Smart Agriculture Based on IoT and Machine Learning

Abraham, Geo; Raksha, R; Nithya, M.

doi:10.1109/iccmc51019.2021.9418392

Cited by 29 publications

(3 citation statements)

References 20 publications

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“…Various animal categorization prototype and an app-based agriculture tracking system were the two goals that were effectively attained by this work. (3) In order to produce an effective outcome, this article (4) proposes a vital survey of several techniques including crop choice, planting, invasive identification, and tracking the system. The study combined picture handling, ML, IOT, & use of AI to focus on the complete system.…”

Section: Discussionmentioning

confidence: 99%

Disease Detection using Region-Based Convolutional Neural Network and ResNet

Sushma Sri,

Hima Sailu,

Pradeepthi

et al. 2023

Data and Metadata

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In recent times, various techniques have been employed in agriculture to address different aspects. These techniques encompass strategies to enhance crop yield, identify hidden pests, and implement effective pest reduction methods, among others. Presented in this study a novel strategy which focuses on identification of plant leaf infections in agricultural fields using drones. By employing cameras on drones with high resolution, we take precise pictures of plant leaves, ensuring comprehensive coverage of the entire area. These images serve as datasets for Deep Learning algorithms, including Convolutional Neural Networks(CNN), Resnet, ReLu enabling the early detection of infections. The deep learning models leverage the captured images to identify and classify infections at their initial stages. The usage of R-CNN and ResNet technology in agriculture field has brought the tremendous change when we detect the disease in earlier stage of crop. Thus the farmer can take the pest preventive measures in the beginning stage to avoid crop failure.

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Section: Discussionmentioning

confidence: 99%

Disease Detection using Region-Based Convolutional Neural Network and ResNet

Sushma Sri,

Hima Sailu,

Pradeepthi

et al. 2023

Data and Metadata

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“…Accordingly, the TinyML sensor can't change itself to the specific setting in which it will be utilized. As per [5] there are two objectives covered. Initial, a versatile application based arrangement that shows the latest sensor readings and successfully empowers field organization from a good ways is shown.…”

Section: IImentioning

confidence: 99%

Soil Quality Monitoring and Crop Prediction Using IOT and Machine Learning

2023

IJRASET

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People are pressured for time in today's fast-paced environment, and technology assists them in a variety of ways. Agriculture is the primary industry in India. As a result, in the current environment, increasing output or production is critical. When it comes to determining certain chemical features of soil that have an immediate impact on plant growth, traditional farming faces various obstacles. Another critical factor to consider is the effect of timely water supply on plant output. The soil alkalinity and electrical conductivity are measured with PH electrodes. This study also looked into the real-time detection of soil variables and the location of chemical properties like pH, humidity, temperature, and soil moisture using a Raspberry Pi as a controller. Semi-supervised learning will be carried out in a methodical manner using the Q-learning algorithm. We used a range of samples in the experiment to show how to apply the indicated performance.

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“…The most common attack in smart farming is the distributed denial of service (DDoS) attack, which can generate fake traffic on the network. This attack intensifies by compromising multiple devices in the network to generate fake traffic to overwhelm the network [19,20]. Therefore, this paper focuses on developing an effective intrusion detection system (IDS) that can accurately detect and classify the DDoS attack on the network.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection in Internet of Things Based Smart Farming Using Hybrid Deep Learning Framework

Kethineni

Pradeepini

2023

Preprint

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Smart agriculture is a popular domain due to its intensified growth in recent times. This domain aggregates the advantages of several computing technologies, where the IoT is the most popular and beneficial. In this work, a novel and effective deep learning based framework is developed to detect intrusions in smart farming systems. The architecture is three-tier, with the first tier being the sensor layer, which involves the placement of sensors in agricultural areas. The second tier is the Fog Computing Layer (FCL), which consists of Fog nodes, and the proposed IDS is implemented in each Fog node. The gathered information is transferred to this fog layer for further analysis of data. The third tier is the cloud computing layer, which provides data storage and end-to-end services. The proposed model includes a fused CNN model with the bidirectional gated recurrent unit (Bi-GRU) model to detect and classify intruders. An attention mechanism is included within the BiGRU model to find the key features responsible for identifying the DDoS attack. In addition, the accuracy of the classification model is improved by using a nature-inspired meta-heuristic optimization algorithm called the Wild Horse Optimization (WHO) algorithm. The last layer is the cloud layer, which collects data from fog nodes and offers storage services. The proposed system will be implemented in the Python platform, using ToN-IoT and APA-DDoS attack datasets for assessment. The proposed system outperforms the existing methods in accuracy (99.35%), detection rate (98.99%), precision (99.9%) and F-Score (99.08%) for the APA DDoS attack dataset and the achieved accuracy of the ToN-IoT dataset (99.71%), detection rate (99.02%), precision (99.89%) and F-score (99.05%).

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Smart Agriculture Based on IoT and Machine Learning

Cited by 29 publications

References 20 publications

Disease Detection using Region-Based Convolutional Neural Network and ResNet

Disease Detection using Region-Based Convolutional Neural Network and ResNet

Soil Quality Monitoring and Crop Prediction Using IOT and Machine Learning

Intrusion Detection in Internet of Things Based Smart Farming Using Hybrid Deep Learning Framework

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