Fuzzy C-Means (FCM) Clustering with Probabilistic Neural Network (PNN) Model for Detection and Classification of Rice Plant Diseases in Internet of Things-Cloud Centric Precision Agriculture

2021 ◽  
Vol 18 (4) ◽  
pp. 1194-1200
Author(s):  
P. Sindhu ◽  
G. Indirani ◽  
P. Dinadayalan
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Internet Of Things ◽  
Precision Agriculture ◽  
Rice Plant ◽  
Probabilistic Neural Network ◽  
Plant Diseases ◽  
Brown Spot ◽  
Fuzzy C Means ◽  
Fcm Clustering

Presently, the field of Internet of Things (loT) has been employed in diverse applications like Smart Grid, Surveillance, Smart homes, and so on. Precision Agriculture is a concept of farm management which makes use of IoT and networking concepts to improve the crop health. Recognition of diseases from the plant images is an active research topic which makes use of machine learning (ML) approaches. This paper introduces an effective rice plant disease identification and classification model to identify the type of disease from infected rice plants. The proposed method aims to detect three rice plant diseases such as Bacterial leaf blight, Brown spot, and Leaf smut. The proposed method involves a set of different processes namely image acquisition, preprocessing, segmentation, feature extraction and classification. At the earlier stage, IoT devices will be used to capture the image and stores it with a cloud server, which executes the classification process. In the cloud, the rice plant images under preprocessing to improvise the quality of the image. Then, fuzzy c-means (FCM) clustering method is utilized for the segmentation of disease portion from a leaf image. Afterwards, feature extraction takes place under three kinds namely color, shape, and texture. Finally, probabilistic neural network (PNN) is applied for multi-class classification. A detailed experimental analysis ensured the effective classification performance of the proposed method under all the test images applied.

scholarly journals Penentuan Notasi Gamelan Rindik Menggunakan Metode Transformasi Wavelet

2018 ◽  
Vol 17 (3) ◽  
pp. 319
Author(s):  
I Gusti Made Meri Utama Yasa ◽  
Linawati Linawati ◽  
N Paramaita
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Wavelet Transform ◽  
Probabilistic Neural Network ◽  
Wavelet Function ◽  
Discrete Wavelet ◽  
Daubechies Wavelet ◽  
Data Process ◽  
Average Accuracy ◽  
Accuracy Level

Abstract—This paper present about recognition of gamelan rindik pattern using wavelet transform. Wavelet transform is used to find the special characteristic of gamelan rindik, which had previously been recorded and stored in computer with format *.wav. The data was subsequently used as training and tested data, Probabilistic Neural Network (PNN) was used to recognize gamelan rindik pattern using. The training and tasted data process used four different rindics, consisting 0f 240 gamelan rindik data. Discrete Wavelet Transform (DWT) was used as the method of feature extraction, with Symlet, Haar, and Daubechies Wavelet function. Those three functions of the wavelet  shows the average accuracy level for Symlet 94.58%, Haar 93.33%, and wavelet Daubechies 94.58%.

scholarly journals A Novel Convolutional Neural Network Based Model for Recognition and Classification of Apple Leaf Diseases

2020 ◽  
Vol 37 (6) ◽  
pp. 1093-1101
Author(s):  
Divakar Yadav ◽  
Akanksha ◽  
Arun Kumar Yadav
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Clustering Algorithm ◽  
Daily Intake ◽  
Plant Diseases ◽  
Data Set ◽  
Proposed Model ◽  
Fcm Clustering ◽  
Contrast Stretching

Plants have a great role to play in biodiversity sustenance. These natural products not only push their demand for agricultural productivity, but also for the manufacturing of medical products, cosmetics and many more. Apple is one of the fruits that is known for its excellent nutritional properties and is therefore recommended for daily intake. However, due to various diseases in apple plants, farmers have to suffer from a huge loss. This not only causes severe effects on fruit’s health, but also decreases its overall productivity, quantity, and quality. A novel convolutional neural network (CNN) based model for recognition and classification of apple leaf diseases is proposed in this paper. The proposed model applies contrast stretching based pre-processing technique and fuzzy c-means (FCM) clustering algorithm for the identification of plant diseases. These techniques help to improve the accuracy of CNN model even with lesser size of dataset. 400 image samples (200 healthy, 200 diseased) of apple leaves have been used to train and validate the performance of the proposed model. The proposed model achieved an accuracy of 98%. To achieve this accuracy, it uses lesser data-set size as compared to other existing models, without compromising with the performance, which become possible due to use of contrast stretching pre-processing combined with FCM clustering algorithm.

scholarly journals RICE PLANT DISEASE CLASSIFICATION USING TRANSFER LEARNING OF DEEP CONVOLUTION NEURAL NETWORK

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 631-635 ◽  
Author(s):  
V. K. Shrivastava ◽  
M. K. Pradhan ◽  
S. Minz ◽  
M. P. Thakur
Keyword(s):  
Neural Network ◽  
Rice Plant ◽  
Plant Disease ◽  
Crop Protection ◽  
Visual Observation ◽  
Plant Diseases ◽  
Disease Classification ◽  
Real Field ◽  
Support Vector ◽  
Identification System

<p><strong>Abstract.</strong> Early and accurate diagnosis of plant diseases is a vital step in the crop protection system. In traditional practices, identification is performed either by visual observation or by testing in laboratory. The visual observation requires expertise and it may vary subject to an individual which may lead to an error while the laboratory test is time consuming and may not be able to provide the results in time. To overcome these issues, image based machine learning approach to detect and classify plant diseases has been presented in literature. We have focused specifically on rice plant (<i>Oryza sativa</i>) disease in this paper. The images of the diseased symptoms in leaves and stems have been captured from the rice field. We have collected a total of 619 rice plant diseased images from the real field condition belong to four classes:(a) Rice Blast (RB), (b) Bacterial Leaf Blight (BLB), (c) Sheat Blight (SB) and (d) Healthy Leave (HL). We have used a pre-trained deep convolutional neural network(CNN) as a feature extractor and Support Vector Machine (SVM) as a classifier. We have obtained encouraging results. The early identification of rice diseases by this approach could be used as a preventive measure well as an early warning system. Further, it could be extended to develop a rice plant disease identification system on real agriculture field.</p>

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