Deep Machine Learning Model for Detection and Predicting Rice and Cotton Diseases Using Environmental Data

DOI

10.2991/978-94-6463-858-5_253How to use a DOI?

Keywords

Rice disease detection; Cotton disease prevention; Machine warning; Convolutional neural networks (CNNs); Crop health monitoring; Early disease detection; Agricultural productivity; Precision agriculture

Abstract

Plant diseases pose a significant challenge to agricultural productivity, leading to considerable reductions in both the quality and quantity of crops. In rice, a vital staple for global food security, common disorders caused by mineral deficiencies and pest infestations typically emerge during critical growth stages such as tilling and panicle onset. Similarly, cotton, a key global crop, is vulnerable to various diseases that threaten crop quality and economic value. Early detection of these diseases is essential for efficient crop management and minimizing losses. Convolutional Neural Networks (CNNs) have shown great potential for automating disease detection through image analysis of plant leaves. However, CNN effectiveness depends on large, labeled datasets, which can be costly and time-consuming to acquire. This paper discusses the challenges and potential of using machine learning techniques, particularly CNNs, for early disease detection in rice and cotton crops. By improving diagnostic accuracy and reducing the time required for data collection, these technologies offer promising solutions to improve crop management and minimize the impact of plant diseases on agricultural productivity.

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  - Asha Vuyyurru
AU  - Bathini Tanvi  Sri
AU  - Gangalapudi Khyathi Priya
AU  - Thummaluru Siva Keerthi Reddy
AU  - Kukkapally Praharshitha
PY  - 2025
DA  - 2025/11/04
TI  - Deep Machine Learning Model for Detection and Predicting Rice and Cotton Diseases Using Environmental Data
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 3018
EP  - 3033
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_253
DO  - 10.2991/978-94-6463-858-5_253
ID  - Vuyyurru2025
ER  -