Journal of Network and Information Security

Open Access Open Access  Restricted Access Subscription Access

A Review on Crop Disease Detection using Deep Learning


Affiliations
1 Seshachala Degree & P.G. College, Puttur, Andhra Pradesh, India
 

   Subscribe/Renew Journal


Lately, extreme atmosphere changes and absence of invulnerability in yields has caused a significant increment in the development of harvest infections. This causes enormous scale devastation of harvests, diminishes development and inevitably prompts money related loss of ranchers. Because of quick development in an assortment of maladies and sufficient information of rancher, distinguishing proof and treatment of the sickness has turned into a noteworthy test. The leaves have surface and visual likenesses which characteristics for an ID of illness type. Henceforth, PC vision utilized with profound learning gives the best approach to tackle this issue. This paper proposes a profound learning-based model which is prepared utilizing open dataset containing pictures of solid and infected harvest leaves. The model serves its target by arranging pictures of leaves into infected classification dependent on the example of imperfection.

Keywords

Crop disease, Deep learning, Image classification, InceptionV3, MobileNet, Transfer learning.
Subscription Login to verify subscription
User
Notifications
Font Size


  • A. Kadir, “A model of plant identification system using GLCM, lacunarity and shen features,” Research Journal of Pharmaceutical, Biological and Chemical Sciences, vol. 5, no. 2, pp. 1-10, 2014.

  • M. R. Naik, and C. Sivappagari, “Plant leaf and disease detection by using HSV features and SVM,” IJESC, vol. 6, no. 12, 2016.

  • G. Olmschenk, H. Tang, and Z. Zhu, “Crowd counting with minimal data using generative adversarial networks for multiple target regression,” 2018 IEEE Winter Conf. Appl. Comput. Vision (WACV), Lake Tahoe, NV, USA, Mar. 12-15, 2018.

  • S. Jain, and J. Dhar, “Image based search engine using deep learning,” 2017 10th Int. Conf. Contemporary Comput. (IC3), Aug. 2017.

  • A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “Mo-bileNets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint arXiv:1704.04861v1, 2017.


Abstract Views: 303

PDF Views: 121




  • A Review on Crop Disease Detection using Deep Learning

Abstract Views: 303  |  PDF Views: 121

Authors

Dilli Charan Sai
Seshachala Degree & P.G. College, Puttur, Andhra Pradesh, India

Abstract


Lately, extreme atmosphere changes and absence of invulnerability in yields has caused a significant increment in the development of harvest infections. This causes enormous scale devastation of harvests, diminishes development and inevitably prompts money related loss of ranchers. Because of quick development in an assortment of maladies and sufficient information of rancher, distinguishing proof and treatment of the sickness has turned into a noteworthy test. The leaves have surface and visual likenesses which characteristics for an ID of illness type. Henceforth, PC vision utilized with profound learning gives the best approach to tackle this issue. This paper proposes a profound learning-based model which is prepared utilizing open dataset containing pictures of solid and infected harvest leaves. The model serves its target by arranging pictures of leaves into infected classification dependent on the example of imperfection.

Keywords


Crop disease, Deep learning, Image classification, InceptionV3, MobileNet, Transfer learning.

References