1. Introduction
About 70–90% of global water resources are consumed through agriculture [
1]. Extreme water use in the agricultural sector also causes water scarcity in semi-arid and arid regions [
2]. Rice is one of the most economical crops that requires large amounts of water. Decreasing water availability for agriculture threatens the productivity of the irrigated rice ecosystem and ways to save water and increase the water productivity of rice must be found [
3]. By optimizing the timing, duration, and amount of irrigation and drainage, water-saving irrigation practice has been proven effective in saving water and increasing rice yield [
4]. To improve the water use efficiency of plants, it is important to accurately and timely predict crop water status. Canopy water content (CWC) is a significant factor in regard to the water use efficiency of plants [
5], drought assessment [
6], a key input variable in irrigation management decisions, and crop ripening monitoring [
7]. Moreover, it is a vital parameter that reflects plant physiological status and health [
8]. It is one of the indicators widely used to evaluate a plant’s water condition [
9]. Traditionally, there are various manners of observing the water state in a plant such as lab-based leaf water content analysis [
10], stomatal conductance [
11], and sap flow measurement [
12]. Despite the high precision delivered by these ways, they are time consuming, destructive and laborious, particularly in large-scale areas [
13]. Moreover, the interest in monitoring the plant water status has increased in the scientific community through remote sensing, which is very widely used to reliably retrieve leaf water content [
8].
We focused on modern procedures for estimation of the CWC from optical remote sensing data, especially from visible (RGB: red–green–blue) and thermal imaging. These have several benefits compared to hyperspectral sensors. Thermal sensing is useful since it is cheap, non-destructive, convenient, and can be done remotely [
14]. Digital imaging may provide a low-cost and easy-to-use alternative [
15]. The hyperspectral camera is heavy, expensive, and requires professional imaging and data processing [
16]. The superiority of proximity sensing technology with regression algorithm relies on three major aspects: hand-crafted feature selection, data fusion for different sensors, and selecting the best model hyperparameters. Elsherbiny et al. [
17] explained that the regression algorithms could be upgraded for robust prediction of rice water status through some actions, including high-level features nominating, hyperparameters optimization, providing various alternatives for the most sensitive features, and integrating the model with the best-combined features. Primary, common methods for extracting RGB-based features include color vegetation indices (VI) and gray level co-occurrence matrix-established texture features (GLCMF). Several studies have found that the crop water deficit will directly affect the biochemical processes and morphology of crops, and this effect can be observed in the color changes of the crop leaves. Digital images are composited by pixels, which are a combination of the color channels RGB. The VI can be calculated from these channels [
18]. Wang et al. [
19] generated regression models using the G–R parameter of the RGB color system with the moisture content of cotton and the moisture content index. The prediction accuracies were up to 90.7% and 91.0%, respectively. Zakaluk and Sri [
20] checked the use of artificial neural networks (ANN) for digital images, taken by a 5 megapixel RGB camera to predict the leaf water potential of potato plants. Then, the relationship between leaf water potential and image features was determined.
Another variant of GLCMF was derived from RGB images to estimate the water situation in plants. Wenting et al. [
21] concluded the relationship between GLCMF of the RGB image of crop leaves and leaf water content at heading-stage maize with an expected R
2 value of 0.702 and the RMSE was within ±2%. Shao et al. [
22] indicated that GLCMF-based partial least square regression model can predict water content in grapevines with better performance. The prediction R
2 was 0.833 with an RMSE of 1.049. Additionally, the thermal imaging system can provide an indication of the water condition in the plant. Leaf temperature is an alternative measure that is a suitable indicator of a plant’s water state [
23]. Moller et al. [
24] showed that thermal imagery can be used to accurately estimate crop water status. Ballester et al. [
25] used a handheld infrared thermal imager to study leaf moisture content in citrus trees and persimmon trees.
The integration of different data sources was a vital component of this work to improve the quality and robustness of the CWC prediction model. This has previously been used to evaluate plant phenotypes. Moller et al. [
24] elucidated that the fusion of visible and thermal imaging could develop the accuracy of the crop water stress index (CWSI) computation and provide accurate data on the crop water status of grapevines. Shao et al. [
22] clarified that the incorporation of both GLCMF and reflectance features could enable the prediction of water content of grapevines and be beneficial for grapevine management and achieved prediction R
2 of 0.900 with an RMSE of 0.826.
The hyperparameter optimization of the model is another factor that has a significant impact on the efficiency of water content prediction. The performance of any machine learning (ML) model is profoundly affected by hyperparameter selection, which has several benefits: it can improve the performance of ML algorithms [
26] and improve the reproducibility and fairness of scientific studies [
27]. Furthermore, it could perform a major role in improving the prediction model because of the direct control of the behaviors of training algorithms [
28]. In this research, the ANN building possibility was used as a tool to support accurate irrigation management decision making by means of visible and thermal sensors. Dawson et al. [
29] developed an ANN model for predicting water content in leaves depending on single leaf reflectance and transmittance, which are important input variables to vegetation canopy reflectance models. The R
2 was defined as 0.86 with an RMSE of 1.3%. Marti et al. [
30] described the application of ANN to estimate stem water potential from soil moisture at different depths and standard meteorological variables, such as temperature, relative humidity, and solar radiation. The ANN presented high-performance accuracy with an optimum R
2 of 0.926.
To maximize irrigation in rice fields, the plant’s water content should be explored accurately via proximal sensing systems. Although visible and thermal imagery are widely used to estimate water content in plants, the best performing algorithm has a high priority in research interests. Based on our knowledge, no prior research has implemented the hybrid methodology of BPNN with features extracted from thermal and RGB imaging to develop a predictive model for accurate estimation of the water content status of rice under different irrigation water treatments and climate change. Hence, the main objectives of this study were (i) to create a well-structured CWC modeling of rice relying on high-level features derived from RGB and thermal images, (ii) to define the superior variants of thermal and RGB features for robust CWC prediction, and (iii) to evaluate the performance of BPNN models using the best-combined features and to adopt the finest model that could be recommended for precision irrigation in the future.
4. Discussion
Color and texture are two important aspects in digital imagery. Plant temperature is also a remarkable tool in thermal imaging, which can be used as an indicator to identify a plant’s water condition. The plant color feature can be used for plant stress assessment [
63]. Texture analysis is significant in many areas such as remote sensing and its common applications include image classification and pattern recognition [
64]. These features of VI and GLCMF were applied with a neural network for CWC quantification in rice. The results confirmed that the neural network behavior with GLCMF was superior to VI for predicting the water status of plants. This is similar to applying classification in the following studies: Jana et al. [
65], to classify eight varieties of fruits, and Dubey and Jalal [
66], to recognize diseases in fruits. Texture features performed better than color features for classification via support vector machine algorithm with accuracy, 32.29% and 69.79% and 83.5% and 88.56%, respectively. Moreover, the thermal analysis results agreed with the findings of Jones [
67], who identified that water content or transpiration has an inverse relationship with leaf temperature. The CWC values were highly influenced owing to the loss of water through transpiration; CWSI and NRCT reflected normalized temperature values for the crop. These results are in agreement with Blum et al. [
68], who indicated that the CWC decreases with increasing canopy temperature as a result of increased water stress.
The current work has allowed a more accurate determination of the water conditions in rice, using the best-combined features extracted from RGB and thermal images. The proposed approaches use all of the sensitive features to changes in the CWC that greatly increase the model performance. Our outcomes achieved greater accuracy than those of Alchanatis et al. [
69], who demonstrated that fusion of thermal and visible imaging can provide precise data on the crop water status of grapevines, and the CWSI was highly correlated with measured stomatal conductance with an R
2 value of 0.97. The developed model outperformed Leinonen and Jones [
70], who stated that the combination of thermal and visible imagery was a more accurate tool for estimating canopy temperature and could be used to identify water deficit stress in a plant. They investigated the relationship between the measured stomatal conductance and the calculated CWSI with an R
2 of 0.867. Likewise, the first-order model of the BPNN-VI-GLCM-T-21 was very precise in contrast to that of Sun et al. [
71], who referred to a reasonable model for computing CWC in wheat based on the ratio vegetation index (RVI: 1605 and 1712 nm) and the normalized difference vegetation index (NDVI: 1712 and 1605 nm), having the highest R
2 and lowest RMSE in model calibration and validation (R
2c = 0.74 and 0.73; RMSEC = 0.026 and 0.027; R
2v = 0.72 and 0.71; RMSEV = 0.028 and 0.029). Furthermore, the proposed model is better than Ge et al. [
72], who concluded that leaf water content in maize at a pot-scale is successfully predicted with the hyperspectral images using the PLSR model for two genotypes in model cross-validation (R
2 = 0.81 and 0.92; RMSE = 3.7 and 2.3; MAPE = 3.6 and 2.2). The created model achieved high performance compared to Pandey et al. [
73], who reported that PLSR analysis can be performed to predict the leaf water content of pot-grown maize and soybean plants with the highest accuracy (R
2 = 0.93, RMSE = 1.62, and MAPE = 1.6%) for validation. In addition, this research is in agreement with the following study in that the combination of color–GLCM–thermal features are high-quality variants in regression or classification applications. Bhole et al. [
74] used different features such as the color (CM: color moments and CCV: color coherence vector), texture (GLCMF), and thermal (T) with a random forest for classification of eleven categories of fruits. The results showed that the model accuracies for integrating GLCM-T, CM-T, CCV-T, and CM-CCV-GLCM-T features were 84.26%, 91.17%, 92.95%, and 93.4%, respectively.
Finally, remote and proximal sensing images acquired with high-resolution cameras, mounted at ground level or on unmanned aerial vehicles (UAV), have spatial resolutions of a few centimeters. These images can provide sufficient accurate information for both assessing plant water status in the field and implementing appropriate irrigation management strategies [
75]. Thus, this work can assist in improving inexpensive, effective high-throughput phenotyping platforms for large numbers of breeding plants at different levels of irrigation. A practical sharing of research may improve site-specific rice irrigation management through designing an intelligent irrigation system based on the best-proposed model. The methodology of this study that relied on ground-based cameras can be scaled up to UAV-based applications to increase productivity [
76] and monitor the water condition of crops in large-scale areas.
5. Conclusions
Estimation of canopy water content (CWC) is highly important in precision plant reproduction and agricultural development. Low-cost outdoor cameras, such as visible and thermal imaging systems, would be an applicable implement for predicting water content in the plant. Therefore, the present study explored the ability to incorporate top-level features retrieved from visible and thermal imaging with a back-propagation neural network (BPNN) to adopt a three-stage model of CWC for rice. Hand-crafted features, including 20 vegetation indices (VI), 6 GLCM texture features (GLCMF), and 2 thermal indicators (T), were identified for analysis. The experimental results showed that the proposed model of BPNN-VI-GLCMF-T provided effective recognition of CWC in the rice crop. Expectation accuracy increased to 99.4% by conjoining 21 superlative features. At hidden neuron layers (8,9), the R2 raised to 0.983 with an RMSE of 0.599. The models with separate features performed lower than the best built-in features. Their corresponding values with prediction R2 were 0.632 (RMSE = 2.788), 0.679 (RMSE = 2.607), and 0.803 (RMSE = 2.043) using 14VI, 5GLCM, and 2T, respectively. Ultimately, the superlative model has a high level of confidence and reliable outcomes. In the future, this tool may open an avenue for rapid, high-throughput assessments of the water condition of plants, as well as being equally important for procedures related to agricultural water management.