Exploring the Role of Machine Learning in Advancing Crop Disease Detection

Ashwini Deshmukh, Devesh Nawgaje, Komal Vyas

2025

Abstract

Crop infections are a significant issue in agriculture, impacting the quality and quantity of produce. Crop diseases can reduce crop output, resulting in lower yields and financial losses for farmers. Research on disease control has been conducted in various scientific and technological fields. The study examines machine learning methods for detecting plant diseases utilising multiple data sources, such as IOT and image technology. Effective disease control has been demonstrated by the tremendous potential presented by technological advancements in sensors, data storage, processing power, and artificial intelligence. In Maharashtra, soybean is cultivated on a large scale and is a highly popular crop. This paper focuses on the study of various soybean diseases prevalent in the region. Using data from various sensors and machine learning to develop models for detection, prediction, analysis, and assessment is becoming increasingly important, according to the research. The growing number and variety of research papers need a literature assessment to inform future advances and contributions. In this paper machine learning methods are used. This article discusses how soybean diseases can be detected using various Machine learning algorithms and can improve plant health status prediction from diverse data sources. The study ends with a discussion of some contemporary issues and research trends. 1 2 3

Paper Citation

in Harvard Style

Deshmukh A., Nawgaje D. and Vyas K. (2025). Exploring the Role of Machine Learning in Advancing Crop Disease Detection. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 360-366. DOI: 10.5220/0013592400004664

in Bibtex Style

@conference{incoft25,
author={Ashwini Deshmukh and Devesh Nawgaje and Komal Vyas},
title={Exploring the Role of Machine Learning in Advancing Crop Disease Detection},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={360-366},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013592400004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Exploring the Role of Machine Learning in Advancing Crop Disease Detection
SN - 978-989-758-763-4
AU - Deshmukh A.
AU - Nawgaje D.
AU - Vyas K.
PY - 2025
SP - 360
EP - 366
DO - 10.5220/0013592400004664
PB - SciTePress