Towards applicability of machine learning techniques in agriculture and energy sector

Arumugam, K.; Sanchez, Domenic T.; Mustafa, Malik; Phoemchalard, Chirasak; Phasinam, Khongdet; Okoronkwo, Ethelbert

doi:10.1016/j.matpr.2021.11.394

Cited by 21 publications

(16 citation statements)

References 9 publications

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“…A smart agriculture system using DL-based computer vision is promising due to the massive growth of agriculture data commonly collected from IoT sensors. Other researchers also studied and applied traditional machine learning (also in combination with DL) techniques, such as fuzzy logic, SVM, supervised learning, decision tree, linear regression, and KNN [ 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ]. The authors of [ 52 ] proposed a small-scale agriculture machine to irrigate and weed automatically in the cultivated area.…”

Section: Resultsmentioning

confidence: 99%

“…The results showed that the machine achieved an accuracy of at least 90% in weeding and watering the deep soil. In recent works from [ 53 , 54 ], the combination of ANN, Gaussian curve fitting, and CNN was proposed in crop farming specifically for fruit-grading systems. The SVM classifier is mainly used for various classifications in smart farming.…”

Section: Resultsmentioning

confidence: 99%

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Recent Advancements and Challenges of AIoT Application in Smart Agriculture: A Review

Adli

Remli

Wong

et al. 2023

Sensors

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As the most popular technologies of the 21st century, artificial intelligence (AI) and the internet of things (IoT) are the most effective paradigms that have played a vital role in transforming the agricultural industry during the pandemic. The convergence of AI and IoT has sparked a recent wave of interest in artificial intelligence of things (AIoT). An IoT system provides data flow to AI techniques for data integration and interpretation as well as for the performance of automatic image analysis and data prediction. The adoption of AIoT technology significantly transforms the traditional agriculture scenario by addressing numerous challenges, including pest management and post-harvest management issues. Although AIoT is an essential driving force for smart agriculture, there are still some barriers that must be overcome. In this paper, a systematic literature review of AIoT is presented to highlight the current progress, its applications, and its advantages. The AIoT concept, from smart devices in IoT systems to the adoption of AI techniques, is discussed. The increasing trend in article publication regarding to AIoT topics is presented based on a database search process. Lastly, the challenges to the adoption of AIoT technology in modern agriculture are also discussed.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Recent Advancements and Challenges of AIoT Application in Smart Agriculture: A Review

Adli

Remli

Wong

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Machine learning models are increasingly used to predict attributes because, as they are not subject to traditional statistical assumptions, they can incorporate variables and find nonlinear and complex patterns in data. Machine learning includes a variety of algorithms for learning predictive rules from historical data and building models that can predict future values [47][48][49][50][51].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Methods to Estimate Productivity of Harvesters: Mechanized Timber Harvesting in Brazil

Munis

Almeida

Camargo

et al. 2022

Forests

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The correct capture of forest operations information carried out in forest plantations can help in the management of mechanized harvesting timber. Proper management must be able to dimension resources and tools necessary for the fulfillment of operations and helping in strategic, tactical, and operational planning. In order to facilitate the decision making of forest managers, this work aimed to analyze the performance of machine learning algorithms in estimating the productivity of timber harvesters. As predictors of productivity, we used the availability of hours of machine use, individual mean volumes of trees, and terrain slopes. The dataset was composed of 144,973 records, carried out over a period of 28 months. We tested the predictive performance of 24 machine learning algorithms in default mode. In addition, we tested the performance of blending and stacking joint learning methods. We evaluated the model’s fit using the root mean squared error, mean absolute error, mean absolute percentage error, and determination coefficient. After cleaning the initial database, we used only 1.12% to build the model. Learning by blending ensemble stood out with a determination coefficient of 0.71 and a mean absolute percentage error of 15%. From the use of data from machine learning algorithms, it became possible to predict the productivity of timber harvesters. Testing a variety of machine learning algorithms with different dynamics contributed to the machine learning technique that helped us reach our goal: maximizing the model’s performance by conducting experimentation.

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“…As a result, the findings of this study, which aims to broaden comparative assessments of using Dealone and Mclean's model in developing nations, will assist other research studies exploring cross-cultural aspects and those in the meta-assessment field. This study also aims to determine the significance of technological attitudes and their relationships with VR-learning intent, usage, user satisfaction, and net benefits [10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Untitled

2022

GSC Adv. Eng. Technol.

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The goal of this study is to determine the importance of IS success and its relationships with the intent to use, utilization, user happiness, and net benefits of VR-learning at six Jordanian universities. This study focuses on the use of cultural and social elements that have been identified as important in improving students' learning processes, with the goal of persuading university administrators to pay attention to factors that influence the usage of VR-learning. At Jordan, however, there has been little success with VR-learning adoption in higher education institutions. In addition, only a few research have looked at the aspects that influence the success of VR-learning deployment.

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Towards applicability of machine learning techniques in agriculture and energy sector

Cited by 21 publications

References 9 publications

Recent Advancements and Challenges of AIoT Application in Smart Agriculture: A Review

Recent Advancements and Challenges of AIoT Application in Smart Agriculture: A Review

Machine Learning Methods to Estimate Productivity of Harvesters: Mechanized Timber Harvesting in Brazil

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