Automated Detection of Sugarcane Leaf Diseases Using Image Processing and Deep Learning

DOI

10.2991/978-94-6239-789-7_2How to use a DOI?

Keywords

Sugarcane; Plant Disease Detection; Image Processing; CNN; Deep Learning

Abstract

Sugarcane has been significant to agricultural economy of the world particularly in India where it has been a cash crop. Although sugarcane is important, the production of this crop is under severe threat due to the occurrence of different leaf diseases that reduce the quality and output of crops. The traditional methods of detecting diseases are labor-intensive, time-consuming, and often involve human error since they are based on visual inspection. To overcome these shortcomings this paper introduces an automated Sugarcane disease prediction system using image processing and deep learning architecture in the form of a Convolutional Neural Network (CNN). The system is trained on a well-collected dataset of labeled sugarcane leaf images and equipped with a well-developed graphical user interface (GUI), so that farmers and agricultural workers could easily conduct the disease detection exercise. The suggested model correctly distinguishes up to five diseases that can affect the sugarcane plant, which are the Red Rot, Smut, Rust, and Yellow Leaf Disease with an accuracy of 94.7%. The experimental findings demonstrate that the model also performs superior to conventional machine learning methods and better in terms of precision, recall, and F1- score. The feature of the system to assist disease detection and treatment at their early stages justifies timely intervention and ensures that agricultural practices are sustainable since they reduce the loss of crops and maximize output.

Copyright

© 2026 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  - Deepak Kumar Pant
AU  - Sameer Dev Sharma
AU  - Parminder Singh
AU  - Bincy Pothen
PY  - 2026
DA  - 2026/10/08
TI  - Automated Detection of Sugarcane Leaf Diseases Using Image Processing and Deep Learning
BT  - Proceedings of theInternational Conference on Innovation and Practices in Artificial Intelligence (ICIPAI 2026)
PB  - Atlantis Press
SP  - 5
EP  - 17
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-789-7_2
DO  - 10.2991/978-94-6239-789-7_2
ID  - Pant2026
ER  -