Applicability of machine learning techniques in predicting wheat yield based on remote sensing and climate data in Pakistan, South Asia

Arshad, Sana; Kazmi, Jamil Hasan; Javed, Muhammad Gohar; Mohammed, Safwan

doi:10.1016/j.eja.2023.126837

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2024

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Cited by 23 publications

References 123 publications

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Assessing Maize (Zea Mays) Biomass at Different Growth Stages: A UAV-Based Multispectral Study on Nitrogen Effects

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Calderon,

Broncano

et al. 2024

Lecture Notes in Geoinformation and Cartography

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Assessing Maize (Zea Mays) Biomass at Different Growth Stages: A UAV-Based Multispectral Study on Nitrogen Effects

Merchan,

Calderon,

Broncano

et al. 2024

Lecture Notes in Geoinformation and Cartography

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Geospatial modelling of ambient air pollutants and chronic obstructive pulmonary diseases at regional scale in Pakistan

Fatima,

Ahmad,

Butt

et al. 2024

Environ Monit Assess

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Assessment of Advanced Machine and Deep Learning Approaches for Predicting CO2 Emissions from Agricultural Lands: Insights Across Diverse Agroclimatic Zones

Harsányi,

Mirzaei,

Arshad

et al. 2024

Earth Syst Environ

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Prediction of carbon dioxide (CO2) emissions from agricultural soil is vital for efficient and strategic mitigating practices and achieving climate smart agriculture. This study aimed to evaluate the ability of two machine learning algorithms [gradient boosting regression (GBR), support vector regression (SVR)], and two deep learning algorithms [feedforward neural network (FNN) and convolutional neural network (CNN)] in predicting CO2 emissions from Maize fields in two agroclimatic regions i.e., continental (Debrecen-Hungary), and semi-arid (Karaj-Iran). This research developed three scenarios for predicting CO2. Each scenario is developed by a combination between input variables [i.e., soil temperature (Δ), soil moisture (θ), date of measurement (SD), soil management (SM)] [i.e., SC1: (SM + Δ + θ), SC2: (SM + Δ), SC3: (SM + θ)]. Results showed that the average CO2 emission from Debrecen was 138.78 ± 72.04 ppm (n = 36), while the average from Karaj was 478.98 ± 174.22 ppm (n = 36). Performance evaluation results of train set revealed that high prediction accuracy is achieved by GBR in SC1 with the highest R2 = 0.8778, and lowest root mean squared error (RMSE) = 72.05, followed by GBR in SC3. Overall, the performance MDLM is ranked as GBR > FNN > CNN > SVR. In testing phase, the highest prediction accuracy was achieved by FNN in SC1 with R2 = 0.918, and RMSE = 67.75, followed by FNN in SC3, and GBR in SC1 (R2 = 0.887, RMSE = 79.881). The performance of MDLM ranked as FNN > GRB > CNN > SVR. The findings of the research provide insights into agricultural management strategies, enabling stakeholders to work towards a more sustainable and climate-resilient future in agriculture.

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Applicability of machine learning techniques in predicting wheat yield based on remote sensing and climate data in Pakistan, South Asia

Cited by 23 publications

References 123 publications

Assessing Maize (Zea Mays) Biomass at Different Growth Stages: A UAV-Based Multispectral Study on Nitrogen Effects

Assessing Maize (Zea Mays) Biomass at Different Growth Stages: A UAV-Based Multispectral Study on Nitrogen Effects

Geospatial modelling of ambient air pollutants and chronic obstructive pulmonary diseases at regional scale in Pakistan

Assessment of Advanced Machine and Deep Learning Approaches for Predicting CO2 Emissions from Agricultural Lands: Insights Across Diverse Agroclimatic Zones

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