E-ISSN 0976-2833 | ISSN 0975-3583
 

Research Article 


ML Based Disease Identification and Grain Classification of Rice

Gadu SrinivasaRao, Gadi Himaja , Veera Nitish Mattaparthi , A.Masimo Monish, Nithin Kamineni, Pachamatla Vamsi, V. Saieswar Reddy.

Abstract
In general agriculture is one of the main sources of income for the farmers and Indian economy greatly depends on agriculture growth and development for better production. Almost three fourths of the world require rice production for their survival and this is cultivated almost all over the world, mostly in Asian countries. However, the farmers have been facing with some continuous challenges for centuries, such as different diseases of rice. If those diseases are not identified in the early stages, there will be a huge loss for the farmers as well as human beings who wish to consume that product. If the plant disease is identified in the early stages, it will be very helpful for the agriculture specialist or farmers to boost up the crop by taking necessary preventive steps and try to increase the profit. Normally the plant experts or specialist try to find out the plant or leaf illnesses based on external symptoms examination, but sometimes this may not give accurate reports. As we all know that the structure of rice plant diseases and insects is very minute and hence it is complex task to predict the diseases and different species in the plants and try to take necessary steps by spreading the pesticides or insecticides for the plants.In order to overcome all these problems, we try to design an application which can able to identify the plant disease from the affected part of crop image and then find out remedies for that disease. At present, it is very interesting to design the deep intricate neural network (CNN) is the latest image recognition solution. Here we try to gather several infected rice plant images and apply ML Algorithm and CNN model to identify the disease name and also find out the necessary preventive measures for that plant. Nevertheless, manual detection of disease costs a large amount of time and labor, so it is inevitably prudent to have an automated system to detect disease. To solve the above problem, we are developing a Machine Learning model using a CNN algorithm to detect the rice crop disease using the image and provide a suitable remedy. By conducting various experiments on our proposed model, we achieved a classification accuracy of 97.17% and 99.45% when applied to the test dataset. These remedies give information on pesticide use to control the disease. As an extension for the current application we try to try to find out the characteristics of rice grain and try to classify the grain name based on shape, size and color. Finally we try to conclude that proposed dataset was trained with a range of different machine learning algorithms and achieved an accuracy of 91.30% on Decision Tree Classifier.

Key words: Machine Learning Algorithms, Deep Neural Networks, Decision Tree Classifier, Agriculture Growth, Image Recognition.


 
ARTICLE TOOLS
Abstract
PDF Fulltext
How to cite this articleHow to cite this article
Citation Tools
Related Records
 Articles by Gadu SrinivasaRao
Articles by Gadi Himaja
Articles by Veera Nitish Mattaparthi
Articles by A.Masimo Monish
Articles by Nithin Kamineni
Articles by Pachamatla Vamsi
Articles by V. Saieswar Reddy
on Google
on Google Scholar


How to Cite this Article
Pubmed Style

Gadu SrinivasaRao , Gadi Himaja , Veera Nitish Mattaparthi , A.Masimo Monish , Nithin Kamineni , Pachamatla Vamsi , V. Saieswar Reddy. ML Based Disease Identification and Grain Classification of Rice. J Cardiovasc. Dis. Res.. 2021; 12(4): 714-729. doi: 10.31838/jcdr.2021.12.04.81


Web Style

Gadu SrinivasaRao , Gadi Himaja , Veera Nitish Mattaparthi , A.Masimo Monish , Nithin Kamineni , Pachamatla Vamsi , V. Saieswar Reddy. ML Based Disease Identification and Grain Classification of Rice. http://www.jcdronline.org/?mno=100275 [Access: July 26, 2021]. doi: 10.31838/jcdr.2021.12.04.81


AMA (American Medical Association) Style

Gadu SrinivasaRao , Gadi Himaja , Veera Nitish Mattaparthi , A.Masimo Monish , Nithin Kamineni , Pachamatla Vamsi , V. Saieswar Reddy. ML Based Disease Identification and Grain Classification of Rice. J Cardiovasc. Dis. Res.. 2021; 12(4): 714-729. doi: 10.31838/jcdr.2021.12.04.81



Vancouver/ICMJE Style

Gadu SrinivasaRao , Gadi Himaja , Veera Nitish Mattaparthi , A.Masimo Monish , Nithin Kamineni , Pachamatla Vamsi , V. Saieswar Reddy. ML Based Disease Identification and Grain Classification of Rice. J Cardiovasc. Dis. Res.. (2021), [cited July 26, 2021]; 12(4): 714-729. doi: 10.31838/jcdr.2021.12.04.81



Harvard Style

Gadu SrinivasaRao , Gadi Himaja , Veera Nitish Mattaparthi , A.Masimo Monish , Nithin Kamineni , Pachamatla Vamsi , V. Saieswar Reddy (2021) ML Based Disease Identification and Grain Classification of Rice. J Cardiovasc. Dis. Res., 12 (4), 714-729. doi: 10.31838/jcdr.2021.12.04.81



Turabian Style

Gadu SrinivasaRao , Gadi Himaja , Veera Nitish Mattaparthi , A.Masimo Monish , Nithin Kamineni , Pachamatla Vamsi , V. Saieswar Reddy. 2021. ML Based Disease Identification and Grain Classification of Rice. Journal of Cardiovascular Disease Research, 12 (4), 714-729. doi: 10.31838/jcdr.2021.12.04.81



Chicago Style

Gadu SrinivasaRao , Gadi Himaja , Veera Nitish Mattaparthi , A.Masimo Monish , Nithin Kamineni , Pachamatla Vamsi , V. Saieswar Reddy. "ML Based Disease Identification and Grain Classification of Rice." Journal of Cardiovascular Disease Research 12 (2021), 714-729. doi: 10.31838/jcdr.2021.12.04.81



MLA (The Modern Language Association) Style

Gadu SrinivasaRao , Gadi Himaja , Veera Nitish Mattaparthi , A.Masimo Monish , Nithin Kamineni , Pachamatla Vamsi , V. Saieswar Reddy. "ML Based Disease Identification and Grain Classification of Rice." Journal of Cardiovascular Disease Research 12.4 (2021), 714-729. Print. doi: 10.31838/jcdr.2021.12.04.81



APA (American Psychological Association) Style

Gadu SrinivasaRao , Gadi Himaja , Veera Nitish Mattaparthi , A.Masimo Monish , Nithin Kamineni , Pachamatla Vamsi , V. Saieswar Reddy (2021) ML Based Disease Identification and Grain Classification of Rice. Journal of Cardiovascular Disease Research, 12 (4), 714-729. doi: 10.31838/jcdr.2021.12.04.81





Most Viewed Articles
  • Massive pericardial effusion as the only manifestation of primary hypothyroidism
    Radheshyam Purkait , Anand Prasad , Ramchandra Bhadra , Arindam Basu
    J Cardiovasc. Dis. Res.. 2013; 4(4): 248-250
    » Abstract » doi: 10.1016/j.jcdr.2014.01.001

  • Impact of light exercises in selective cognitive response andhandballshooting accuracy performance in Mesopotamia handball players
    Ahuda Naji Zaidan, Qusay Mohammed Hamdan, Mohammed Kadhim Saleh, Samer Saadoun Abd El , Rida
    J Cardiovasc. Dis. Res.. 2021; 12(2): 141-145
    » Abstract » doi: 10.31838/jcdr.2021.12.02.18

  • Reduced nitrate level in individuals with hypertension and diabetes
    Shiekh Gazalla Ayub, Taha Ayub, Saquib Naveed Khan, Rubiya Dar, Khurshid Iqbal Andrabi
    J Cardiovasc. Dis. Res.. 2011; 2(3): 172-176
    » Abstract » doi: 10.4103/0975-3583.85264

  • Factor analysis of risk variables associated with metabolic syndrome in adult Asian Indians
    Mithun Das, Susil Pal, Arnab Ghosh
    J Cardiovasc. Dis. Res.. 2010; 1(2): 86-91
    » Abstract » doi: 10.4103/0975-3583.64442

  • Typical coronary artery aneurysm exactly within drug-eluting stent implantation region in a patient with rheumatoid arthritis
    Ying Zheng, Jing-yuan Mao
    J Cardiovasc. Dis. Res.. 2012; 3(4): 329-331
    » Abstract » doi: 10.4103/0975-3583.102725

  • Most Downloaded
  • Assessment of the Knowledge and Attitude of Male Students towards Smoking Based on Health Belief Model
    Rafat Rezapour-Nasrabad, Fatemeh Izadi, Atousa Karimi, Fateme Shariati Far, Khatereh Rostami, Amin Kiani, Afsaneh Ghasemi
    J Cardiovasc. Dis. Res.. 2020; 11(4): 116-121
    » Abstract » doi: 10.31838/jcdr.2020.11.04.20

  • Diabetic Retinopathy, The Automated of Detection of Retinal Fundus Images with Probabilistic Neural Networks (PNN)
    Elvina Amanda, Marischa Elveny, Rahmad Syah
    J Cardiovasc. Dis. Res.. 2020; 11(4): 302-306
    » Abstract » doi: 10.31838/jcdr.2020.11.04.54

  • Investigation of the Relationship between Social Support and Adherence to Treatment among Elderly Individuals with Type II Diabetes Mellitus
    Afsaneh Ghasemi, Rafat Rezapour-Nasrabad, Leila Nikrouz, Fatemeh Izadi, Atousa Karimi, Fateme Shariati Far, Zahra Khiali
    J Cardiovasc. Dis. Res.. 2020; 11(4): 122-129
    » Abstract » doi: 10.31838/jcdr.2020.11.04.21

  • Cardio-Vascular Disease Classification Using Stacked Segmentation Model and Convolutional Neural Networks
    G. Charlyn Pushpa Latha, S. Sridhar, S. Prithi, T. Anitha
    J Cardiovasc. Dis. Res.. 2020; 11(4): 26-31
    » Abstract » doi: 10.31838/jcdr.2020.11.04.05

  • The Prediction of the Bisoprolol Effectiveness in Patients with Stable Coronary Artery Disease with Post-Infarction Cardiosclerosis
    Svetlana S. Bunova, Ol'ga V. Zamahina, Nikolaj A. Nikolaev, Nina I.Zhernakova, Andrey A.Grishchenko
    J Cardiovasc. Dis. Res.. 2020; 11(4): 105-109
    » Abstract » doi: 10.31838/jcdr.2020.11.04.18

  • Most Cited Articles