Leaf-Counting in Monocot Plants Using Deep Regression Models.

Leaf numbers are vital in estimating the yield of crops. Traditional manual leaf-counting is tedious, costly, and an enormous job. Recent convolutional neural network-based approaches achieve promising results for rosette plants. However, there is a lack of effective solutions to tackle leaf counting for monocot plants, such as sorghum and maize. The existing approaches often require substantial training datasets and annotations, thus incurring significant overheads for labeling. Moreover, these approaches can easily fail when leaf structures are occluded in images. To address these issues, we present a new deep neural network-based method that does not require any effort to label leaf structures explicitly and achieves superior performance even with severe leaf occlusions in images. Our method extracts leaf skeletons to gain more topological information and applies augmentation to enhance structural variety in the original images. Then, we feed the combination of original images, derived skeletons, and augmentations into a regression model, transferred from Inception-Resnet-V2, for leaf-counting. We find that leaf tips are important in our regression model through an input modification method and a Grad-CAM method. The superiority of the proposed method is validated via comparison with the existing approaches conducted on a similar dataset. The results show that our method does not only improve the accuracy of leaf-counting, with overlaps and occlusions, but also lower the training cost, with fewer annotations compared to the previous state-of-the-art approaches.The robustness of the proposed method against the noise effect is also verified by removing the environmental noises during the image preprocessing and reducing the effect of the noises introduced by skeletonization, with satisfactory outcomes.

Wild emmer introgression alters root-to-shoot growth dynamics in durum wheat in response to water stress.

Water deficit during the early vegetative growth stages of wheat (Triticum) can limit shoot growth and ultimately impact grain productivity. Introducing diversity in wheat cultivars to enhance the range of phenotypic responses to water limitations during vegetative growth can provide potential avenues for mitigating subsequent yield losses. We tested this hypothesis in an elite durum wheat background by introducing a series of introgressions from a wild emmer (Triticum turgidum ssp. dicoccoides) wheat. Wild emmer populations harbor rich phenotypic diversity for drought-adaptive traits. To determine the effect of these introgressions on vegetative growth under water-limited conditions, we used image-based phenotyping to catalog divergent growth responses to water stress ranging from high plasticity to high stability. One of the introgression lines exhibited a significant shift in root-to-shoot ratio in response to water stress. We characterized this shift by combining genetic analysis and root transcriptome profiling to identify candidate genes (including a root-specific kinase) that may be linked to the root-to-shoot carbon reallocation under water stress. Our results highlight the potential of introducing functional diversity into elite durum wheat for enhancing the range of water stress adaptation.

A novel joint index based on peripheral blood CD4+/CD8+ T cell ratio, albumin level, and monocyte count to determine the severity of major depressive disorder.

BACKGROUND: Inflammation and immune status are correlated with the severity of major depressive disorder (MDD).The purpose of this study was to establish an optimization model of peripheral blood parameters to predict the severity of MDD. METHODS: MDD severity in the training and validation cohorts (n = 99 and 97) was classified using the Hamilton Depression Scale, Thirty-eight healthy individuals as controls. Significant severity-associated factors were identified using a multivariate logistic model and combined to develop a joint index through binary logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to identify the optimal model and evaluate the discriminative performance of the index. RESULTS: In the training cohort, lower CD4+/CD8+ T cell ratio, albumin level, and a higher monocyte percentage (M%) were significant as operating sociated with severe disease (P < 0.05 for all). The index was developed using these factors and calculated as CD4+/CD8+ T cell ratio, albumin level, and M%, with a sensitivity and specificity of 90 and 70%, respectively. The AUC values for the index in the training and validation cohorts were 0.85 and 0.75, respectively, indicating good discriminative performance. CONCLUSION: We identified disease severity-associated joint index that could be easily evaluated: CD4+/CD8+ T cell ratio, albumin level, and M%.

Loss of COMT activity reduces lateral root formation and alters the response to water limitation in sorghum brown midrib (bmr) 12 mutant.

Lignin is a key target for modifying lignocellulosic biomass for efficient biofuel production. Brown midrib 12 (bmr12) encodes the sorghum caffeic acid O-methyltransferase (COMT) and is one of the key enzymes in monolignol biosynthesis. Loss of function mutations in COMT reduces syringyl (S) lignin subunits and improves biofuel conversion rate. Although lignin plays an important role in maintaining cell wall integrity of xylem vessels, physiological and molecular consequences due to loss of COMT on root growth and adaptation to water deficit remain unexplored. We addressed this gap by evaluating the root morphology, anatomy and transcriptome of bmr12 mutant. The mutant had reduced lateral root density (LRD) and altered root anatomy and response to water limitation. The wild-type exhibits similar phenotypes under water stress, suggesting that bmr12 may be in a water deficit responsive state even in well-watered conditions. bmr12 had increased transcript abundance of genes involved in (a)biotic stress response, gibberellic acid (GA) biosynthesis and signaling. We show that bmr12 is more sensitive to exogenous GA application and present evidence for the role of GA in regulating reduced LRD in bmr12. These findings elucidate the phenotypic and molecular consequences of COMT deficiency under optimal and water stress environments in grasses.

Allelic variation in rice Fertilization Independent Endosperm 1 contributes to grain width under high night temperature stress.

A higher minimum (night-time) temperature is considered a greater limiting factor for reduced rice yield than a similar increase in maximum (daytime) temperature. While the physiological impact of high night temperature (HNT) has been studied, the genetic and molecular basis of HNT stress response remains unexplored. We examined the phenotypic variation for mature grain size (length and width) in a diverse set of rice accessions under HNT stress. Genome-wide association analysis identified several HNT-specific loci regulating grain size as well as loci that are common for optimal and HNT stress conditions. A novel locus contributing to grain width under HNT conditions colocalized with Fie1, a component of the FIS-PRC2 complex. Our results suggest that the allelic difference controlling grain width under HNT is a result of differential transcript-level response of Fie1 in grains developing under HNT stress. We present evidence to support the role of Fie1 in grain size regulation by testing overexpression (OE) and knockout mutants under heat stress. The OE mutants were either unaltered or had a positive impact on mature grain size under HNT, while the knockouts exhibited significant grain size reduction under these conditions.

HyperSeed: An End-to-End Method to Process Hyperspectral Images of Seeds.

High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds. As a test case, the hyperspectral images of rice seeds are obtained from a high-performance line-scan image spectrograph covering the spectral range from 600 to 1700 nm. The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation. The output is generated in the form of a hyperspectral cube and curve for each seed. In our experiment, we presented the visual results of seed segmentation on different seed species. Moreover, we conducted a classification of seeds raised in heat stress and control environments using both traditional machine learning models and neural network models. The results show that the proposed 3D convolutional neural network (3D CNN) model has the highest accuracy, which is 97.5% in seed-based classification and 94.21% in pixel-based classification, compared to 80.0% in seed-based classification and 85.67% in seed-based classification from the support vector machine (SVM) model. Moreover, our pipeline enables systematic analysis of spectral curves and identification of wavelengths of biological interest.

Dielectric Properties of Normal and Metastatic Lymph Nodes Ex Vivo From Lung Cancer Surgeries.

The dielectric properties of normal and tumor human tissues have been widely reported in recent years. However, the dielectric properties of intrathoracic lymph nodes (LNs) have not been reported. In this communication, we measured the dielectric properties (i.e., permittivity and conductivity) of ex vivo intrathoracic LNs obtained from lung cancer surgeries. Results show that the permittivity and conductivity of metastatic LNs are higher than those of normal LNs over the frequency range of 1 MHz-4 GHz. Statistically significant differences are observed at single specific frequencies (64, 128, 298, 433, and 915 MHz and 2.45 GHz). Our study provides the basic data to support future-related research and fills the research gap on the dielectric properties of LNs in the lungs. Bioelectromagnetics. 2020;41:148-155. © 2020 Bioelectromagnetics Society.

Measurement Method for Height-Independent Vegetation Indices Based on an Active Light Source.

A coefficient CW, which was defined as the ratio of NIR (near infrared) to the red reflected spectral response of the spectrometer, with a standard whiteboard as the measuring object, was introduced to establish a method for calculating height-independent vegetation indices (VIs). Two criteria for designing the spectrometer based on an active light source were proposed to keep CW constant. A designed spectrometer, which was equipped with an active light source, adopting 730 and 810 nm as the central wavelength of detection wavebands, was used to test the Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI) in wheat fields with two nitrogen application rate levels (NARLs). Twenty test points were selected in each kind of field. Five measuring heights (65, 75, 85, 95, and 105 cm) were set for each test point. The mean and standard deviation of the coefficient of variation (CV) for NDVI in each test point were 3.85% and 1.39% respectively, the corresponding results for RVI were 2.93% and 1.09%. ANOVA showed the measured VIs possessed a significant ability to discriminate the NARLs and had no obvious correlation with the measurement heights. The experimental results verified the feasibility and validity of the method for measuring height-independent VIs.

Nondestructive Determination of Nitrogen, Phosphorus and Potassium Contents in Greenhouse Tomato Plants Based on Multispectral Three-Dimensional Imaging.

Measurement of plant nitrogen (N), phosphorus (P), and potassium (K) levels are important for determining precise fertilization management approaches for crops cultivated in greenhouses. To accurately, rapidly, stably, and nondestructively measure the NPK levels in tomato plants, a nondestructive determination method based on multispectral three-dimensional (3D) imaging was proposed. Multiview RGB-D images and multispectral images were synchronously collected, and the plant multispectral reflectance was registered to the depth coordinates according to Fourier transform principles. Based on the Kinect sensor pose estimation and self-calibration, the unified transformation of the multiview point cloud coordinate system was realized. Finally, the iterative closest point (ICP) algorithm was used for the precise registration of multiview point clouds and the reconstruction of plant multispectral 3D point cloud models. Using the normalized grayscale similarity coefficient, the degree of spectral overlap, and the Hausdorff distance set, the accuracy of the reconstructed multispectral 3D point clouds was quantitatively evaluated, the average value was 0.9116, 0.9343 and 0.41 cm, respectively. The results indicated that the multispectral reflectance could be registered to the Kinect depth coordinates accurately based on the Fourier transform principles, the reconstruction accuracy of the multispectral 3D point cloud model met the model reconstruction needs of tomato plants. Using back-propagation artificial neural network (BPANN), support vector machine regression (SVMR), and gaussian process regression (GPR) methods, determination models for the NPK contents in tomato plants based on the reflectance characteristics of plant multispectral 3D point cloud models were separately constructed. The relative error (RE) of the N content by BPANN, SVMR and GPR prediction models were 2.27%, 7.46% and 4.03%, respectively. The RE of the P content by BPANN, SVMR and GPR prediction models were 3.32%, 8.92% and 8.41%, respectively. The RE of the K content by BPANN, SVMR and GPR prediction models were 3.27%, 5.73% and 3.32%, respectively. These models provided highly efficient and accurate measurements of the NPK contents in tomato plants. The NPK contents determination performance of these models were more stable than those of single-view models.

A 2-Protein Signature Predicting Clinical Outcome in High-Grade Serous Ovarian Cancer.

OBJECTIVE: High-grade serous ovarian cancer (HGSOC) accounts for approximately 70% deaths in ovarian cancer. The overall survival (OS) of HGSOC is poor and still remains a clinical challenge. High-grade serous ovarian cancer can be divided into 4 molecular subtypes. The prognosis of different molecular subtypes is still unclear. We aimed to investigate the prognostic values of immunohistochemistry-based different molecular subtypes in patients with HGSOC. METHODS: We analyzed the protein expression of representative biomarkers (CXCL11, HMGA2, and MUC16) of 3 different molecular subtypes in 110 formalin-fixed, paraffin-embedded HGSOC by tissue microarrays. RESULTS: High CXCL11 expression predicted worse OS, not disease-free survival (DFS; P = 0.028 for OS, P = 0.191 for DFS). High HMGA2 expression predicted worse OS and DFS (P = 0.037 for OS, P = 0.021 for DFS). MUC16 expression was not associated with OS or DFS (P = 0.919 for OS, P = 0.517 for DFS). Multivariate regression analysis showed that CXCL11 combined with HMGA2 signature was an independent predictor for OS and DFS in patients with HGSOC. CONCLUSIONS: CXCL11 combined with HMGA2 signature was a clinically applicable prognostic model that could precisely predict an HGSOC patient's OS and tumor recurrence. This model could serve as an important tool for risk assessment of HGSOC prognosis.

A Novel LiDAR-Based Instrument for High-Throughput, 3D Measurement of Morphological Traits in Maize and Sorghum.

Recently, imaged-based approaches have developed rapidly for high-throughput plant phenotyping (HTPP). Imaging reduces a 3D plant into 2D images, which makes the retrieval of plant morphological traits challenging. We developed a novel LiDAR-based phenotyping instrument to generate 3D point clouds of single plants. The instrument combined a LiDAR scanner with a precision rotation stage on which an individual plant was placed. A LabVIEW program was developed to control the scanning and rotation motion, synchronize the measurements from both devices, and capture a 360° view point cloud. A data processing pipeline was developed for noise removal, voxelization, triangulation, and plant leaf surface reconstruction. Once the leaf digital surfaces were reconstructed, plant morphological traits, including individual and total leaf area, leaf inclination angle, and leaf angular distribution, were derived. The system was tested with maize and sorghum plants. The results showed that leaf area measurements by the instrument were highly correlated with the reference methods (R² > 0.91 for individual leaf area; R² > 0.95 for total leaf area of each plant). Leaf angular distributions of the two species were also derived. This instrument could fill a critical technological gap for indoor HTPP of plant morphological traits in 3D.

An Information-Theoretic Framework for Evaluating Edge Bundling Visualization.

Edge bundling is a promising graph visualization approach to simplifying the visual result of a graph drawing. Plenty of edge bundling methods have been developed to generate diverse graph layouts. However, it is difficult to defend an edge bundling method with its resulting layout against other edge bundling methods as a clear theoretic evaluation framework is absent in the literature. In this paper, we propose an information-theoretic framework to evaluate the visual results of edge bundling techniques. We first illustrate the advantage of edge bundling visualizations for large graphs, and pinpoint the ambiguity resulting from drawing results. Second, we define and quantify the amount of information delivered by edge bundling visualization from the underlying network using information theory. Third, we propose a new algorithm to evaluate the resulting layouts of edge bundling using the amount of the mutual information between a raw network dataset and its edge bundling visualization. Comparison examples based on the proposed framework between different edge bundling techniques are presented.

Lung bone marrow-derived hematopoietic progenitor cells enhance pulmonary fibrosis.

RATIONALE: Bone marrow (BM)-derived cells have been implicated in pulmonary fibrosis. However, their precise role in pathogenesis is incompletely understood. OBJECTIVES: To elucidate roles of BM-derived cells in bleomycin-induced pulmonary fibrosis, and clarify their potential relationship to lung hematopoietic progenitor cells (LHPCs). METHODS: GFP BM-chimera mice treated with or without bleomycin were used to assess the BM-derived cells. MEASUREMENTS AND MAIN RESULTS: GFP(+) cells in the chimera lung were found to be comprised of two distinct phenotypes: GFP(hi) and GFP(low) cells. The GFP(hi), but not GFP(low), population was significantly increased after bleomycin treatment. Flow-cytometric analysis and quantitative real-time polymerase chain reaction revealed that GFP(hi) cells exhibited phenotypic characteristics of CD11c(+) dendritic cells and macrophages. GFP(hi) cell conditioned media were chemotactic for fibroblasts obtained from fibrotic but not normal lung in vitro. Moreover, adoptive transfer of GFP(hi) cells exacerbated fibrosis in recipient mice, similar to that seen on adoptive transfer of BM-derived CD11c(+) cells from donor bleomycin-treated mice. Next, we evaluated the potential of LHPCs as the source of GFP(hi) cells. Isolation of LHPCs by flow sorting revealed enrichment in cKit(+)/Sca1(-)/Lin(-) cells, most of which were GFP(+) indicating their BM origin. The number of LHPCs increased rapidly after bleomycin treatment. Furthermore, stem cell factor induced LHPC proliferation, whereas granulocyte-macrophage-colony stimulating factor induced differentiation to GFP(hi) cells. CONCLUSIONS: BM-derived LHPCs with a novel phenotype could differentiate into GFP(hi) cells, which enhanced pulmonary fibrosis. Targeting this mobilized LHPCs might represent a novel therapeutic approach in chronic fibrotic lung diseases.

RmdnCache: Dual-Space Prefetching Neural Network for Large-Scale Volume Visualization.

Volume visualization plays a significant role in revealing important intrinsic patterns of 3D scientific datasets. However, these datasets are often large, making it challenging for interactive visualization systems to deliver a seamless user experience because of high input latency that arises from I/O bottlenecks and limited fast memory resources with high miss rates. To address this issue, we have proposed a deep learning-based prefetching method called RmdnCache, which optimizes the data flow across the memory hierarchy to reduce the input latency of large-scale volume visualization. Our approach accurately prefetches the content of the next view to fast memory using learning-based prediction while rendering the current view. The proposed deep learning architecture consists of two networks, RNN and MDN in respective spaces, which work together to predict both the location and likelihood distribution of the next view for defining an optimal prefetching range. Our method outperforms existing state-of-the-art prefetching algorithms in reducing overall input latency for visualizing real-world large-scale volumetric datasets.

DC2Net: An Asian Soybean Rust Detection Model Based on Hyperspectral Imaging and Deep Learning.

Asian soybean rust (ASR) is one of the major diseases that causes serious yield loss worldwide, even up to 80%. Early and accurate detection of ASR is critical to reduce economic losses. Hyperspectral imaging, combined with deep learning, has already been proved as a powerful tool to detect crop diseases. However, current deep learning models are limited to extract both spatial and spectral features in hyperspectral images due to the use of fixed geometric structure of the convolutional kernels, leading to the fact that the detection accuracy of current models remains further improvement. In this study, we proposed a deformable convolution and dilated convolution neural network (DC2Net) for the ASR detection. The deformable convolution module was used to extract the spatial features, while the dilated convolution module was applied to extract features from the spectral dimension. We also adopted the Shapley value and the channel attention methods to evaluate the importance of each wavelength during decision-making, thereby identifying the most contributing ones. The proposed DC2Net can realize early asymptomatic detection of ASR even when visual symptoms have not appeared. The results of the experiment showed that the detection performance of DC2Net dominated state-of-the-art methods, reaching an overall accuracy at 96.73%. Meanwhile, the experimental result suggested that the Shapley Additive exPlanations method was able to extract feature wavelengths correctly, thereby helping DC2Net achieve reasonable performance with less input data. The research result of this study could provide early warning of ASR outbreak in advance, even at the asymptomatic period.

An online monitoring device for measuring the concentration of four types of in-situ microorganisms by using the near-infrared band.

Using optical density at 600 nm (OD600) to measure the microbial concentration is a popular approach due to its advantages like quick response and non-destructive. However, the OD600 measurement might be affected by the metabolic pigment, and it would become invalid when the solution dilution is insufficient. To overcome these issues, we proposed to adopt a more robust wavelength at 890 nm to quantify the attenuation of transmission light. After selecting this light source, we designed the light path and the circuit of the online monitoring device. Meanwhile, the random forest algorithm was introduced for temperature compensation and improving the stability of the device. This device was verified by monitoring the microbial concentration of four strains (Yeast, Bacillus, Arthrobacter, and Escherichia coli). The experimental result suggested that the mean absolute percentage error reached 4.11 %, 4.28 %, 4.49 %, and 4.53 % respectively, which is helpful to improve the accuracy of microbial concentration measurement.

radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenotype in pancreatic cancer.

Addressing the significant level of variability exhibited by pancreatic cancer necessitates the adoption of a systems biology approach that integrates molecular data, biological properties of the tumors, medical images, and clinical features of the patients. In this study, a comprehensive multi-omics methodology was employed to examine a distinctive collection of patient dataset containing rapid autopsy tumor and normal tissue samples as well as longitudinal imaging with a focus on pancreatic cancer. By performing a whole exome sequencing analysis on tumor and normal tissues to identify somatic gene variants and a radiomic feature analysis to tumor CT images, the genome-wide association approach established a connection between pancreatic cancer driver genes and relevant radiomic features, enabling a thorough and quantitative assessment of the heterogeneity of pancreatic tumors. The significant association between sets of genes and radiomic features revealed the involvement of genes in shaping tumor morphological heterogeneity. Some results of the association established a connection between the molecular level mechanism and their outcomes at the level of tumor structural heterogeneity. Because tumor structure and tumor structural heterogeneity are related to the patients' overall survival, patients who had pancreatic cancer driver gene mutations with an association to a certain radiomic feature have been observed to experience worse survival rates than cases without these somatic mutations. Furthermore, the association analysis has revealed potential gene mutations and radiomic feature candidates that warrant further investigation in future research endeavors.

Telomerase and telomere length in pulmonary fibrosis.

In addition to its expression in stem cells and many cancers, telomerase activity is transiently induced in murine bleomycin (BLM)-induced pulmonary fibrosis with increased levels of telomerase transcriptase (TERT) expression, which is essential for fibrosis. To extend these observations to human chronic fibrotic lung disease, we investigated the expression of telomerase activity in lung fibroblasts from patients with interstitial lung diseases (ILDs), including idiopathic pulmonary fibrosis (IPF). The results showed that telomerase activity was induced in more than 66% of IPF lung fibroblast samples, in comparison with less than 29% from control samples, some of which were obtained from lung cancer resections. Less than 4% of the human IPF lung fibroblast samples exhibited shortened telomeres, whereas less than 6% of peripheral blood leukocyte samples from patients with IPF or hypersensitivity pneumonitis demonstrated shortened telomeres. Moreover, shortened telomeres in late-generation telomerase RNA component knockout mice did not exert a significant effect on BLM-induced pulmonary fibrosis. In contrast, TERT knockout mice exhibited deficient fibrosis that was independent of telomere length. Finally, TERT expression was up-regulated by a histone deacetylase inhibitor, while the induction of TERT in lung fibroblasts was associated with the binding of acetylated histone H3K9 to the TERT promoter region. These findings indicate that significant telomerase induction was evident in fibroblasts from fibrotic murine lungs and a majority of IPF lung samples, whereas telomere shortening was not a common finding in the human blood and lung fibroblast samples. Notably, the animal studies indicated that the pathogenesis of pulmonary fibrosis was independent of telomere length.

FIZZ2/RELM-ß induction and role in pulmonary fibrosis.

Found in inflammatory zone (FIZZ) 2, also known as resistin-like molecule (RELM)-ß, belongs to a novel cysteine-rich secreted protein family named FIZZ/RELM. Its function is unclear, but a closely related family member, FIZZ1, has profibrotic activities. The human ortholog of rodent FIZZ1 has not been identified, but human FIZZ2 has significant sequence homology to both rodent FIZZ2 (59%) and FIZZ1 (50%). Given the greater homology to rodent FIZZ2, analyzing the role of FIZZ2 in a rodent model of bleomycin-induced pulmonary fibrosis would be of greater potential relevance to human fibrotic lung disease. The results showed that FIZZ2 was highly induced in lungs of rodents with bleomycin-induced pulmonary fibrosis and of human patients with idiopathic pulmonary fibrosis. FIZZ2 expression was induced in rodent and human lung epithelial cells by Th2 cytokines, which was mediated via STAT6 signaling. The FIZZ2 induction in murine lungs was found to be essential for pulmonary fibrosis, as FIZZ2 deficiency significantly suppressed pulmonary fibrosis and associated enhanced extracellular matrix and cytokine gene expression. In vitro analysis indicated that FIZZ2 could stimulate type I collagen and α-smooth muscle actin expression in lung fibroblasts. Furthermore, FIZZ2 was shown to have chemoattractant activity for bone marrow (BM) cells, especially BM-derived CD11c(+) dendritic cells. Notably, lung recruitment of BM-derived cells was impaired in FIZZ2 knockout mice. These findings suggest that FIZZ2 is a Th2-associated multifunctional mediator with potentially important roles in the pathogenesis of fibrotic lung diseases.

Application of machine learning in groundwater quality modeling - A comprehensive review.

Groundwater is a crucial resource across agricultural, civil, and industrial sectors. The prediction of groundwater pollution due to various chemical components is vital for planning, policymaking, and management of groundwater resources. In the last two decades, the application of machine learning (ML) techniques for groundwater quality (GWQ) modeling has grown exponentially. This review assesses all supervised, semi-supervised, unsupervised, and ensemble ML models implemented to predict any groundwater quality parameter, making this the most extensive modern review on this topic. Neural networks are the most used ML model in GWQ modeling. Their usage has declined in recent years, giving rise to more accurate or advanced techniques such as deep learning or unsupervised algorithms. Iran and the United States lead the world in areas modeled, with a wealth of historical data available. Nitrate has been modeled most exhaustively, targeted by nearly half of all studies. Advancements in future work will be made with further implementation of deep learning and explainable artificial intelligence or other cutting-edge techniques, application of these techniques for sparsely studied variables, the modeling of new or unique study areas, and the implementation of ML techniques for groundwater quality management.