Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm

Ramesh, S.; Vydeki, D.

doi:10.1016/j.inpa.2019.09.002

Cited by 190 publications

(66 citation statements)

References 15 publications

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“…In literature, many methods and techniques have been applied to identify the paddy diseases based on its images. The high ratio of these techniques are using generalize image processing techniques [34], Self-Organizing Map (SOM) neural network [6], Gaussian Naïve Bayes [7], Support Vector Machine (SVM) [8], Radial Basis Function Network [9], Deep Neural Network (DNN) based on Jaya Optimization (JO) Algorithm [10], and the recent technique is Convolutional Neural Network (CNN) [11] [12].…”

Section: Related Workmentioning

confidence: 99%

“…For that, various types of image processing and machine learning techniques have been proposed by previous researchers to detect and classify paddy disease through affected paddy leaf [9][10] [11] [12]. Some of the classification techniques used are Self Organizing Map (SOM) neural network [6], Gaussian Naïve Bayes [7], Support Vector Machine [8], Radial Basis Function Network [9], Optimized Deep Neural Network with Jaya Optimization Algorithm [10] and the recent technique is Convolutional Neural Network (CNN) [11] [12]. Convolutional Neural Network is one of the deep learning techniques that have exposed great promise result in image classification.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Convolutional Neural Network for Paddy Leaf Disease and Pest Classification

Senan¹,

Aamir²,

Ibrahim³

et al. 2020

IJACSA

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Improving the quality and quantity of paddy production is very important since rice is the most consumed staple food for billion people around the world. Early detection of the paddy diseases and pests at different stages of growth is very crucial in paddy production. However, the current manual method in detecting and classifying the paddy diseases and pests requires a very knowledgeable farmer and time consuming. Thus, this study attempts to utilize an effective image processing and machine learning technique to detect and classify the paddy diseases and pests more accurately and less time processing. To accomplish this study, 3355 images comprises of 4 classes paddy images which are healthy, brown spot, leaf blast, and hispa was used. Then the proposed five layers of CNN technique is used to classify the images. The result shows that the proposed CNN technique is outperform and achieved the accuracy rate up to 93% as compared to other state-of-art comparative models.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Efficient Convolutional Neural Network for Paddy Leaf Disease and Pest Classification

Senan¹,

Aamir²,

Ibrahim³

et al. 2020

IJACSA

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“…A study conducted by S. Ramesh and D. Vydeki from VIT University, Chennai, India aims to recognition and classification paddy plant diseases to protect farmer's crops as well as paddy plant. In their research, they used Deep Neural Network with Jaya Optimizing Algorithm to identify diseases, which are Blast Disease, Bacterial Blight, Sheath Rot and Brown Spot [4].…”

Section: Detection and Recognition Of Paddy Plant Leaf Diseases Usingmentioning

confidence: 99%

Detection and Recognition of Paddy Plant Leaf Diseases using Machine Learning Technique

Parven*,

Rashiduzzaman,

Sultana

et al. 2020

IJITEE

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Abstract: Rice is one of the important food crops and the most staple food for half of the world population. Farmers are often faces several obstacles in paddy production because of various paddy leaf diseases. As a result, rice production is extensively reduced. For finding the paddy plant leaf diseases, there are many techniques are available in the computer vision-based area. Now, it is the main concern to fast and accurate recognition of paddy plant diseases in the initial stage. For this reason, we proposed a better approach for early paddy plant leaf disease detection by using simple image processing and machine learning techniques. There are four types of paddy leaf diseases are highlighted in this paper; which are Brown Spot, Sheath Blight, Blast Disease and Narrow Brown Spot. To do this, at first the required normal and diseased paddy plant leaf images are captured directly from different paddy fields. The unnecessary background of the leaves images are eliminated by using mask in the pre-processing section. Then output is fed into the segmentation part where K-means clustering is used to separate the normal portion and diseased portion of the leaf images. Finally, the mentioned diseases are classified using Support Vector Machine (SVM)algorithm. The accuracy of the system is 94%. This technique can be also applied anywhere in the agriculture industry for plant leaf diseases detection.

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“…Currently, several attempts have been made to use Deep Learning Neural Networks (DLNN) for the classification of herbs [86][87][88][89][90][91][92][93][94][95][96][97][98][99], and plants diseases [100][101][102][103][104][105][106][107]. The motivation is to attract attention to such methods after the state-of-the-art processing of natural images [84,85].…”

Section: Introductionmentioning

confidence: 99%

Efficient and Automated Herbs Classification Approach Based on Shape and Texture Features using Deep Learning

Muneer

Fati

2020

IEEE Access

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Recognizing the desired herb among thousands of herbs is an exhausting and time-consuming practice. Hence, herbs identification via a vision system is beneficial since the pharmacist and botanic need not to collect them through traditional ways. Thus, this paper proposed an efficient and automatic classification system to recognize Malaysian herbs that would be used in medical or cooking areas. As per the authors' knowledge, there is no evidence for similar studies on medical herbs in Malaysia. In the proposed system, we have investigated different classifiers to build an efficient classifier; then, the classifier was integrated with a mobile app to ease the real-time classification. The proposed system employed two classifiers, namely Support Vector Machine (SVM) and Deep Learning Neural Network (DLNN). The two models have been tested on our own dataset, which contains 1000 leaves. The experimental results showed that SVM achieved 74.63% recognition accuracy and DLNN achieved 93% recognition accuracy for both of the experimental model and the developed mobile app. Furthermore, the processing time was 4 seconds for SVM and 5 seconds for DLNN classifier, while the processing time using the mobile app was 2 seconds only.

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Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm

Cited by 190 publications

References 15 publications

An Efficient Convolutional Neural Network for Paddy Leaf Disease and Pest Classification

An Efficient Convolutional Neural Network for Paddy Leaf Disease and Pest Classification

Detection and Recognition of Paddy Plant Leaf Diseases using Machine Learning Technique

Efficient and Automated Herbs Classification Approach Based on Shape and Texture Features using Deep Learning

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