Soil moisture content estimation using active infrared thermography technique: An exploratory laboratory study

Almutawa, Noura A.; Eid, Waleed

doi:10.1016/j.kjs.2022.11.001

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…The research presented in [14] describes the integration of infrared thermography (IRT) in the assessment of soil moisture content, demonstrating a non-destructive and non-contact method that holds promise for advanced plant monitoring systems. Through the application of an external halogen lamp light and capturing soil surface temperatures with an infrared camera, the study achieved a high correlation between thermal data and soil moisture content.…”

Section: Related Work and Research Gapsmentioning

confidence: 99%

Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems

Kempelis,

Polaka,

Romanovs

et al. 2024

Future Internet

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Urban agriculture presents unique challenges, particularly in the context of microclimate monitoring, which is increasingly important in food production. This paper explores the application of convolutional neural networks (CNNs) to forecast key sensor measurements from thermal images within this context. This research focuses on using thermal images to forecast sensor measurements of relative air humidity, soil moisture, and light intensity, which are integral to plant health and productivity in urban farming environments. The results indicate a higher accuracy in forecasting relative air humidity and soil moisture levels, with Mean Absolute Percentage Errors (MAPEs) within the range of 10–12%. These findings correlate with the strong dependency of these parameters on thermal patterns, which are effectively extracted by the CNNs. In contrast, the forecasting of light intensity proved to be more challenging, yielding lower accuracy. The reduced performance is likely due to the more complex and variable factors that affect light in urban environments. The insights gained from the higher predictive accuracy for relative air humidity and soil moisture may inform targeted interventions for urban farming practices, while the lower accuracy in light intensity forecasting highlights the need for further research into the integration of additional data sources or hybrid modeling approaches. The conclusion suggests that the integration of these technologies can significantly enhance the predictive maintenance of plant health, leading to more sustainable and efficient urban farming practices. However, the study also acknowledges the challenges in implementing these technologies in urban agricultural models.

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Section: Related Work and Research Gapsmentioning

confidence: 99%

Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems

Kempelis,

Polaka,

Romanovs

et al. 2024

Future Internet

View full text Add to dashboard Cite

show abstract

Prediction of moisture content of cement-stabilized earth blocks using soil characteristics, cement content, and ultrasonic pulse velocity

Sathiparan,

Tharuka,

Jeyananthan

2024

J. Eng. Appl. Sci.

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This article investigates the importance of moisture content in cement-stabilized earth blocks (CSEBs) and explores methods for their prediction using machine learning. A key aspect of the research is the development of accurate moisture content prediction models. The study compares the performance of various machine learning models, and XGBoost emerges as the most promising model, demonstrating superior accuracy in predicting moisture content based on factors like soil properties, cement content, and ultrasonic pulse velocity (UPV). The study employs SHAP (SHapley Additive exPlanations) to understand how these features influence the model’s predictions. UPV is the most significant factor affecting predicted moisture content, followed by cement content and soil properties like uniformity coefficient. Also, the study explores the possibility of using a reduced set of features for moisture content prediction. They demonstrate that a combination of UPV, cement content, and uniformity coefficient can achieve good accuracy, highlighting the potential for practical applications where obtaining all data points might be challenging.

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Soil moisture content estimation using active infrared thermography technique: An exploratory laboratory study

Cited by 2 publications

References 17 publications

Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems

Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems

Prediction of moisture content of cement-stabilized earth blocks using soil characteristics, cement content, and ultrasonic pulse velocity

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