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Performance evaluation of machine learning algorithms for estimating reference evapotranspiration based on NASA POWER weather data: a case study in Nigeria
(2026-01-01)
Faloye, Oluwaseun Temitope
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Awotoye, Grace
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Eludire, Oluwadamilare Oluwasegun
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Olaleye, Oluwatobi Solomon
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Oluwadare, Ayoola Olamitomi
The Penman–Monteith (PM) method is recognized as the globally accepted approach for estimating reference evapotranspiration (ETo). However, its use is constrained in areas with limited or unavailable data. Predicting ETo using multiple support vector machine (SVM) kernels and decision tree (DT) ensembles with NASA POWER data is innovative, as previous SVM-based ETo prediction studies have relied primarily on linear kernels. This study aims to evaluate the performance of different machine learning (ML) models, specifically SVM and DT and their ensembles, using NASA Power data as input. For this purpose, ML models were trained using average values of the monthly climatic data (maximum and minimum air temperatures, relative humidity, and wind speed) from NASA POWER. ETo was used as the output variable and was calculated from ground-observed data using the PM method. The developed ML models underwent training and validation to determine ETo in areas with different weather conditions in Nigeria: Kano—dry weather, Onne—wet weather, and Ibadan—moderate weather. Thirty and 70 % of the data were used during training and validation, respectively. The SVMs used in this study include linear SVM, quadratic SVM, cubic SVM, fine Gaussian (FG) SVM, medium Gaussian (MG) SVM, and coarse Gaussian SVM. The decision trees include fine, medium, and coarse trees, along with their ensembles: bagged and boosted trees. The model performance was evaluated using various error metrics. The FG SVM model exhibited the most accurate and precise estimation of ETo, with root mean square error (RMSE) values of 0.38 and 0.599 mm during the training and testing phases, respectively. Additionally, the coefficient of determination (r2) was good, with values of 0.87 and 0.72 during training and validation. The FG SVM outperformed all other models across all study locations, demonstrating its robustness in predicting ETo despite the contrasting weather conditions. Overall, this study revealed that the integration of data from NASA POWER with FG SVM accurately estimated reference evapotranspiration, which is important for effective water resource management in areas where ground climatic data is unavailable.
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Forecasting maize yield from growth parameters using machine learning in a biochar-inorganic fertilizer amended soil under drip irrigation
(2025-12-01)
Faloye, Oluwaseun Temitope
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Ajayi, Ayodele Ebenezer
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Kamchoom, Viroon
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Sinsamutpadung, Natdanai
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Adeyeri, Oluwafemi
The combined application of biochar and inorganic fertilizers has demonstrated significant potential to enhance crop productivity under both rainfed and irrigated conditions. However, predictive modeling approaches utilizing machine learning (ML) to simulate field outcomes under diverse agronomic scenarios remain understudied. This study addresses two critical objectives: (i) to evaluate the efficacy of ML models—Support Vector Machine (SVM), Artificial Neural Network (ANN), and Boosted Trees (BT)—in predicting maize grain yieldin biochar-inorganic fertizer amended soil under drip irrigation; and (ii) to identify the growth stage(s) and ML models that deliver the most accurate predictions. A three-year factorial field experiment was conducted during dry seasons, testing five biochar rates (0, 3, 6, 10 and 20 t/ha), two fertilizer levels (0 and 300 kg/ha), and deficit irrigation treatments (60%, 80%, and 100% of full irrigation). Growth parameters were measured at vegetative (35 days after planting – DAP), flowering stage (49 DAP), and maturity stage (77 DAP), with grain yield recorded at harvest (90 DAP). The measured growth parameters at the different DAP were used for the grain yield forecast. 70, 15 and 15% of the dataset were used for model training, validation and testing, respectively. Field results revealed progressive increases in growth parameters from vegetative to maturity stages, with treatment efficacy following the order: control < biochar-only < fertilizer-only < combined biochar-fertilizer. ML predictions mirrored this hierarchy, with ANN achieving superior accuracy (R² = 0.73–0.85, RMSE = 0.43–0.76, NRMSE = 0.095–0.17 at maturity) compared to SVM and BT. Predictive performance was weakest at the vegetative stage (35 DAP) but improved during flowering (49 DAP) and maturity (77 DAP), underscoring the importance of later growth data for reliable yield forecasting. This study demonstrates that ML models, particularly ANN, can effectively predict maize yield using accessible growth metrics, offering a cost- and labor-efficient complement to traditional field research. By enabling rapid scenario analysis, such models empower stakeholders to optimize resource allocation and inform crop management decisions under varying irrigation and soil amendment strategies.
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Hydro-physical and chemical suitability of rosewood sawdust as a hydroponic substrate under drip irrigation
(2025-11-01)
Samuel, Smart Idumoro
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Faloye, Oluwaseun Temitope
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Okunola, Abiodun Afolabi
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Adediran, Adeolu
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Kamchoom, Viroon
Comprehensive characterization of hydroponic substrate is important to determine its suitability as a growing media. Therefore, the suitability hypothesis was tested by determining the rosewood sawdust substrate hydrological response at different sizes:<0.425, 0.425–1.70 and 1.70–2.00mm. The physical and chemical properties of the substrates were determined in the laboratory using standard procedures. Water storage and air capacity of the substrates were determined while regression models were developed for the water storage prediction with respect to the substrate sizes and pipeline distance away from the fertigation source. The highest total porosity of 75.92% was obtained for the large particle, while the lowest value of 72.57% was recorded in the finest particle and thus translated to improved moisture content and storage efficiency. The values of field capacity obtained for the coarse and fine particle were 133 and 159%, respectively. The developed regression model for the water storage produces coefficient of determination (R2) greater than 0.6 (60%), indicating a good prediction. Results showed that major nutrients required for plants growth, in the rosewood enhanced the nutrients (N, P, K, Mg, Ca) in the applied solution, and were mostly considered normal, when compared to the standard. However, the electrical conductivity of 31.2 mS cm-1 obtained in the substrate was too high, thus necessitating the need to pre-treat it for reduced electrical conductivity (EC) before use. Therefore, considering the enhancement in the nutrients solution when applied to the rosewood, the substrate is recommended for growing crops in hydroponics.
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Simulation of emitter discharge along drip laterals under drip fertigation system using artificial neural network
(2025-07-01)
Faloye, Oluwaseun Temitope
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Samuel, Smart Idumoro
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Okunola, Abiodun Afolabi
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Kamchoom, Viroon
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Sinsamutpadung, Natdanai
Simulation of emitter discharge under a drip fertigation system is important for capturing the variation in water and nutrient distribution to crops. This is important for an effective design and irrigation management for agricultural crops. Moreover, the field discharge measurements are laborious and time-consuming, hence the need for the development of a representative model. The application of artificial neural network to simulate drip emitter along drip laterals is new in the field of flow measurement under drip irrigation. The purpose of this study is to predict the emitter discharge along drip laterals using artificial neural network (ANN) and evaluate the performance of the model. The input parameters fed into the ANN include; pipe length away from the fertigation source, elevation heads and distance of emitter point along the laterals. The field measured discharge was considered as the output. Evaluation parameters considered for the designed drip fertigation system indicated high efficiency, in the range between 81 and 98%. Interaction effects were observed between the pipe length and elevation head on the uniformity coefficient (CU) and emitter discharge. When all data were simulated, the ANN model simulated the emitter discharge accurately and precisely along the drip laterals, with R2 value ranging between 0.81 and 0.89, while the normalized root mean square error (NRMSE) was mostly below 20%, thus indicating a good prediction. The mean absolute error ranged between 0.034 and 0.048. Therefore, the ANN model was efficient for capturing the variation in emitter discharge well under the drip fertigation system.
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Development of Soil Wetting Front Estimation Models in Sandy Soil with a Hard Pan Under Drip Irrigation Using Empirical and Response Surface Methodologies
(2025-02-01)
Faloye, Oluwaseun Temitope
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Ezeh, Amanda
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Kamchoom, Viroon
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Abioye, Oluwaseyi Matthew
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Ikubanni, Peter Pelumi
An accurate estimation of a soil wetting front is important for improving water-use efficiency under drip irrigation. This study is aimed at determining the wetting front of sandy soil under drip irrigation using the existing and modified empirical equations and optimizing the input parameters using the response surface methodology (RSM). This study was conducted during the dry season in sandy soil containing a hard pan at a soil depth of about 30 cm. The soil wetting front was measured using measuring tape at two emitter discharges under a point-source drip irrigation system type. An evaluation of the models for the wetting fronts during calibration and validation showed that the coefficient determination (r2) ranged between 77–98% and 79–99% for the empirical and modified models, respectively. The normalized root mean square error (NRMSE) was less than 20% and greater than 20% for the modified and existing equations, respectively, thus emphasizing that the modified model produced a better result. Using the RSM approach, the linear + interaction produced the best result, and the optimization result revealed an optimum irrigation time and volume of 75 min and 225 cm3, corresponding to a maximum wetting depth and width of 26.8 and 21.7 cm, respectively.
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Evaluation of various machine learning-based bias correction approaches for NASA POWER air temperatures: a case study of Nigeria
(2025-01-01)
Faloye, Oluwaseun Temitope
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Kamchoom, Viroon
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Ogunrinde, Akinwale T.
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Ajayi, Ayodele Ebenezer
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Oguntunde, Philip Gbenro
Remotely sensed air temperature data from NASA POWER are widely used in regions with scarce climatic observations, particularly for agricultural applications such as calculating crop water requirements. This study employed a suite of machine learning (ML) algorithms to correct biases in NASA POWER air temperature outputs, including multiple support vector regression (SVR) variants—Linear SVR, Quadratic SVR, Cubic SVR, Fine Gaussian SVR, Medium Gaussian SVR, Coarse Gaussian SVR—and ensemble decision tree models: bagged trees (BGT) and boosted trees (BT). The objective of this study was to assess the ability of different ML algorithms to reduce biases in NASA POWER air temperature data, with the broader goal of identifying the most suitable ML method for air temperature bias correction in Nigeria. For this analysis, we used daily air temperature records from seven meteorological stations across diverse regions of Nigeria. The performance of NASA POWER minimum and maximum air temperature datasets was evaluated using standard error metrics. Subsequent application of ML algorithms significantly improved data accuracy: the normalized root mean square error (NRMSE) of the corrected outputs was mostly below 10%, indicating excellent predictive performance when ML was integrated. Among the SVR variants tested, Fine Gaussian SVR consistently yielded the best prediction results. This finding suggests that Fine Gaussian SVR is a robust tool for enhancing the reliability of air temperature data—critical for improving the accuracy of crop water requirement calculations in regions where in-situ air temperature observations are limited.
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Modeling and Optimization of Maize Yield and Water Use Efficiency under Biochar, Inorganic Fertilizer and Irrigation Using Principal Component Analysis
(2024-10-01)
Faloye, Oluwaseun Temitope
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Ajayi, Ayodele Ebenezer
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Oguntunde, Philip Gbenro
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Kamchoom, Viroon
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Fasina, Abayomi
This study was conducted to predict the grain yield of a maize crop from easy-to-measure growth parameters and select the best treatment combinations of biochar, inorganic fertilizer, and irrigation for the maize grain yield and water use efficiency (WUE) using the Principal Component Analysis (PCA) technique. Two rates of biochar (0 and 20 t ha−1) and fertilizer (0 and 300 kg ha−1) were applied to the soil, with maize crop planted, and subjected to deficit irrigation at 60, 80, and 100% of full irrigation amounts (FIA). Maize growth parameters (number of leaves—NL, leaf area—LA, leaf area index—LAI, and plant height—PH) were measured weekly. The results showed that the developed principal component regression (PCR) from the easy-to-measure growth parameters were strong and moderate in predicting the maize yield and WUE, with coefficient of determination; r2 values of 0.92 and 0.56, respectively. Using the PCA technique, the integration of irrigation with the least amount of water (60% FAI) with biochar (20 t ha−1) and fertilizer (300 kg ha−1) produced the highest ranking on grain yield and water use efficiency. This optimization technique showed that with the adoption of the integrative approach, 40% of irrigation water could be saved for other agricultural purposes
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Evaluating Impacts of Biochar and Inorganic Fertilizer Applications on Soil Quality and Maize Yield Using Principal Component Analysis
(2024-08-01)
Faloye, Oluwaseun Temitope
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Ajayi, Ayodele Ebenezer
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Kamchoom, Viroon
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Akintola, Olayiwola Akin
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Oguntunde, Philip Gbenro
A 2-year field experiment was conducted to test the effects of individual and co-application of biochar and inorganic fertilizer on soil quality using the principal component analysis (PCA) technique. The dry season field experiments were performed with biochar applied at 0 and 20 t ha−1, and fertilizer at 300 and 0 kg ha−1 (control). The factorial combinations of the above-mentioned treatments were subjected to irrigation at 60, 80, and 100% of irrigation amounts (IAs). Soil hydro-physical and chemical properties and grain yield were determined at harvest. Results from the PCA indicated that the soil total nitrogen (N) and moisture content (MC) were the soil properties mostly affecting the grain yield. The amendments’ effects on the soil physico-chemical properties and maize yield were in the order control < biochar < fertilizer < biochar + fertilizer. The derived comprehensive soil quality index (CSQI) from the PCA showed that the soil quality increased by 76, 100, and 200% in treatments individually applied with biochar, inorganic fertilizer, and the co-applications. This study therefore showed that the PCA revealed the actual dynamics in soil properties, in terms of the SQI upon the soil amendment addition, as well as their relationship with maize yield under different weather conditions.

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