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RICE PLANT DISEASE CLASSIFICATION USING TRANSFER LEARNING OF DEEP CONVOLUTION NEURAL NETWORK
2019
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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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. ...
Identification of rice diseases using deep convolutional neural networks. Neurocomputing, 267, pp. 378-384. Figure 2 . 2 AlexNet Architecture
. ...
doi:10.5194/isprs-archives-xlii-3-w6-631-2019
fatcat:cqdphkmt5nhozee5bimisrpyce
Rice Plant Disease Detection
2020
International Journal for Research in Applied Science and Engineering Technology
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In this study, we put forward a novel rice disease detection technique based on Convolutional Neural Networks (CNNs) techniques. ...
So we can make use of image processing for detection of leaf disease using PYTHON. This paper tells about leaf disease detection using deep CNN. ...
Paper [4], Rice Blast Disease Recognition Using a Deep Convolutional Neural Network. ...
doi:10.22214/ijraset.2020.6033
fatcat:p4toi4uxkfddzokga77kgmp3am
Paddy Doctor: A Visual Image Dataset for Paddy Disease Classification
[article]
2022
arXiv
pre-print
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We benchmarked the Paddy Doctor using a Convolutional Neural Network (CNN) and two transfer learning approaches, VGG16 and MobileNet. ...
In a country with vast agricultural regions and limited crop protection experts, manual identification of paddy diseases is challenging. ...
Convolutional neural networks are one of the widely used techniques. ...
arXiv:2205.11108v1
fatcat:3hfpggkf5jgptg4qyvrq6ah2ti
A Pre-Trained Deep Convolutional Neural Network for the Detection of Tungro in Rice Plants
2018
Zenodo
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This paper presents a computer vision application of transfer learning in the detection of 'Tungro' among rice plants, using pre-trained deep convolutional neural networks. ...
An AlexNet network, consisting of 5 convolution layers and 3 fully connected layers of neurons, was customized and fine-tuned to accommodate as inputs, images of rice plants representing two (2) classes ...
Jerry Mercado, the Municipal Agricultural Officer of Goa, Camarines Sur, and the Crop Protection Office of DA-ROV. ...
doi:10.5281/zenodo.5211074
fatcat:m6zzcsgcyzb4jel5w72v3fk2b4
Classification Of Rice Plant Diseases Using the Convolutional Neural Network Method
2021
Lontar Komputer
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Machine learning is one of the technologies used to identify types of rice diseases. The classification system of rice plant disease used the Convolutional Neural Network method. ...
Convolutional Neural Network (CNN) is a machine learning method used in object recognition. This method applies to the VGG19 architecture, which has features to improve results. ...
Types of rice diseases can be identified in several ways, one of which is leaf characteristics. The first research is Identification Using Convolutional Neural Network (CNN). ...
doi:10.24843/lkjiti.2021.v12.i02.p06
fatcat:addfzdku5zebdcldsm2yef3mvq
A Multiclass Deep Convolutional Neural Network Classifier for Detection of Common Rice Plant Anomalies
2018
International Journal of Advanced Computer Science and Applications
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This study examines the use of deep convolutional neural network in the classification of rice plants according to health status based on images of its leaves. ...
Six hundred (600) images of rice plants representing the classes were used in the training. ...
Aside from those mentioned above, while some researchers have also ventured into the possibility of using shallow, fullyconnected networks in identifying rice diseases/infestations, the use of deep convolutional ...
doi:10.14569/ijacsa.2018.090109
fatcat:vhxgyfouvvhhva73yigqwc2hmq
A Survey on Deep Learning in Crop Planting
2019
IOP Conference Series: Materials Science and Engineering
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Deep learning has been widely used in many fields such as medical field, industry, transportation system, agriculture is no exception. Crop planting is a vital part of agriculture. ...
In recent years, with the explosive growth of data, deep learning has become one of the hottest research areas in artificial intelligence. ...
[26] proposed deep convolutional neural networks (CNNs) to identify 10 common rice diseases with an accuracy of 95.48%. Crop yield is related to the food supply [27] . ...
doi:10.1088/1757-899x/490/6/062053
fatcat:qotif67wnjg2bpwo4vsucqn6oi
Identification of Paddy Leaf Diseases using Machine Learning Techniques
2022
International Journal of Computer Applications
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Using a dataset of 800 natural images of diseased and healthy rice plant leaves and stems captured from the rice experimental field, machine learning models and Convolutional Neural Networks (CNN) models ...
This study proposes a novel paddy leaves diseases identification method based on a deep CNN model. The proposed CNN model achieves the highest training accuracy of 80.25% with the training data set. ...
So that, SVM is used to compare feature extraction methods and improve the performance of the classification. [5] A novel rice diseases identification method based on deep convolutional neural networks ...
doi:10.5120/ijca2022921898
fatcat:yqzaalgc6rbdro3yacpeh2betq
Image based Plant leaf disease detection using Deep learning
2021
International Journal of Computer Communication and Informatics
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Hence it is important for early detection and identification of diseases in plants. ...
The proposed methodology consists of collection of Plant leaf dataset, Image preprocessing, Image Augmentation and Neural network training. The dataset is collected from ImageNet for training phase. ...
Acknowledgements The authors declare that they have no conflict of interest.
Conflict of interest The authors declare that they have no conflict of interest. About The License ...
doi:10.34256/ijcci2115
fatcat:2qvsptt57bhnrf3nehpdina5g4
Development of a Prototype Application for Rice Disease Detection Using Convolutional Neural Networks
2020
International Journal of Emerging Trends in Engineering Research
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The researchers also used the Rice Disease Image Dataset by Huy Minh Do available at https://www.kaggle.com/ to train state-of-the-art convolutional neural networks using transfer learning. ...
Moreover, we used image augmentation to increase the number of image samples and the accuracy of the neural networks as well. ...
The application helps in the identification of the rice leaf diseases using convolutional neural networks that match the input data and the data stored in the database of the application. ...
doi:10.30534/ijeter/2020/708102020
fatcat:le67qeqrhbbhrdbkfxicieacha
RDA- CNN: Enhanced Super Resolution Method for Rice Plant Disease Classification
2022
Computer systems science and engineering
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We propose a novel Reconstructed Disease Aware-Convolutional Neural Network (RDA-CNN), inspired by recent CNN architectures, that integrates image super resolution and classification into a single model ...
In the field of agriculture, the development of an early warning diagnostic system is essential for timely detection and accurate diagnosis of diseases in rice plants. ...
For image classification, the number of CNN frameworks have been proposed. Furthermore, the five popular Convolutional Neural Networks are used for the rice crop disease classification. ...
doi:10.32604/csse.2022.022206
fatcat:az5m23xt7bc3tm63r6ogq2jxdu
Autoencoders for semantic segmentation of rice fungal diseases
2021
Agronomy Research
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The authors consider a new approach based on the use of autoencoders - special neural network architectures. ...
In the article, the authors examine the possibility of automatic localization of rice fungal infections using modern methods of computer vision. ...
The approach we propose here develops a number of researches where deep convolutional neural networks are used to solve problems of classification of plant diseases. ...
doi:10.15159/ar.21.019
fatcat:toziw2cyarf5hkt5lpkv5ua2fi
Classification Techniques for Plant Disease Detection
2020
International journal of recent technology and engineering
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In this review paper, we focused mainly on the most utilized classification mechanisms in disease detection of plants such as Convolutional Neural Network, Support Vector Machine, KNearest Neighbor, and ...
Artificial Neural Network. ...
ACKNOWLEDGMENT The authors wish to thank the faculty of the University that has given the facilities to carry out the research work and publish this work. ...
doi:10.35940/ijrte.f9902.038620
fatcat:vx7df2ffqnhy5iruoo462updwa
Deep neural networks with transfer learning in millet crop images
2019
Computers in industry (Print)
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Convolutional neural network Feature extraction Classification Precision agriculture Mildew disease In this paper, we propose an approach using transfer learning with feature extraction to build an identification ...
system of mildew disease in pearl millet. ...
The main motivation of this work is to construct a deep convolutional neural network model to build an identification system for the disease mildew in crop millet. ...
doi:10.1016/j.compind.2019.02.003
fatcat:in5hx4v2jve77addmdkkz7mexm
Crop Diseases and Pest Detection using Deep Learning and Image Processing Techniques
2021
International Journal for Research in Applied Science and Engineering Technology
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Crop disease and pest detection can be done using deep learning and image recognition techniques on leaves and other areas of the crop. ...
We plan to model a crop disease and pest diagnostic system using image processing and deep learning techniques. ...
Convolutional Neural Network (CNN) A CNN, or convolutional neural network, is a deep learning neural network designed to process ordered arrays of data, such as portrayals. ...
doi:10.22214/ijraset.2021.34915
fatcat:wszfm7cxprd6peo762wioxnh2m
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