Internet of Things (IoT) Integrated Solutions for environmentally friendly intelligent Farming: A Systematic Review

Chauhan, Ankit; Tripathy, Padmaja

doi:10.1109/icacite57410.2023.10182525

Cited by 3 publications

(1 citation statement)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, despite the increasing use of ICTs, Tanzania's agricultural sector has not benefited much, which Mtega & Msungu [32] attributed to farmers' limited access to the right ICT tools. Chauhan & Tripathy [47] stated that the Internet of Things (IoT) is an important way to achieve higher productivity. However, it has an important limitation, which is the security and privacy problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating the Interplay of ICT and Agricultural Inputs on Sustainable Agricultural Production: An ARDL Approach

Hasan,

Mimi,

Voumik

et al. 2023

J. Hum. Earth Future

View full text Add to dashboard Cite

Sustainable agriculture is an important target of the Sustainable Development Goals (SDGs). Information and communication technologies (ICT) could be an important instrument to assist developing countries in achieving this goal. This study investigates the short- and long-term effects of ICT-related factors (telephone, Internet, mobile phone use) alongside traditional inputs (agricultural land, labor, fertilizer) on sustainable agricultural production in Bangladesh using an autoregressive distributed lag (ARDL) model from 2000 to 2020. In the short term, telephone use and labor involved in agriculture have a positive impact on agricultural production. Conversely, the influence of internet use, mobile phone use, and agricultural land on agricultural production is negative in the short run. In the long run, these relationships have undergone substantial changes. The positive effects of telephone usage and agricultural labor demonstrate a decreasing trend, but the impacts of Internet usage, mobile phone usage, and total agricultural land display a progressively favorable pattern in the long run. Notably, the use of fertilizers has been found to have a positive impact on long-term agricultural production. This study offers valuable insights into the evolving role of ICT in agricultural sustainability, emphasizing the need for context-specific policy interventions that consider both short- and long-term benefits. Doi: 10.28991/HEF-2023-04-04-01 Full Text: PDF

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Investigating the Interplay of ICT and Agricultural Inputs on Sustainable Agricultural Production: An ARDL Approach

Hasan,

Mimi,

Voumik

et al. 2023

J. Hum. Earth Future

View full text Add to dashboard Cite

show abstract

Internet of Things (IoT)-Enabled Machine Learning Models for Efficient Monitoring of Smart Agriculture

Aldossary,

Alharbi,

Anwar Ul Hassan

2024

IEEE Access

View full text Add to dashboard Cite

Advancements in the agricultural sector are essential because the need for food is rising as the population of the world is expanding day by day. Traditional agricultural practices are not able to fulfill these needs. Furthermore, these practices are manual and are not optimized resulting in the wastage of resources, which is not suitable for resource-constrained agricultural environments. Besides this, the Internet of Things (IoT) network is playing an important role in the modern farming system. In this paper, we introduce an innovative IoT-enabled hybrid model for smart agriculture, integrating Machine Learning (ML) and Artificial Intelligence (AI) algorithms to provide a cost-effective and reliable decision-making system. Furthermore, we introduce a robust anomaly detection mechanism while applying the capabilities of Multilayer Perceptron (MLP), Naïve Bayes, and Support Vector Machine (SVM) on the dry beans' dataset. Hybrid models, combining neural networks with Random Forest and SVM, were also explored for anomaly detection in the dataset. Furthermore, deep learning models known as MobileNetV2, VGG16, and InceptionV3 are used for the classification of soil type datasets. The hybrid deep learning models were also developed, incorporating InceptionV3 with Long Short-Term Memory (LSTM) and VGG16 with fully connected dense layers. Two types of data sets are used in this study, which are the dry beans dataset (2021) and soil type Dataset (2024). Both datasets contain images. The ML techniques are applied to these datasets for anomaly detection. The simulations results show that the classification performance of the MobileNetV2 model, it has an accuracy and recall of 0.97. It shows that the model can correctly identify the soil type around 97%. On the other hand, the hybrid model combining random forest and neural network achieved an accuracy of 92%, further validating the effectiveness of our approach. Furthermore, the SVM model achieves an impressive overall accuracy of 0.93. Additionally, this accuracy is further enhanced with the integration of SVM and neural networks. Similarly, the hybrid model combining inception V3 with the LSTM layer exhibits a notable accuracy of 0.91, highlighting its efficiency in accurately classifying various instances. Lastly, the hybrid model employing random forest and neural network architecture achieves a commendable accuracy of 92%.

show abstract

Apis Insight for Phenotype Classification and Hive Health Forecasting using IoT and Deep Learning

Singh,

Arulmurugan

2024

2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS)

View full text Add to dashboard Cite

Internet of Things (IoT) Integrated Solutions for environmentally friendly intelligent Farming: A Systematic Review

Cited by 3 publications

References 51 publications

Investigating the Interplay of ICT and Agricultural Inputs on Sustainable Agricultural Production: An ARDL Approach

Investigating the Interplay of ICT and Agricultural Inputs on Sustainable Agricultural Production: An ARDL Approach

Internet of Things (IoT)-Enabled Machine Learning Models for Efficient Monitoring of Smart Agriculture

Apis Insight for Phenotype Classification and Hive Health Forecasting using IoT and Deep Learning

Contact Info

Product

Resources

About