Convolutional Neural Network Based Crop Monitoring

Sasikala C., Sainath Reddy R., Sree Ram Vijaya Vikram R., Pavan Kumar Ram Prasad D., Satish G.

2025

Abstract

To ensure sustainable crop production, farmers need to focus on efficient farming practices such as crop health, soil health, pest control and yield analysis. This process relies on reliable monitoring of disease and ripeness classification. This paper provides a machine learning system that classifies the crop images based on ripeness and detect diseases. Here for Feature extraction, we use Histogram of oriented gradients, for ripeness classification we use logistic regression and for disease classification we use Convolutional Neural Networks. To implement this system, we are using flask-based web interface where it ensures seamless deployment and we have visual tools like bar chart, pie chart to improve readability. More-over this system provides insights to nutrient management to optimize yields and reduce crop losses. A voice enabled feature enables that farmers can retrieve the information about yield analysis, nutrient management, remedial measures, and disease classification. This system improves efficient crop monitoring where it can minimize the errors from manual inspection, to maintain sustainable agricultural productivity and it supports decision making based on data to enhance crop health and yield impact.

Paper Citation

in Harvard Style

C. S., R. S., R. S., D. P. and G. S. (2025). Convolutional Neural Network Based Crop Monitoring. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 856-861. DOI: 10.5220/0013956600004919

in Bibtex Style

@conference{icrdicct`2525,
author={Sasikala C. and Sainath R. and Sree R. and Pavan D. and Satish G.},
title={Convolutional Neural Network Based Crop Monitoring},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={856-861},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013956600004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Convolutional Neural Network Based Crop Monitoring
SN - 978-989-758-777-1
AU - C. S.
AU - R. S.
AU - R. S.
AU - D. P.
AU - G. S.
PY - 2025
SP - 856
EP - 861
DO - 10.5220/0013956600004919
PB - SciTePress