Smart Agri Assist: Enhancing Leaf Disease Recognition Using Deep Learning Techniques

Sirisha Pagadala, Harshitha Reddy Peddakotla, Kusuma Mara, Greeshma Teja Gaddam, Mythri Thammineni

2025

Abstract

Implementing disease prediction systems for potato leaves would improve agricultural productivity and crop yield through early identification. This project is focused on detecting blight diseases using Artificial Intelligence (AI) and Deep Learning techniques. The proposed system can analyse leaf images, as shown in Figure 1, using a MobileNet-based architecture to extract the critical features making sure enough attention is paid towards colour, texture as well as shape in order to do leaf classification. Along with a dense layer, this also requires a SoftMax layer to ensure that the model provides an output with a confidence score corresponding to each diagnosis in order to establish the reliability of the output. It further offers personalized therapeutic recommendations, including advice on organic and chemical pathologies and mechanical damage management, aiding farmers in tracking up on the specified pathology and damage. This indicates how deep learning is substantially changing agriculture and also holds the potential to help farmers make better decisions to minimize crop loss and increase sustainability in agricultural practices.

Paper Citation

in Harvard Style

Pagadala S., Peddakotla H., Mara K., Gaddam G. and Thammineni M. (2025). Smart Agri Assist: Enhancing Leaf Disease Recognition Using Deep Learning Techniques. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 819-826. DOI: 10.5220/0013874000004919

in Bibtex Style

@conference{icrdicct`2525,
author={Sirisha Pagadala and Harshitha Peddakotla and Kusuma Mara and Greeshma Gaddam and Mythri Thammineni},
title={Smart Agri Assist: Enhancing Leaf Disease Recognition Using Deep Learning Techniques},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25},
year={2025},
pages={819-826},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013874000004919},
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 - Volume 1: ICRDICCT`25
TI - Smart Agri Assist: Enhancing Leaf Disease Recognition Using Deep Learning Techniques
SN - 978-989-758-777-1
AU - Pagadala S.
AU - Peddakotla H.
AU - Mara K.
AU - Gaddam G.
AU - Thammineni M.
PY - 2025
SP - 819
EP - 826
DO - 10.5220/0013874000004919
PB - SciTePress