Multivariant Disease Detection from Different Plant Leaves and Classification

Year : 2026 | Volume : 15 | 02 | Page :
    By

    V NarasimhaReddy,

  • D. Srikar,

  1. PG Student, Embedded Systems BV Raju Institute of Engineering and Technology Sreeram Nagar Colony , Vanasthalipuram, Hyderabad, India
  2. Assistant Professor, Embedded Systems BV Raju Institute of Engineering and Technology Sreeram Nagar Colony , Vanasthalipuram, Hyderabad, India

Abstract

Agricultural growth is significant in Indian GDP which is based on yield of crops, quality of the plants and procedure of the plants taken. To maintain good quality of plant, the plant diseases should be identified and then given proper suggestions to farmers for specific fertilizers and pesticides to be used. The use of specific fertilizers or pesticides makes plant more health with good quality so that farmers can get more yield than traditional way of farming with same fertilizers of all plants and for all types of diseases. Manual test of diseases in plants is costlier as well as time taking process so to avoid that in this application, web application has been designed which taken input plant leaf and then predicts the plant is either healthy or having disease with disease specifications also includes pesticides or fertilizers. Convolutional Neural Networks (CNNs) are used for finding the type of disease present in plant and it has ability to find multivariate diseases. Proposed deep learning classifier predicts the healthy or disease plant and if there is disease plant then as per the disease there are few specifications given as requirement of water, requirement of soil PH and requirement of rainfall for specific plant with disease. Compared to state of art techniques of machine learning such as K-nearest neighbor and support vector machine, proposed CNN model outperforms the existing models. For easy access to this application in proposed method web application using Django Framework has been designed which make this application user friendly and easily accessible. This proposed system minimizers the cost and time at it reduces the requirement of experts needed in this field which helps in better yield as well as better production.

Keywords: Smart Agriculture, Detection of multivariate disease, fertilizers, rainfall, water requirement recommendations, Crop yield improvement

How to cite this article:
V NarasimhaReddy, D. Srikar. Multivariant Disease Detection from Different Plant Leaves and Classification. Research and Reviews : Journal of Crop science and Technology. 2026; 15(02):-.
How to cite this URL:
V NarasimhaReddy, D. Srikar. Multivariant Disease Detection from Different Plant Leaves and Classification. Research and Reviews : Journal of Crop science and Technology. 2026; 15(02):-. Available from: https://journals.stmjournals.com/rrjocst/article=2026/view=246572


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Ahead of Print Subscription Original Research
Volume 15
02
Received 11/05/2026
Accepted 16/05/2026
Published 12/06/2026
Publication Time 32 Days
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