Multi Crop Disease Detection

DOI

10.2991/978-94-6463-858-5_98How to use a DOI?

Keywords

crop disease detection; convolutional neural networks; cotton disease; rice disease; tomato disease

Abstract

Agriculture affects people’s lives and financial standing. It provides a significant portion of the GDP and has a large workforce. Crop infections are one of the main issues contributing to farmers’ current crop production losses, which can be attributed to a variety of factors. Naturally occurring diseases are a major cause of crop losses. Ineffective disease control causes an annual loss of agricultural produce, which, if left unchecked, will have detrimental consequences on productivity, quantity, and quality. Detecting leaf disease with automatic methods like image processing is very important and advantageous. In the existing system, the CNN model is built to classify the diseases of Paddy Crop with 72 percent accuracy. In this study, an application was created utilizing CNN, which detects the diseases and provides appropriate remedies. The dataset included six types of diseases. Three crops and Each crop have two types of diseases. Paddy crop, which is mostly produced in India; tomato crop; and cotton crop, which holds a unique position among all crops and is also referred to as “white gold”. These three crops have been taken into account for this study. 96 percent is the accuracy of proposed CNN model.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - M. S. B. Kasyapa
AU  - M. Meena
AU  - L. Bhanuchander
AU  - K. Sandeep
AU  - V. Sriram
PY  - 2025
DA  - 2025/11/04
TI  - Multi Crop Disease Detection
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 1178
EP  - 1188
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_98
DO  - 10.2991/978-94-6463-858-5_98
ID  - Kasyapa2025
ER  -