Assessment of maize water status using a consumer-grade camera and thermal imagery

The analysis of plant response to water deficits can help us to identify appropriate water-saving and irrigation methods. The goal of this study is to assess the potential of eight indices derived from a modified consumer-grade camera and a thermal camera for monitoring the relative water content (RWC) of maize. The study design was a randomized complete block design with three replications and 16 treatments with four levels of irrigation water percentage based on field capacity (100% Fc, 80% Fc, 60% Fc and 40% Fc), and four levels of nitrogen (without nitrogen, 100 kg N/ha, 200 kg N/ha and 300 kg N/ha) were used. RWC values were used to evaluate the performance of the eight crop water stress indicators. The results showed that the best performance results of the studied vegetation indices were DANS, CTSD and CTCV, respectively. As observed, R values were 0.88, 0.76 and 0.67, respectively.


INTRODUCTION
As the world population grows, the world population's living standards increase, diets change and the effects of climate change intensify. So, the development of agriculture is critical for food security. Agriculture is both a major cause and casualty of water scarcity, especially in arid and semi-arid areas worldwide (FAO ). Consequently, a deeper understanding is necessary of abiotic stress such as drought, salinity and low or high temperatures on productivity demands and plant growth and development, through precision irrigation (Ihuoma & Madramootoo ).  (Sugiura et al. ; Santesteban et al. ; Zhang et al. a, b). However, satellite platforms have limitations such as high cost for high special resolution images, short availability of usable data (because clouds and cloud shadows may hide ground features), low temporal resolution and fixed schedule. On the other hand, most aerial imagery utility and availability are limited by the cost and complexity of the imaging system. So, consumer-grade cameras that have been designed to acquire imagery, reduce costs and increase the spatial resolution of images. Modified consumer-grade cameras, as another type of consumer-grade camera, replace the infrared (IR)-blocking filter in front of the complementary metal oxide semiconductor (CMOS) or charge coupled device (CCD) sensors with a long-pass IR filter to capture IR light spectral information details (Yang et al. ; Zhang et al. ). A low-cost modified camera that use external filters does not require special skill for opening the sensors and removing or changing them (Putra & Soni ).
Under water-stress, plant leaves display higher leaf temperature. Therefore, an infrared thermographic camera can measure canopy temperature. Hence, vegetation temperature can be used as an indicator of vegetation health. One issue that needs to be eliminated is the soil background from the thermal image. There are two commonly used methods to eliminate background pixels: (i) a threshold-based approach that uses thermal imagery only, and canopy temperature (Tc) extraction using algorithms such as Otsu and edge detec-

Experimental site and crop management
The field experiment was planned with a randomized complete block design and three replicates during 2018. An early maturing variety of maize in a greenhouse located in the western region of Iran (34 47 0 N, and 48 28 0 E) was used. Four irrigation treatments (I 1 : 100% (control), I 2 : 80%, I 3 : 60%, and I 4 : 40% of field capacity) and also four nitrogen treatments (N 1 : without nitrogen, N 2 : 100 kg N/ha, Acquisition and pretreatment of RGB, IR and thermal images Figure 1 shows some of the equipment and processing steps of extracting plant indices from RGB, IR and thermal images. To initiate image processing, we registered visible and infrared images using MATLAB R2017a (Version 9.2). The image registration algorithm in shown in Figure 2. In the next step, for background elimination, the red-green ratio index (RGRI) was calculated using Equation (1), where R and G represent the digital numbers of the red and green bands.
Then the Otsu algorithm was applied to the RGRI to separate green plants from the background (Verrelst et al. ).
Otsu's algorithm is undoubtedly one of the most suitable thresholding techniques. It uses the image histogram data as input and finds a pixel value (so-called threshold level) that is able to separate pixels into two classes, foreground and background (Bangare et al. ). Therefore, the maize fractional vegetation cover (FVC) on RGB images was extracted. As shown in Figure 1, the FVC was used as a binary mask to segment the green plants against the background by multiplying FVC in the vegetation indices.
In the next step, to take thermal images, an FLIR ONE thermal imager for Android (resolution 0.1 C) was used.
Afterward, the spatial resolution of the RGB and FVC images was resampled to match the scale of the thermal images by using the nearest-neighbour interpolation algor-  Table 2. We obtained these indices in each plant and compared with RWC by statistical methods.

Statistical analyses
The regression models between vegetation indices and RWC were developed. Then the adjusted determination coefficient were calculated for comparisons. RMSE and MBE are given in Equations (10) and (11): where: N is the number of sample data, Yobs i is the observed values and Yest i the estimated values.

RESULT AND DISCUSSION
Irrigation water applied, and leaf nitrogen status    observed. After silking, N content slightly increased then continued to decline (Schepers et al. ).

Distribution of ground-truth Tc and RWC
In Figure 4, the box plot shows the RWC rate data over the study. Regarding the irrigation scheduling (Figure 3(a)), RWC values were varied in each day of the growth stage and their values were also lower when water stress increased. Figure 5 shows the distributions of ground-truth Tc and RWC for I 1 , I 2 , I 3 and I 4 based on data acquired on DAS 36 and 48, respectively. As shown in Figure 5, there was a difference between the distributions of groundtruth Tc and RWC among treatment groups. The results, which are not presented in this paper, showed that there were also not significant interaction effects between nitrogen and irrigation on ground-truth Tc and RWC. In I 1 treatment, soil moisture was always maintained at field capacity level, so RWC was higher than 80% and the temp- Consequently, the temperatures derived from thermal images were accurate (Zhang et al. ).

Relationships between crop water stress indicators and
RWC of maize

CONCLUSION
The use of both modified consumer-grade cameras and thermal cameras allowed the estimating of RWC, to assess maize water status. In fact, high correlations were found between indices based on thermal images with maize water status. To improve the accuracy of Tc extraction and background elimination, we used the RGRI-Otsu method based on the combination of RGB and thermal images.
The Tc extracted was highly correlated with the groundtruth measurements with R 2 of 0.87 and RMSE of 1.41 C.  This finding showed that DANS, CTSD and CTCV can estimate maize water stress and were also simpler than the CWSI index. This method is low-cost and easy to use; therefore, there is a huge potential for implementing it on UAVs that allow farmers to observe their fields from the sky. However, further studies are required to test the performance of it for other crops and climatic and farming-practice conditions.

DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.