Rice Leaf Disease Classification Using Cnn

Tejaswini, Pallapothala; Singh, Priyanshi; Ramchandani, Monica; Rathore, Yogesh; Janghel, Rekh Ram

doi:10.1088/1755-1315/1032/1/012017

Cited by 23 publications

(8 citation statements)

References 15 publications

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“…Here the image is enhanced by resizing, improving color clarity, brightness, rotations, and image smoothening, unwanted image background is eliminated [28].…”

Section: Pre-processingmentioning

confidence: 99%

AI and Neural Network-Based Approach for Paddy Disease Identification and Classification

et al. 2024

Int. Res. J. multidiscip. Technovation

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The purpose of this work is to use the artificial intelligence features of the ResNet50 architecture to provide a novel method of paddy disease identification. Farmers face numerous problems in raising paddy as its yield is affected by various factors like changing biodiversity, environment, weather pests, and disease. Traditional methods combined with smart farming, innovation, tools, and technology are needed for the mass production of food Here we develop a model using a convolutional neural network, ResNet50 that identifies disease in paddy leaf. The proposed model paddy disease identification model will give more precise results. The paddy disease identification model may be transformed into TensorFlow Lite (TFLite), which can be used for Android phones and drone applications, among other things. The Paddy model in this article obtained a training accuracy of almost 99% and a test accuracy of 92.83% when it was trained on 13,876 well-defined datasets. The loss function of 0.0014 at 100 epochs demonstrated that the model was effectively trained using ResNet50.

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“…Here the image is enhanced by resizing, improving color clarity, brightness, rotations, and image smoothening, unwanted image background is eliminated [28].…”

Section: Pre-processingmentioning

confidence: 99%

AI and Neural Network-Based Approach for Paddy Disease Identification and Classification

et al. 2024

Int. Res. J. multidiscip. Technovation

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“…To have an insight into the working the models LIME is used. Tejaswini et al [13] implemented a classical CNN based approach for the classification of Rice leaf disease. The dataset contained a total of 3 unhealthy and 1 healthy classes of rice leaf images.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Deep Learning Models for Accurate Detection of Plant Diseases: A Comprehensive Survey

Bhilare,

Swain,

Patel

2023

EAI Endorsed Trans IoT

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Agriculture plays an important role towards the economic growth of any nation. It also has a significant effect on global GDP. The enhancement in agro production helps in controlling greatly the inflation. Today a large percentage of population from rural India is still dependent on agriculture. But every year there is a huge loss happen in agriculture due to different plant diseases. A farmer does not able to recognise any plant disease at its beginning stage due to insufficient knowledge. Sometimes they take help of agriculture officers in this process. However, if the infection level has grown by that point, it typically leads to a significant crop loss. Also the diagnosis made by the agriculture officer based on their past experience, is always not accurate. Computational vision-based solutions can be used to deal with this great disaster to a large extent. Computer vision mainly deals with different algorithms that enable a computer to identify a hidden pattern for recognition using image or video data. In this work a detailed investigation has been performed on the different computer vision based solutions proposed by different authors to detect various crop diseases.

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“…With the fast development of software and hardware technologies, the application of artificial intelligence (AI), image identification, big data, and deep learning (DL) have been more frequent in the domain of agriculture, especially in crop disease detection and identification. If diseases can affect rice plants, their morphological characteristics and physiological structures are modified leading to signs such as decay, deformation, and leaf discoloration [4]. This laborious method is implemented manually in terms of developing the recent pest condition and gives area for misdiagnosis and delays in diagnosing period [5].…”

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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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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Rice Leaf Disease Classification Using Cnn

Cited by 23 publications

References 15 publications

AI and Neural Network-Based Approach for Paddy Disease Identification and Classification

AI and Neural Network-Based Approach for Paddy Disease Identification and Classification

Comparative Analysis of Deep Learning Models for Accurate Detection of Plant Diseases: A Comprehensive Survey

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

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