AI‐enhanced soil management and smart farming

Chen, Qianyu; Li, Lanyu; Chong, Clive; Wang, Xiaonan

doi:10.1111/sum.12771

Cited by 25 publications

(12 citation statements)

References 46 publications

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“…Parameters such as soil, temperature, weight, precipitation, and mugginess are included in this information. In paper [30], the effect of these characteristics on trim development is depicted, and the discoveries are dissected. It has been found that air parameters, soil sort, and soil composition have an effect on edit generation rates.…”

Section: Literature Reviewsmentioning

confidence: 99%

Smart Farming System Based on Intelligent Internet of Things and Predictive Analytics

Ferehan

Haqiq

Ahmad

2022

Journal of Food Quality

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The Internet of Things (IoT) makes it conceivable to communicate among distinctive things. The use of IoT in the farming industry is critical for increasing utility. Smart agricultural practices may boost crop yield while also creating more output with the same amount of input. The majority of farmers, however, are still unaware of the most recent technologies and procedures. In this study, a revolutionary wireless mobile robot based on the Internet of Things (IoT) is created and installed to perform a variety of outdoor tasks. The benefits of this work include more accurate and efficient data, as well as a reduction in manpower. This research has applications in agriculture, arrival, and water division. Keen agrarian frameworks have been built up in different parts of the world utilising the Internet of Things (IoT) and remote sensor systems. One of the branches that springs to intellect in this respect is exactness cultivating. Numerous analysts have made checking and robotization frameworks for different cultivating capacities. Information collection and transmission between IoT gadgets set in ranches will be basic utilising WSN. The Kalman Filter (KF) is used with expectation investigation within the proposed method to get high-quality information free of commotion and exchange it with cluster-based WSNs. The quality of information utilised for examination is progressed as a result of this strategy, and information transport overhead within the wireless sensor network application is decreased. A decision tree is used for forecast analytics decision making for trim surrender expectation, trim classification, soil classification, climate expectation, and trim malady expectation. IoT components integrated with IoT cloud are coordinates in proposed framework to supply keen arrangement for edit development observing to clients.

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Section: Literature Reviewsmentioning

confidence: 99%

Smart Farming System Based on Intelligent Internet of Things and Predictive Analytics

Ferehan

Haqiq

Ahmad

2022

Journal of Food Quality

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“…For decision tree models, the tree’s depth is the most significant component because it defines how much the tree may subcategorize data according to the distinctive features of the dataset. However, for the boosted SVR and DTR models, the learning rates and the number of weak estimators are equally important in building efficient models . This work employed the test-set-cross-validation technique as the optimization strategy.…”

Section: Overview Of the Developed Algorithmsmentioning

confidence: 99%

Investigating the Soil Unconfined Compressive Strength Based on Laser-Induced Breakdown Spectroscopy Emission Intensities and Machine Learning Techniques

et al. 2023

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Laser-induced breakdown spectroscopy (LIBS) is a remarkable elemental identification and quantification technique used in multiple sectors, including science, engineering, and medicine. Machine learning techniques have recently sparked widespread interest in the development of calibration-free LIBS due to their ability to generate a defined pattern for complex systems. In geotechnical engineering, understanding soil mechanics in relation to the applications is of paramount importance. The knowledge of soil unconfined compressive strength (UCS) enables engineers to identify the behaviors of a particular soil and propose effective solutions to given geotechnical problems. However, the experimental techniques involved in the measurements of soil UCS are incredibly expensive and time-consuming. In this work, we develop a pioneering technique to estimate the soil unconfined compressive strength using artificial intelligent methods based on the spectra obtained from the LIBS system. Decision tree regression (DTR) and support vector regression learners were initially employed, and consequently, the adaptive boosting method was applied to improve the performance of the two single learners. The prediction power of the established models was determined using the standard performance evaluation metrics such as the root-mean-square error, CC between the predicted and actual soil UCS values, mean absolute error, and R 2 score. Our results revealed that the boosted DTR exhibited the highest coefficient of correlation of 99.52% and an R 2 value of 99.03% during the testing phase. To validate the models, the UCS values of soils stabilized with lime and cement were predicted with an optimum degree of accuracy, confirming the models’ suitability and generalization strength for soil UCS investigations.

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“…Increased encryption and security measures are required due to the increase in data [25]. In order to combine several digital platforms in agriculture, seamless system compatibility is essential [26]. Energy conservation, notably the use of renewable energy for sensors, must be prioritised [27].…”

Section: Challenges: Cost Data Privacy System Compatibility Energy Ma...mentioning

confidence: 99%

Advancements in Sensor-Based Technologies for Precision Agriculture: An Exploration of Interoperability, Analytics and Deployment Strategies

Shukla,

Maurya,

Sharma

2023

Ecsa 2023

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AI‐enhanced soil management and smart farming

Cited by 25 publications

References 46 publications

Smart Farming System Based on Intelligent Internet of Things and Predictive Analytics

Smart Farming System Based on Intelligent Internet of Things and Predictive Analytics

Investigating the Soil Unconfined Compressive Strength Based on Laser-Induced Breakdown Spectroscopy Emission Intensities and Machine Learning Techniques

Advancements in Sensor-Based Technologies for Precision Agriculture: An Exploration of Interoperability, Analytics and Deployment Strategies

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