Controlling Agronomic Variables of Saffron Crop Using IoT for Sustainable Agriculture

Kour, Kanwalpreet; Gupta, Deepali; Gupta, Kamali; Juneja, Sapna; Kaur, Manjit; Alharbi, Amal H.; Lee, Heung-No

doi:10.3390/su14095607

Cited by 23 publications

(7 citation statements)

References 64 publications

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“…These kinds of data are directly collected by the researcher. The data are original, raw data that need to be pre-processed before feeding into the model ( Kour et al., 2022 ). This produces reliable, qualitative data but is a more costly data collection method.…”

Section: Methodsmentioning

confidence: 99%

A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases

Lamba,

Kukreja,

Rashid

et al. 2023

Front. Plant Sci.

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IntroductionPaddy leaf diseases have a catastrophic influence on the quality and quantity of paddy grain production. The detection and identification of the intensity of various paddy infections are critical for high-quality crop production.MethodsIn this paper, infections in paddy leaves are considered for the identification of illness severity. The dataset contains both primary and secondary data. The four online repositories used for secondary data resources are Mendeley, GitHub, Kaggle and UCI. The size of the dataset is 4,068 images. The dataset is first pre-processed using ImageDataGenerator. Then, a generative adversarial network (GAN) is used to increase the dataset size exponentially. The disease severity calculation for the infected leaf is performed using a number of segmentation methods. To determine paddy infection, a deep learning-based hybrid approach is proposed that combines the capabilities of a convolutional neural network (CNN) and support vector machine (SVM). The severity levels are determined with the assistance of a domain expert. Four degrees of disease severity (mild, moderate, severe, and profound) are considered.ResultsThree infections are considered in the categorization of paddy leaf diseases: bacterial blight, blast, and leaf smut. The model predicted the paddy disease type and intensity with a 98.43% correctness rate. The loss rate is 41.25%.DiscussionThe findings show that the proposed method is reliable and effective for identifying the four levels of severity of bacterial blight, blast, and leaf smut infections in paddy crops. The proposed model performed better than the existing CNN and SVM classification models.

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Section: Methodsmentioning

confidence: 99%

A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases

Lamba,

Kukreja,

Rashid

et al. 2023

Front. Plant Sci.

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“…Different components of a typical hydraulic pump system are shown in Figure 2. Hydraulic pressure is converted into a sufficient mechanical force through hydraulic cylinders used to operate the press as per the required pressure [58]. A pressure relief valve, as shown in Figure 2, operates based on a pre-configured pressure value to return the hydraulic fluid into the hydraulic tank once the press operation has been performed.…”

Section: Methodsmentioning

confidence: 99%

Energy Saving Implementation in Hydraulic Press Using Industrial Internet of Things (IIoT)

Sumit

Gupta

Juneja³

et al. 2022

Electronics

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With the growing cost of electrical energy, the necessity of energy-saving implementation in industries based on energy audits has become a major focus area. Energy audit results indicate energy-saving potential in an application and require the physical presence of the auditor’s team for monitoring and analyzing the energy consumption data. The use of Industrial Internet of Things (IIoT) for remote data monitoring and analysis is growing and new industrial applications based on IIoT are being developed and used by various industrial sectors. Possibilities of a mixed method of physical and remote energy audit using IIoT in industrial applications and its advantages as proposed in this research work needs to be explored. Existing hydraulic press machines running with direct online starter (DOL) can be run with variable speed drive (VSD) for energy saving but this requires an extensive energy audit. Key electrical and operational parameters of the hydraulic pump motor were monitored and analyzed remotely using IIoT in this research work by operating the hydraulic press with DOL and VSD motor control methods one by one. The input power factor of the hydraulic pump motor showed an improvement from 0.79 in DOL control to 0.9 in VSD control at different motor loads. The hydraulic pump motor starting current showed a reduction of 84% with VSD control. The hydraulic pump motor’s continuous current was reduced by 40% and 65% during the loading and unloading cycle, respectively, with VSD control. Electrical consumption was reduced by 24% as a result of operating the hydraulic pump motor at 35 Hz with VSD control without impacting the performance of the hydraulic press. These results indicated more efficient control by changing to VSD control in comparison with DOL control. A combination of physical and remote energy audits as performed in this research work using the proposed IIoT framework can be utilized for implementing energy saving in hydraulic presses thus motivating industries to adopt available more energy-efficient technologies at a faster pace.

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“…The animal visual cortex served as a model for CNN computing. It decodes visual data and has a fine sensitivity to the input's smallest subregions [50]. The convolutional layer's main elements are its receptive field, stride, dilation, and padding [51].…”

Section: ) Convolution Layermentioning

confidence: 99%

Improved Multiclass Brain Tumor Detection using Convolutional Neural Networks and Magnetic Resonance Imaging

Mahjoubi¹,

Hamida²,

Gannour³

et al. 2023

IJACSA

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Recently, Deep learning algorithms, particularly Convolutional Neural Networks (CNNs), have been applied extensively for image recognition and classification tasks, with successful results in the field of medicine, such as in medical image analysis. Radiologists have a hard time categorizing this lethal illness since brain tumors include a variety of tumor cells. Lately, methods based on computer-aided diagnostics claimed to employ magnetic resonance imaging to help with the diagnosis of brain cancers (MRI). Convolutional Neural Networks (CNNs) are often used in medical image analysis, including the detection of brain cancers. This effort was motivated by the difficulty that physicians have in appropriately detecting brain tumors, particularly when they are in the early stages of brain bleeding. This proposed model categorized the brain image into four distinct classes: (Normal, Glioma, Meningioma, and Pituitary). The proposed CNN networks reach 95% of recall, 95.44% accuracy and 95.36% of F1-score.

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Controlling Agronomic Variables of Saffron Crop Using IoT for Sustainable Agriculture

Cited by 23 publications

References 64 publications

A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases

A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases

Energy Saving Implementation in Hydraulic Press Using Industrial Internet of Things (IIoT)

Improved Multiclass Brain Tumor Detection using Convolutional Neural Networks and Magnetic Resonance Imaging

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