Incorporating machine learning models and remote sensing to assess the spatial distribution of saturated hydraulic conductivity in a light-textured soil

Rezaei, Meisam; Mousavi, Seyed Rohollah; Rahmani, Asghar; Zeraatpisheh, Mojtaba; Rahmati, Mehdi; Pakparvar, M; Mahjenabadi, Vahid Alah Jahandideh; Seuntjens, Piet; Cornelis, Wim

doi:10.1016/j.compag.2023.107821

Cited by 21 publications

(10 citation statements)

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“…The prediction of irrigation WY was accomplished using ANN and RF algorithms, along with auxiliary variables such as topographic attributes, remotely sensed indices, and soil variables. We chose these algorithms due to their success in digital soil mapping, as demonstrated in previous studies ( Saeed et al., 2017 ; Rostaminia et al., 2021 ; Rezaei et al., 2023 ). More detailed information about the performance of utilized ML algorithms is given below.…”

Section: Methodsmentioning

confidence: 99%

“…where, a i and b i are the observed and predicted values,

and

, are the average of the observed and predicted values, r is the correlation coefficient between the observed and predicted values, and

, and

are the variance of the observed and predicted values. To assess the accuracy of the results and model performances, the Kruskal–Wallis (KW) test was used to identify any statistically significant differences in performance among MLAs ( Demir and Citakoglu, 2023 ; Rezaei et al., 2023 ).…”

Section: Methodsmentioning

confidence: 99%

“…RF models leverage an ensemble of decision trees to make robust predictions, while ANNs models simulate the interconnectedness of neurons in the human brain to capture complex relationships (Liu et al, 2012). These algorithms have been successfully applied in various agricultural contexts, such as digital soil mapping (Rostaminia et al, 2021;Mousavi et al, 2022;Khosravani et al, 2023;Rezaei et al, 2023), showcasing their effectiveness in predicting crop yields based on environmental factors and soil properties (Taghizadeh-Mehrjardi et al, 2020;Wang et al, 2020;Basir et al, 2021). As regards, Boori et al (2023) indicate that the rapid advances in satellite technologies and MLAs, particularly ANNs, have the potential to offer affordable and comprehensive solutions for accurate grain prediction.…”

Section: Introductionmentioning

confidence: 99%

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Spatial prediction of winter wheat yield gap: agro-climatic model and machine learning approaches

Mousavi,

Jahandideh Mahjenabadi,

Khoshru

et al. 2024

Front. Plant Sci.

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This study aimed to identify the most influential soil and environmental factors for predicting wheat yield (WY) in a part of irrigated croplands in southwest Iran, using the FAO-Agro-Climate method and machine learning algorithms (MLAs). A total of 60 soil samples and wheat grain (1 m × 1 m) in 1200 ha of Pasargad plain were collected and analyzed in the laboratory. Attainable WY was assessed using the FAO method for the area. Pearson correlation analysis was used to select the best set of soil properties for modeling. Topographic attributes and vegetation indices were used as proxies of landscape components and cover crop to map actual WY in the study area. Two well-known MLAs, random forest (RF) and artificial neural networks (ANNs), were utilized to prepare an actual continuous WY map. The k-fold method was used to determine the uncertainty of WY prediction and quantify the quality of prediction accuracy. Results showed that soil organic carbon (SOC) and total nitrogen (TN) had a positive and significant correlation with WY. The SOC, TN, normalized different vegetation index (NDVI), and channel network base level (CHN) were recognized as the most important predictors and justifying more than 50% of actual WY. The ANNs outperformed the RF algorithm with an R2 of 0.75, RMSE of 400 (kg ha−1), and RPD of 2.79, according to statistical indices. The uncertainty analysis showed that the maximum uncertainty of the prediction map [400 (kg ha−1)] was very low compared to the mean value [4937 (kg ha−1)] of WY map. Calculation yield gap using the FAO-agro-climatic model showed that the average yield gap of the region was about 50% of actual yield. The findings of this study demonstrated that integrating simulated attainable crop growth using crop model and a set of soil and environmental covariates with the ANNs algorithm can effectively predict WY gaps in large areas with acceptable and reasonable accuracy. The study emphasizes that the implementation of efficient management practices has the potential to enhance agricultural production in the study area and similar regions. These results represent a significant advancement of sustainable agriculture and provide valuable insights for ensuring global food security.

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Section: Methodsmentioning

confidence: 99%

“…where, a i and b i are the observed and predicted values,

and

, are the average of the observed and predicted values, r is the correlation coefficient between the observed and predicted values, and

, and

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatial prediction of winter wheat yield gap: agro-climatic model and machine learning approaches

Mousavi,

Jahandideh Mahjenabadi,

Khoshru

et al. 2024

Front. Plant Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…where, a i and b i are the observed and predicted values, a and b, are the average of the observed and predicted values, r is the correlation coefficient between the observed and predicted values, and ∂ a , and ∂ b are the variance of the observed and predicted values. To assess the accuracy of the results and model performances, the Kruskal-Wallis (KW) test was used to identify any statistically significant differences in performance among MLAs (Demir and Citakoglu, 2023;Rezaei et al, 2023).…”

Section: Model Validation and Uncertainty Analysis 261 Model Validationmentioning

confidence: 99%

“…RF models leverage an ensemble of decision trees to make robust predictions, while ANNs models simulate the interconnectedness of neurons in the human brain to capture complex relationships . These algorithms have been successfully applied in various agricultural contexts, such as digital soil mapping (Rostaminia et al, 2021;Mousavi et al, 2022;Khosravani et al, 2023;Rezaei et al, 2023), showcasing their effectiveness in predicting crop yields based on environmental factors and soil properties (Taghizadeh-Mehrjardi et al, 2020;Wang et al, 2020;Basir et al, 2021). As regards, Boori et al (2023) indicate that the rapid advances in satellite technologies and MLAs, particularly ANNs, have the potential to offer affordable and comprehensive solutions for accurate grain prediction.…”

Section: Introductionmentioning

confidence: 99%

Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Plant Breeding

Orlov

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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The elusive link between soil physics and catchment hydrology

Sidle,

Saito

2024

Hydrological Processes

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Incorporating machine learning models and remote sensing to assess the spatial distribution of saturated hydraulic conductivity in a light-textured soil

Cited by 21 publications

References 87 publications

Spatial prediction of winter wheat yield gap: agro-climatic model and machine learning approaches

Spatial prediction of winter wheat yield gap: agro-climatic model and machine learning approaches

Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Plant Breeding

The elusive link between soil physics and catchment hydrology

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