Hispa Rice Disease Classification using Convolutional Neural Network

Sharma, Rishabh; Kukreja, Vinay; Kadyan, Virender

doi:10.1109/icspc51351.2021.9451800

Cited by 63 publications

(6 citation statements)

References 13 publications

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“…ℎ = 𝛼𝛼 * 𝑟𝑟𝑎𝑎𝑎𝑎𝑎𝑎(−1,1) (7) 𝑋𝑋 𝑛𝑛𝑛𝑛𝑛𝑛 = ℎ * �𝑋𝑋 𝑗𝑗𝑗𝑗 − 𝑋𝑋 𝑀𝑀 − 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝑚𝑚𝐺𝐺 � + 𝑋𝑋 𝑗𝑗𝑗𝑗 (8) In Eqs. (7) and (8), 𝛼𝛼 refers to a random integer within [0,1]. 𝑋𝑋 𝑀𝑀 − 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝑚𝑚𝐺𝐺 shows the randomly generated solution amongst the available solutions in the global memory, 𝑋𝑋 𝑗𝑗𝑗𝑗 denotes the selected solution in the worst section for making some modifications, and also ℎ indicates the decimal number.…”

Section: Hyperparameter Tuning Using Ffamentioning

confidence: 99%

“…These inefficient standard manual analysis techniques prevent adequate rice production from attaining social requirements, requiring the emergence of new identification methods [7]. Developments in computer technology are provided to increase conventional image-processing approaches, comprising detection, feature extraction, reduced dimensionality, and image pre-processing [8]. These techniques are employed for agricultural images for different resolutions, for instance, classification of crop pest and disease sites and varieties.…”

Section: Introductionmentioning

confidence: 99%

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Leveraging Deep Learning and Farmland Fertility Algorithm for Automated Rice Pest Detection and Classification Model

2024

KSII TIIS

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Rice pest identification is essential in modern agriculture for the health of rice crops. As global rice consumption rises, yields and quality must be maintained. Various methodologies were employed to identify pests, encompassing sensor-based technologies, deep learning, and remote sensing models. Visual inspection by professionals and farmers remains essential, but integrating technology such as satellites, IoT-based sensors, and drones enhances efficiency and accuracy. A computer vision system processes images to detect pests automatically. It gives real-time data for proactive and targeted pest management. With this motive in mind, this research provides a novel farmland fertility algorithm with a deep learning-based automated rice pest detection and classification (FFADL-ARPDC) technique. The FFADL-ARPDC approach classifies rice pests from rice plant images. Before processing, FFADL-ARPDC removes noise and enhances contrast using bilateral filtering (BF). Additionally, rice crop images are processed using the NASNetLarge deep learning architecture to extract image features. The FFA is used for hyperparameter tweaking to optimise the model performance of the NASNetLarge, which aids in enhancing classification performance. Using an Elman recurrent neural network (ERNN), the model accurately categorises 14 types of pests. The FFADL-ARPDC approach is thoroughly evaluated using a benchmark dataset available in the public repository. With an accuracy of 97.58, the FFADL-ARPDC model exceeds existing pest detection methods.

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Section: Hyperparameter Tuning Using Ffamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Leveraging Deep Learning and Farmland Fertility Algorithm for Automated Rice Pest Detection and Classification Model

2024

KSII TIIS

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“…Kalai Priya et al [14] performed the fine tuning of the hybrid algorithms to increase the accuracy of rice plant disease detection using deep learning networks. Rishabh Sharma et al [15] made an attempt at real-time identification of hispa rice plant disease and its severity from images taken from paddy fields in the state of Punjab, India. Shreya Ghosal et al [16] improved the testing accuracy of pre-trained VGG-16 model using ImageNet dataset with the transfer learning technique.…”

Section: Related Workmentioning

confidence: 99%

Generic Paddy Plant Disease Detector (GP2D2)

Prathima¹,

G²,

Sivachandar³

et al. 2023

Int. j. electr. comput. eng. syst. (Online)

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Rice is the primary food for almost half of the world’s population, especially for the people of Asian countries. There is a demand to improve the quality and increase the quantity of rice production to meet the food requirements of the increasing population. Bulk cultivation and quality production of crops need appropriate technology assistance over manual traditional methods. In this work, six popular Deep-CNN architectures, namely AlexNet, VGG-19, VGG-16, InceptionV3, MobileNet, and ResNet-50, are exploited to identify the diseases in paddy plants since they outperform most of the image classification applications. These CNN models are trained and tested with Plant Village dataset for classifying the paddy plant images into one of the four classes namely, Healthy, Brown Spot, Hispa, or Leaf Blast, based on the disease condition. The performance of the chosen architectures is compared with different hyper parameter settings. AlexNet outperformed other convolutional neural networks (CNNs) in this multiclass classification task, achieving an accuracy of 89.4% at the expense of a substantial number of network parameters, indicating the large model size of AlexNet. For developing mobile applications, the ResNet-50 architecture was adopted over other CNNs, since it has a comparatively smaller number of network parameters and a comparable accuracy of 86.1%. A fine-tuned ResNet-50 architecture supported mobile app, “Generic Paddy Plant Disease Detector (GP2D2)” has been developed for the identification of most commonly occurring diseases in paddy plants. This tool will be more helpful for the new generation of farmers in bulk cultivation and increasing the productivity of paddy. This work will give insight into the performance of CNN architectures in rice plant disease detection task and can be extended to other plants too.

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“…IoT has a significant impact and is expanding in this industry as intelligence spreads to everything, including homes, cars, cities, farms, and other structures. Concerning the present widely used applications and the most recent research trends in this field, these papers highlight and contribute to the numerous facets of the Internet of Things ( Sharma et al, 2021 ). In order to find the appropriate hyperparameter, this research optimized hyperparameters using Random Search, DL and Bayesian optimization.…”

Section: Related Workmentioning

confidence: 99%

Enhancing accuracy in brain stroke detection: Multi-layer perceptron with Adadelta, RMSProp and AdaMax optimizers

Uppal,

Gupta,

Juneja

et al. 2023

Front. Bioeng. Biotechnol.

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The human brain is an extremely intricate and fascinating organ that is made up of the cerebrum, cerebellum, and brainstem and is protected by the skull. Brain stroke is recognized as a potentially fatal condition brought on by an unfavorable obstruction in the arteries supplying the brain. The severity of brain stroke may be reduced or controlled with its early prognosis to lessen the mortality rate and lead to good health. This paper proposed a technique to predict brain strokes with high accuracy. The model was constructed using data related to brain strokes. The aim of this work is to use Multi Layer Perceptron (MLP) as a classification technique for stroke data and used multi-optimizers that include Adaptive moment estimation with Maximum (AdaMax), Root Mean Squared Propagation (RMSProp) and Adaptive learning rate method (Adadelta). The experiment shows RMSProp optimizer is best with a data training accuracy of 95.8% and a value for data testing accuracy of 94.9%. The novelty of work is to incorporate multiple optimizers alongside the MLP classifier which offers a comprehensive approach to stroke prediction, providing a more robust and accurate solution. The obtained results underscore the effectiveness of the proposed methodology in enhancing the accuracy of brain stroke detection, thereby paving the way for potential advancements in medical diagnosis and treatment.

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Hispa Rice Disease Classification using Convolutional Neural Network

Cited by 63 publications

References 13 publications

Leveraging Deep Learning and Farmland Fertility Algorithm for Automated Rice Pest Detection and Classification Model

Leveraging Deep Learning and Farmland Fertility Algorithm for Automated Rice Pest Detection and Classification Model

Generic Paddy Plant Disease Detector (GP2D2)

Enhancing accuracy in brain stroke detection: Multi-layer perceptron with Adadelta, RMSProp and AdaMax optimizers

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