Deep learning for proximal soil sensor development towards smart irrigation

Kurtulmuş, Ezgi; Arslan, Bilge; Kurtulmuş, Ferhat

doi:10.1016/j.eswa.2022.116812

Cited by 18 publications

(8 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some works make use of CNNs to identify probable plant illnesses associated with irrigation systems [51]. Conversely, traditional CNN architectures (specifically, GoogleNet and ResNet) have demonstrated significant potential in identifying irrigation needs for agricultural fields with different soil texture classes and lighting in soil frames segmented in close-up photos [47].…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Machine Learning for Smart Irrigation in Agriculture: How Far along Are We?

Del-Coco,

Leo,

Carcagnì

2024

Information

View full text Add to dashboard Cite

The management of water resources is becoming increasingly important in several contexts, including agriculture. Recently, innovative agricultural practices, advanced sensors, and Internet of Things (IoT) devices have made it possible to improve the efficiency of water use. However, it is the application of control strategies based on advanced machine learning techniques that enables the adoption of smart irrigation scheduling and the immediate economic, social, and environmental benefits. This challenging research area has attracted the attention of many researchers worldwide, who have proposed several technological and methodological solutions. Unfortunately, the results of these scientific efforts have not yet been categorized in a thematic survey, making it difficult to understand how far we are from optimal water management based on machine learning. This paper fills this gap by focusing on smart irrigation systems with an emphasis on machine learning. More specifically, the generic structure of a smart agriculture system is presented, and existing machine learning strategies and available datasets are discussed. Furthermore, several open issues are identified, especially in the processing of long-term data, also due to the lack of corresponding annotated datasets. Finally, some interesting future research directions to be pursued in order to build scalable, domain-independent approaches are proposed.

show abstract

Section: Deep Learning Methodsmentioning

confidence: 99%

Machine Learning for Smart Irrigation in Agriculture: How Far along Are We?

Del-Coco,

Leo,

Carcagnì

2024

Information

View full text Add to dashboard Cite

show abstract

“…The system uses a combination of sensors, including electromagnetic induction, time-domain reflectometry, and capacitance sensors, to collect data on soil properties like texture, structure, and water content. The data is then fed into a deep learning model that predicts soil moisture levels with high accuracy [23].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Cyclone Intensity Prediction for Smart Cities Using a Deep-Learning Approach for Accurate Prediction

Jayaraman,

Venkatachalam,

Eid

et al. 2023

Atmosphere

View full text Add to dashboard Cite

Accurate cyclone intensity prediction is crucial for smart cities to effectively prepare and mitigate the potential devastation caused by these extreme weather events. Traditional meteorological models often face challenges in accurately forecasting cyclone intensity due to cyclonic systems’ complex and dynamic nature. Predicting the intensity of cyclones is a challenging task in meteorological research, as it requires expertise in extracting spatio-temporal features. To address this challenge, a new technique, called linear support vector regressive gradient descent Jaccardized deep multilayer perceptive classifier (LEGEMP), has been proposed to improve the accuracy of cyclone intensity prediction. This technique utilizes a dataset that contains various attributes. It employs the Herfindahl correlative linear support vector regression feature selection to identify the most important characteristics for enhancing cyclone intensity forecasting accuracy. The selected features are then used in conjunction with the Nesterov gradient descent jeopardized deep multilayer perceptive classifier to predict the intensity classes of cyclones, including depression, deep depression, cyclone, severe cyclone, very severe cyclone, and extremely severe cyclone. Experimental results have demonstrated that LEGEMP outperforms conventional methods in terms of cyclone intensity prediction accuracy, requiring minimum time, error rate, and memory consumption. By leveraging advanced techniques and feature selection, LEGEMP provides more reliable and precise predictions for cyclone intensity, enabling better preparedness and response strategies to mitigate the impact of these destructive storms. The LEGEMP technique offers an improved approach to cyclone intensity prediction, leveraging advanced classifiers and feature selection methods to enhance accuracy and reduce error rates. We demonstrate the effectiveness of our approach through rigorous evaluation and comparison with conventional prediction methods, showcasing significant improvements in prediction accuracy. Integrating our enhanced prediction model into smart city disaster management systems can substantially enhance preparedness and response strategies, ultimately contributing to the safety and resilience of communities in cyclone-prone regions.

show abstract

“…In the past years, significant work has been carried out regarding the advancements of the agricultural system. There was a proposal by [19] on smart irrigation system using deep learning models as part of the sensor networks for the determination of different soil irrigation requirements. Deep learning models was used to identify and collect samples in diverse soil texture-water under various illumination conditions.…”

Section: Irrigation Challengementioning

confidence: 99%

Contextual use of a Smart Farm Irrigation System using Internet of Things (IoT)

Abioye(Ogunbiyi,

Adegoke,

Abogunrin

et al. 2023

Preprint

View full text Add to dashboard Cite

A crucial component of agricultural output is water, and its production can be reduced by shortage, which negatively affects food security. In an effort to address this developing issue, irrigation was employed as form of water management. Various irrigation methods were employed but there were various limitation to those methods such as soil and water erosion due to inability of soil moisture determination. This work focusses on the creation of an irrigation system based on the Internet of Things. The implementation was done using Arduino microcontroller programmed in C programming Language. The application’s data on soil moisture level, valve condition, and remote management of the smart irrigation system were all displayed using an Arduino microcontroller. In Nigeria, the smart irrigation system was tested on three different soil types: clay, loam, and sandy. Data including the moisture reading and irrigation time were acquired after inserting the moisture sensor probe into the soil sample. The average time taken to irrigate clay, loamy and sandy soil at 2kg, 5kg, 8kg and 10kg were 10.93s, 15s, 19.75s, 23.25s; 9.5s, 12.4s, 17s, 21.5s; 8.25s, 11.3s, 14.7s and 20.2s, respectively. This system helps in reducing soil erosion, as it monitors and reads soils moisture in real-time.

show abstract

Deep learning for proximal soil sensor development towards smart irrigation

Cited by 18 publications

References 44 publications

Machine Learning for Smart Irrigation in Agriculture: How Far along Are We?

Machine Learning for Smart Irrigation in Agriculture: How Far along Are We?

Enhancing Cyclone Intensity Prediction for Smart Cities Using a Deep-Learning Approach for Accurate Prediction

Contextual use of a Smart Farm Irrigation System using Internet of Things (IoT)

Contact Info

Product

Resources

About