Roche, R.; Mary, B.; Ripoche, D.; Zimmer, D.; Sierra, J.; Bertuzzi, P.; Burger, P.; et al. An overview of the crop model STICS. Eur. J. Agron.
**2003**, 18, 309–332. [Google Scholar] [CrossRef] - Stenberg, P.; Linder, S.; Smolander, H.; Flower-Ellis, J. Performance of the LAI-2000 plant canopy analyzer in estimating leaf area index of some Scots pine stands. Tree Physiol.
**1994**, 14, 981–995. [Google Scholar] [CrossRef] [PubMed] - Wang, C.; Nie, S.; Xi, X.; Luo, S.; Sun, X. Estimating the biomass of maize with hyperspectral and LiDAR data. Remote Sens.
**2016**, 9, 11. [Google Scholar] [CrossRef][Green Version] - Luo, S.; Wang, C.; Xi, X.; Nie, S.; Fan, X.; Chen, H.; Yang, X.; Peng, D.; Lin, Y.; Zhou, G. Combining hyperspectral imagery and LiDAR pseudo-waveform for predicting crop LAI, canopy height and above-ground biomass. Ecol. Indic.
**2019**, 102, 801–812. [Google Scholar] [CrossRef] - Hashimoto, N.; Saito, Y.; Maki, M.; Homma, K. Simulation of reflectance and vegetation indices for unmanned aerial vehicle (UAV) monitoring of paddy fields. Remote Sens.
**2019**, 11, 2119. [Google Scholar] [CrossRef][Green Version] - Sakamoto, T.; Shibayama, M.; Kimura, A.; Takada, E. Assessment of digital camera-derived vegetation indices in quantitative monitoring of seasonal rice growth. ISPRS J. Photogramm. Remote Sens.
**2011**, 66, 872–882. [Google Scholar] [CrossRef] - Fan, X.; Kawamura, K.; Guo, W.; Xuan, T.D.; Lim, J.; Yuba, N.; Kurokawa, Y.; Obitsu, T.; Lv, R.; Tsumiyama, Y.; et al. A simple visible and near-infrared (V-NIR) camera system for monitoring the leaf area index and growth stage of Italian ryegrass. Comput. Electron. Agric.
**2018**, 144, 314–323. [Google Scholar] [CrossRef] - Lee, K.J.; Lee, B.W. Estimation of rice growth and nitrogen nutrition status using color digital camera image analysis. Eur. J. Agron.
**2013**, 48, 57–65. [Google Scholar] [CrossRef] - Tanaka, Y.; Katsura, K.; Yamashita, Y. Verification of image processing methods using digital cameras for rice growth diagnosis. J. Japan Soc. Photogramm. Remote Sens.
**2020**, 59, 248–258. [Google Scholar] - Maes, W.H.; Steppe, K. Perspectives for Remote Sensing with Unmanned Aerial Vehicles in Precision Agriculture. Trends Plant Sci.
**2019**, 24, 152–164. [Google Scholar] [CrossRef] [PubMed] - Zhang, C.; Kovacs, J.M. The application of small unmanned aerial systems for precision agriculture: A review. Precis. Agric.
**2012**, 13, 693–712. [Google Scholar] [CrossRef] - Kross, A.; McNairn, H.; Lapen, D.; Sunohara, M.; Champagne, C. Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops. Int. J. Appl. Earth Obs. Geoinf.
**2015**, 34, 235–248. [Google Scholar] [CrossRef][Green Version] - Liu, K.; Zhou, Q.B.; Wu, W.B.; Xia, T.; Tang, H.J. Estimating the crop leaf area index using hyperspectral remote sensing. J. Integr. Agric.
**2016**, 15, 475–491. [Google Scholar] [CrossRef][Green Version] - Gupta, R.K.; Vijayan, D.; Prasad, T.S. The relationship of hyper-spectral vegetation indices with leaf area index (LAI) over the growth cycle of wheat and chickpea at 3 nm spectral resolution. Adv. Sp. Res.
**2006**. [Google Scholar] [CrossRef] - Lu, N.; Zhou, J.; Han, Z.; Li, D.; Cao, Q.; Yao, X.; Tian, Y.; Zhu, Y.; Cao, W.; Cheng, T. Improved estimation of aboveground biomass in wheat from RGB imagery and point cloud data acquired with a low-cost unmanned aerial vehicle system. Plant Methods
**2019**, 15, 1–16. [Google Scholar] [CrossRef][Green Version] - Houborg, R.; McCabe, M.F. A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning. ISPRS J. Photogramm. Remote Sens.
**2018**, 135, 173–188. [Google Scholar] [CrossRef] - Li, S.; Yuan, F.; Ata-UI-Karim, S.T.; Zheng, H.; Cheng, T.; Liu, X.; Tian, Y.; Zhu, Y.; Cao, W.; Cao, Q. Combining color indices and textures of UAV-based digital imagery for rice LAI estimation. Remote Sens.
**2019**, 11, 63. [Google Scholar] [CrossRef][Green Version] - Verrelst, J.; Muñoz, J.; Alonso, L.; Delegido, J.; Rivera, J.P.; Camps-Valls, G.; Moreno, J. Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3. Remote Sens. Environ.
**2012**, 118, 127–139. [Google Scholar] [CrossRef] - Wang, L.; Chang, Q.; Yang, J.; Zhang, X.; Li, F. Estimation of paddy rice leaf area index using machine learning methods based on hyperspectral data from multi-year experiments. PLoS ONE
**2018**, 13, e0207624. [Google Scholar] [CrossRef] [PubMed][Green Version] - Zhu, Y.; Liu, K.; Liu, L.; Myint, S.W.; Wang, S.; Liu, H.; He, Z. Exploring the potential of world view-2 red-edge band-based vegetation indices for estimation of mangrove leaf area index with machine learning algorithms. Remote Sens.
**2017**, 9, 60. [Google Scholar] [CrossRef][Green Version] - He, K.; Zhang, X.; Ren, S.; Sun, J. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 7–13 December 2015; pp. 1026–1034. [Google Scholar]
- Mohanty, S.P.; Hughes, D.P.; Salathé, M. Using Deep Learning for Image-Based Plant Disease Detection. Front. Plant Sci.
**2016**, 7, 1419. [Google Scholar] [CrossRef] [PubMed][Green Version] - Chen, Y.; Lin, Z.; Zhao, X.; Wang, G.; Gu, Y. Deep learning-based classification of hyperspectral data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.
**2014**, 7, 2094–2107. [Google Scholar] [CrossRef] - Reyes, A.K.; Caicedo, J.C.; Camargo, J. Fine-tuning Deep Convolutional Networks for Plant Recognition. CLEF Work. Notes
**2015**, 1391, 467–475. [Google Scholar] - Xinshao, W.; Cheng, C. Weed seeds classification based on PCANet deep learning baseline. In Proceedings of the 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015, Hong Kong, China, 16–19 December 2015; pp. 408–415. [Google Scholar]
- Song, X.; Zhang, G.; Liu, F.; Li, D.; Zhao, Y.; Yang, J. Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model. J. Arid Land
**2016**, 8, 734–748. [Google Scholar] [CrossRef][Green Version] - Zhou, X.; Kono, Y.; Win, A.; Matsui, T.; Tanaka, T.S.T. Predicting within-field variability in grain yield and protein content of winter wheat using UAV-based multispectral imagery and machine learning approaches. Plant Prod. Sci.
**2020**. [Google Scholar] [CrossRef] - Krizhevsky, A.; Sutskever, I.; Hinton, G.E. ImageNet Classification with Deep Convolutional Neural Networks. In Proceedings of the Advances in Neural Information Processing Systems 25, Neural Information Processing Systems Foundation, Inc. ( NIPS ), Lake Tahoe, NV, USA, 3–6 December 2012; pp. 1106–1114. [Google Scholar]
- Carl, F. Jordan Derivation of Leaf-Area Index from Quality of Light on the Forest Floor. Ecology
**1969**, 50, 663–666. [Google Scholar] - Chen, J.M. Evaluation of vegetation indices and a modified simple ratio for boreal applications. Can. J. Remote Sens.
**1996**, 22, 229–242. [Google Scholar] [CrossRef] - Huete, A. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ.
**1988**, 25, 295–309. [Google Scholar] [CrossRef] - Gitelson, A.A.; Kaufman, Y.J.; Stark, R.; Rundquist, D. Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ.
**2002**, 80, 76–87. [Google Scholar] [CrossRef][Green Version] - Woebbecke, D.M.; Meyer, G.E.; Von Bargen, K.; Mortensen, D.A. Color Indices for Weed Identification Under Various Soil, Residue, and Lighting Conditions. Trans. ASAE
**1995**, 38, 259–269. [Google Scholar] [CrossRef] - Meyer, G.E.; Neto, J.C. Verification of color vegetation indices for automated crop imaging applications. Comput. Electron. Agric.
**2008**, 63, 282–293. [Google Scholar] [CrossRef] - Mao, W.; Wang, Y.; Wang, Y. Real-time Detection of Between-row Weeds Using Machine Vision. In Proceedings of the 2003 ASAE Annual Meeting, American Society of Agricultural and Biological Engineers (ASABE), Las Vegas, NV, USA, 27–30 July 2003; p. 1. [Google Scholar]
- Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ.
**1979**, 8, 127–150. [Google Scholar] [CrossRef][Green Version] - Louhaichi, M.; Borman, M.M.; Johnson, D.E. Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto Int.
**2001**, 16, 65–70. [Google Scholar] [CrossRef] - Bendig, J.; Yu, K.; Aasen, H.; Bolten, A.; Bennertz, S.; Broscheit, J.; Gnyp, M.L.; Bareth, G. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int. J. Appl. Earth Obs. Geoinf.
**2015**, 39, 79–87. [Google Scholar] [CrossRef] - Hague, T.; Tillett, N.D.; Wheeler, H. Automated crop and weed monitoring in widely spaced cereals. Precis. Agric.
**2006**, 7, 21–32. [Google Scholar] [CrossRef] - He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; IEEE Computer Society, Las Vegas, NV, USA, 26 June–1 July 2016; pp. 770–778. [Google Scholar]
- Xie, S.; Girshick, R.; Dollár, P.; Tu, Z.; He, K. Aggregated residual transformations for deep neural networks. In Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, 21–26 July 2017; pp. 5987–5995. [Google Scholar]
- Afonso, M.V.; Barth, R.; Chauhan, A. Deep learning based plant part detection in Greenhouse settings. In Proceedings of the 12th EFITA International Conference: Digitizing Agriculture, European Federation for Information Technology in Agriculture, Food and the Environment (EFITA), Rhodes Island, Greece, 19 July 2019; pp. 48–53. [Google Scholar]
- Liu, B.; Tan, C.; Li, S.; He, J.; Wang, H. A Data Augmentation Method Based on Generative Adversarial Networks for Grape Leaf Disease Identification. IEEE Access
**2020**, 8, 102188–102198. [Google Scholar] [CrossRef] - Yoshida, H.; Horie, T.; Katsura, K.; Shiraiwa, T. A model explaining genotypic and environmental variation in leaf area development of rice based on biomass growth and leaf N accumulation. F. Crop. Res.
**2007**, 102, 228–238. [Google Scholar] [CrossRef] - Rasmussen, J.; Ntakos, G.; Nielsen, J.; Svensgaard, J.; Poulsen, R.N.; Christensen, S. Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots? Eur. J. Agron.
**2016**, 74, 75–92. [Google Scholar] [CrossRef] - Inoue, Y.; Guérif, M.; Baret, F.; Skidmore, A.; Gitelson, A.; Schlerf, M.; Darvishzadeh, R.; Olioso, A. Simple and robust methods for remote sensing of canopy chlorophyll content: A comparative analysis of hyperspectral data for different types of vegetation. Plant Cell Environ.
**2016**, 39, 2609–2623. [Google Scholar] [CrossRef] [PubMed][Green Version] - Liang, L.; Di, L.; Zhang, L.; Deng, M.; Qin, Z.; Zhao, S.; Lin, H. Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method. Remote Sens. Environ.
**2015**, 165, 123–134. [Google Scholar] [CrossRef] - Broge, N.H.; Leblanc, E. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens. Environ.
**2001**, 76, 156–172. [Google Scholar] [CrossRef] - Dong, T.; Liu, J.; Shang, J.; Qian, B.; Ma, B.; Kovacs, J.M.; Walters, D.; Jiao, X.; Geng, X.; Shi, Y. Assessment of red-edge vegetation indices for crop leaf area index estimation. Remote Sens. Environ.
**2019**, 222, 133–143. [Google Scholar] [CrossRef] - Lecun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature
**2015**, 521, 436–444. [Google Scholar] [CrossRef] - Kamilaris, A.; Prenafeta-Boldú, F.X. Deep learning in agriculture: A survey. Comput. Electron. Agric.
**2018**, 147, 70–90. [Google Scholar] [CrossRef][Green Version] - Maruyama, A.; Kuwagata, T.; Ohba, K. Measurement Error of Plant Area Index using Plant Canopy Analyzer and its Dependence on Mean Tilt Angle of The Foliage. J. Agric. Meteorol.
**2005**, 61, 229–233. [Google Scholar] [CrossRef][Green Version] - Fang, H.; Li, W.; Wei, S.; Jiang, C. Seasonal variation of leaf area index (LAI) over paddy rice fields in NE China: Intercomparison of destructive sampling, LAI-2200, digital hemispherical photography (DHP), and AccuPAR methods. Agric. For. Meteorol.
**2014**, 198, 126–141. [Google Scholar] [CrossRef] - Chen, J.M.; Rich, P.M.; Gower, S.T.; Norman, J.M.; Plummer, S. Leaf area index of boreal forests: Theory, techniques, and measurements. J. Geophys. Res. Atmos.
**1997**, 102, 29429–29443. [Google Scholar] [CrossRef]

**Figure 1.**Sampling point of plant canopy analyzer (PCA) for below the canopy. The data of PCA for below the canopy of eight harvested plants (2 rows by 4 plants) data were collected from 10 points. The data were taken in the direction of the arrows from the position of the enclosed numbers: 4 points were taken from between the plants in each of the 2 rows at a 45-degree angle to the direction of the rows towards the inside of the canopy (from No. 1 to 4), and 6 points were taken from between the rows parallel to the rows (from No. 5 to 10).

**Figure 2.**Examples of ortho-mosaic images (9 July). The green triangles in the four corners of the field represent ground control points (GCPs): (

**a**) a multispectral ortho-mosaic image (near-infrared (NIR)); (

**b**) an RGB ortho-mosaic image.

**Figure 3.**An example of polygons for extracting reflectance from a multispectral ortho-mosaic image (NIR, July 9, Koshihikari, +N, R1).

**Figure 4.**Examples of the small images of eight hills cut out at a resolution of 100 × 100 pixels from the RGB ortho-mosaic images (Koshihikari, +N, R1).

**Figure 5.**Examples of inflated images used as input data for deep learning (DL) (June 26, Koshihikari, +N, R1).

**Figure 6.**Network architectures for the three patterns of input datasets of DL used in this study: (

**a**) nine types of CIs; (

**b**) RGB images; (

**c**) nine types of CIs and RGB images. “C = 32” indicates grouped convolutions with 32 groups. “7 × 7 conv 64,/2” indicates a convolution layer using 64 kinds of 7 × 7 kernel filter with a stride of 2 pixels. “fc 200” indicates a fully connected layer with 200 outputs.

**Figure 7.**Seasonal changes of ground-measured leaf area index (LAI) for three rice varieties grown under two nitrogen management conditions. Each value represents the average of three replications.

**Figure 8.**Comparison of the estimation accuracy of each regression model with each of the VIs and CIs. The black bars indicate the coefficient of determination (R

^{2}), and the white bars indicate the root mean squared error (RMSE) between the ground-measured LAI and estimated LAI from the regression models based on each of the VIs and CIs.

**Figure 9.**Correlations between ground-measured LAI and estimated LAI from the regression models based on each VI: (

**a**) NDVI (NIR, Red); (

**b**) NDVI (NIR, Rededge); (

**c**) NDVI (Rededge, Red); (

**d**) SR (NIR, Red); (

**e**) SR (NIR, Rededge); (

**f**) SR (Rededge, Red); (

**g**) MSR (NIR, Red); (

**h**) MSR (NIR, Rededge); (

**i**) MSR (Rededge, Red); (

**j**) SAVI (NIR, Red); (

**k**) SAVI (NIR, Rededge); (

**l**) SAVI (Rededge, Red). The equation of each regression model is shown in Table 2.

**Figure 10.**Correlations between ground-measured LAI and estimated LAI from the regression models based on each CI: (

**a**) VARI; (

**b**) E × G; (

**c**) E × R; (

**d**) E × B; (

**e**) NGRDI; (

**f**) MGRVI; (

**g**) GLA; (

**h**) RGBVI; (

**i**) VEG. The equation of each regression model is shown in Table 2.

**Figure 11.**Correlations between ground-measured LAI and estimated LAI of validation data with models developed by DL with three patterns of input datasets: (

**a**) nine types of CIs, (

**b**) RGB images, (

**c**) nine types of CIs and RGB images.

**Figure 13.**Summary of LAI estimation accuracy of 5 methods: the regression model based on SR (NIR, Red), the regression model based on VEG, the estimation model developed by RF using nine types of CIs and RGB images, the estimation model developed by DL using RGB images and PCA (LAI-2200).

Camera | Spectral Band (nm) | Resolution (Pixels) |
---|---|---|

Rededge-MX (multispectral camera) | 475 (Blue), 560 (Green), 668 (Red), 717 (Rededge), 840 (NIR) | 1280 × 960 |

Zenmuse X4S (RGB camera) | R, G, B | 5472 × 3648 |

Index | Formula | Reference | |
---|---|---|---|

VIs | NDVI (λ1, λ2) | (R_{λ1} − R_{λ2})/(R_{λ1} + R_{λ2}) | Jordan [34] |

SR (λ1, λ2) | R_{λ1}/R_{λ2} | Jordan [34] | |

MSR (λ1, λ2) | ((R_{λ1}/R_{λ2}) − 1)/((R_{λ1}/R_{λ2}) + 1)^{0.5} | Chen [35] | |

SAVI (λ1, λ2) | 1.5(R_{λ1} − R_{λ2})/(R_{λ1} + R_{λ2} + 0.5) | Huete [36] | |

CIs | VARI | (g − r)/(g + r − b) | Gitelson et al. [37] |

E × G | 2g − r – b | Woebbecke et al. [38] | |

E × R | 1.4r – g | Meyer & Neto [39] | |

E × B | 1.4b – g | Mao et al. [40] | |

NGRDI | (g − r)/(g + r) | Tucker [41] | |

MGRVI | (g^{2} − r^{2})/(g^{2} + r^{2}) | Tucker [41] | |

GLA | (2g − r − b)/(2g + r + b) | Louhaichi et al. [42] | |

RGBVI | (g^{2} − b × r)/(g^{2} + b × r) | Bendig et al. [43] | |

VEG | g/(r^{a}b^{(1 − a)}), a = 0.667 | Hague et al. [44] |

Input Dataset | Epoch | Batch Size | Optimizer | Weight Decay |
---|---|---|---|---|

CIs | 100 | 16 | Adam | 0 |

Images | 100 | 16 | Adam | 0 |

CIs + Images | 100 | 8 | Adam | 0.01 |

Index | Model | Regression Equation | |
---|---|---|---|

VI | NDVI (NIR, Red) | Exponential | y = 0.0809 × exp(4.41 × x) |

NDVI (NIR, Rededge) | Linear | y = 8.17 × x − 0.363 | |

NDVI (Rededge, Red) | Exponential | y = 0.112 × exp(5.09 × x) | |

SR (NIR, Red) | Logarithmic | y = 1.58 × In(x) − 0.707 | |

SR (NIR, Rededge) | Logarithmic | y = 3.10 × In(x) + 0.0131 | |

SR (Rededge, Red) | Linear | y = 0.794 × x − 0.781 | |

MSR (NIR, Red) | Linear | y = 0.859 × x − 0.154 | |

MSR (NIR, Rededge) | Linear | y = 2.77 × x − 0.644 | |

MSR (Rededge, Red) | Linear | y = 2.54 × x − 1.61 | |

SAVI (NIR, Red) | Exponential | y = 0.0810 × exp(2.94 × x) | |

SAVI (NIR, Rededge) | Linear | y = 5.45 × x − 0.363 | |

SAVI (Rededge, Red) | Exponential | y = 0.113 × exp(3.39 × x) | |

CI | VARI | Exponential | y = 0.252 × exp(5.74 × x) |

E × G | Linear | y = 12.3 × x − 0.175 | |

E × R | Exponential | y = 1.36 × exp(−11.7 × x) | |

E×B | Linear | y = -24.7 × x + 3.25 | |

NGRDI | Exponential | y = 0.275 × exp(9.72 × x) | |

MGRVI | Exponential | y = 0.258 × exp(5.40 × x) | |

GLA | Linear | y = 18.1 × x − 0.238 | |

RGBVI | Exponential | y = 0.261 × exp(6.13 × x) | |

VEG | Linear | y = 5.99 × x − 6.01 |

**Table 5.**Estimation accuracy of validation data with models developed by four kinds of machine-learning algorithms with three patterns of input datasets.

Algorithm | Input Dataset | Equation | R^{2} | RMSE |
---|---|---|---|---|

ANN | CIs | y = 1.00x | 0.940 | 0.401 |

Images | y = 1.02x | 0.906 | 0.568 | |

CIs + Images | y = 1.01x | 0.828 | 0.659 | |

PLSR | CIs | y = 1.01x | 0.939 | 0.422 |

Images | y = 0.957x | 0.252 | 1.697 | |

CIs + Images | y = 0.982x | 0.715 | 0.940 | |

RF | CIs | y = 1.02x | 0.939 | 0.436 |

Images | y = 0.996x | 0.851 | 0.585 | |

CIs + Images | y = 0.993x | 0.957 | 0.342 | |

SVR | CIs | y = 0.932x | 0.945 | 0.399 |

Images | y = 0.967x | 0.882 | 0.549 | |

CIs + Images | y = 0.967x | 0.883 | 0.549 |

**Table 6.**Estimation accuracy of training data with models developed by DL with three patterns of input datasets.

Input Dataset | Equation | R^{2} | RMSE |
---|---|---|---|

CIs | y = 0.994x | 0.900 | 0.605 |

Images | y = 0.991x | 0.979 | 0.280 |

CIs + Images | y = 1.01x | 0.989 | 0.203 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Yamaguchi, T.; Tanaka, Y.; Imachi, Y.; Yamashita, M.; Katsura, K. Feasibility of Combining Deep Learning and RGB Images Obtained by Unmanned Aerial Vehicle for Leaf Area Index Estimation in Rice. *Remote Sens.* **2021**, *13*, 84.
https://doi.org/10.3390/rs13010084

**AMA Style**

Yamaguchi T, Tanaka Y, Imachi Y, Yamashita M, Katsura K. Feasibility of Combining Deep Learning and RGB Images Obtained by Unmanned Aerial Vehicle for Leaf Area Index Estimation in Rice. *Remote Sensing*. 2021; 13(1):84.
https://doi.org/10.3390/rs13010084

**Chicago/Turabian Style**

Yamaguchi, Tomoaki, Yukie Tanaka, Yuto Imachi, Megumi Yamashita, and Keisuke Katsura. 2021. "Feasibility of Combining Deep Learning and RGB Images Obtained by Unmanned Aerial Vehicle for Leaf Area Index Estimation in Rice" *Remote Sensing* 13, no. 1: 84.
https://doi.org/10.3390/rs13010084