Feature Extraction and Disease Prediction from Paddy Crops Using Data Mining Techniques

Das, Sunanda; Sengupta, Shampa

doi:10.1007/978-981-15-2449-3_13

Cited by 11 publications

(4 citation statements)

References 14 publications

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“…An extended range of datasets is available. A vast variety of auxiliary datasets may coordinate with intrusion detection approaches such as ADFA-LD, NSL-KDD [ 54 ], BOT-IoT [ 55 ], etc. The proposed IDS is designed for RPL-based communication networks.…”

Section: Methodsmentioning

confidence: 99%

Fog-Assisted Deep-Learning-Empowered Intrusion Detection System for RPL-Based Resource-Constrained Smart Industries

Attique

Wang

2022

Sensors

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The Internet of Things (IoT) is a prominent and advanced network communication technology that has familiarized the world with smart industries. The conveniently acquirable nature of IoT makes it susceptible to a diversified range of potential security threats. The literature has brought forth a plethora of solutions for ensuring secure communications in IoT-based smart industries. However, resource-constrained sectors still demand significant attention. We have proposed a fog-assisted deep learning (DL)-empowered intrusion detection system (IDS) for resource-constrained smart industries. The proposed Cuda–deep neural network gated recurrent unit (Cu-DNNGRU) framework was trained on the N-BaIoT dataset and was evaluated on judicious performance metrics, including accuracy, precision, recall, and F1-score. Additionally, the Cu-DNNGRU was empirically investigated alongside state-of-the-art classifiers, including Cu-LSTMDNN, Cu-BLSTM, and Cu-GRU. An extensive performance comparison was also undertaken among the proposed IDS and some outstanding solutions from the literature. The simulation results showed ample strength with respect to the validation of the proposed framework. The proposed Cu-DNNGRU achieved 99.39% accuracy, 99.09% precision, 98.89% recall, and an F1-score of 99.21%. In the performance comparison, the values were substantially higher than those of the benchmarked schemes, as well as competitive security solutions from the literature.

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Section: Methodsmentioning

confidence: 99%

Fog-Assisted Deep-Learning-Empowered Intrusion Detection System for RPL-Based Resource-Constrained Smart Industries

Attique

Wang

2022

Sensors

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“…The study [45] outlines the creation of an automated device designed to analyze and provide advice to farmers using photos of infected paddy fields. The main goal is to streamline the detection and classification of rice diseases [46] by integrating vector supports and artificial neural networks.…”

Section: Techniquesmentioning

confidence: 99%

Deep Learning-Based Classification Methods for Detection of Diseases in Rice Leaves – A Review

Pai,

Basthikodi

et al. 2024

ijisae

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Cultivating rice is crucial in India to meet demands of a growing population. In order to improve crop yield, it's essential to address factors like diseases caused by bacteria, fungi, and viruses. Detecting and managing these diseases is vital, and one effective approach is employing rice plant disease detection methods. Deep learning techniques, known for their ability to analyse data, are used for disease identification in plants. This work explores various deep learning approaches for detecting rice plant disease. Deep learning, particularly in computer vision, has shown significant progress in detecting plant diseases. The study compares the effectiveness deep learning mechanisms, demonstrating superior performance of deep learning models. Utilizing deep learning can help prevent major crop losses by detecting leaf diseases through image analysis.

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“…There are many works on tomato leaf detection, machine learning (Uma et al, 2016;Anusha and Geetha, 2022;Harakannanavara et al, 2022), deep learning (Haridasan et al, 2023;Sankareshwaran et al, 2023;Yakkundimath and Saunshi, 2023), optimization techniques, data mining (Das and Sengupta, 2020;Demilie, 2024), regression analysis, image analysis (Ganatra and Patel, 2020;Ngugi et al, 2021), and prediction techniques that are found in the literature. Mustafa et al (2023) proposed a fivelayer CNN model for detecting plant diseases using leaf images.…”

Section: Literature Surveymentioning

confidence: 99%

Improved tomato leaf disease classification through adaptive ensemble models with exponential moving average fusion and enhanced weighted gradient optimization

V.,

Kumar,

Praveen

et al. 2024

Front. Plant Sci.

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Tomato is one of the most popular and most important food crops consumed globally. The quality and quantity of yield by tomato plants are affected by the impact made by various kinds of diseases. Therefore, it is essential to identify these diseases early so that it is possible to reduce the occurrences and effect of the diseases on tomato plants to improve the overall crop yield and to support the farmers. In the past, many research works have been carried out by applying the machine learning techniques to segment and classify the tomato leaf images. However, the existing machine learning-based classifiers are not able to detect the new types of diseases more accurately. On the other hand, deep learning-based classifiers with the support of swarm intelligence-based optimization techniques are able to enhance the classification accuracy, leading to the more effective and accurate detection of leaf diseases. This research paper proposes a new method for the accurate classification of tomato leaf diseases by harnessing the power of an ensemble model in a sample dataset of tomato plants, containing images pertaining to nine different types of leaf diseases. This research introduces an ensemble model with an exponential moving average function with temporal constraints and an enhanced weighted gradient optimizer that is integrated into fine-tuned Visual Geometry Group-16 (VGG-16) and Neural Architecture Search Network (NASNet) mobile training methods for providing improved learning and classification accuracy. The dataset used for the research consists of 10,000 tomato leaf images categorized into nine classes for training and validating the model and an additional 1,000 images reserved for testing the model. The results have been analyzed thoroughly and benchmarked with existing performance metrics, thus proving that the proposed approach gives better performance in terms of accuracy, loss, precision, recall, receiver operating characteristic curve, and F1-score with values of 98.7%, 4%, 97.9%, 98.6%, 99.97%, and 98.7%, respectively.

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Feature Extraction and Disease Prediction from Paddy Crops Using Data Mining Techniques

Cited by 11 publications

References 14 publications

Fog-Assisted Deep-Learning-Empowered Intrusion Detection System for RPL-Based Resource-Constrained Smart Industries

Fog-Assisted Deep-Learning-Empowered Intrusion Detection System for RPL-Based Resource-Constrained Smart Industries

Deep Learning-Based Classification Methods for Detection of Diseases in Rice Leaves – A Review

Improved tomato leaf disease classification through adaptive ensemble models with exponential moving average fusion and enhanced weighted gradient optimization

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