Revolutionizing Plant Health Monitoring with Machine Learning for Leaf Diseases

Rohan Kumar P., Sheru Sricharan, K. Chinnathambi

2025

Abstract

Accomplishing sustainable agricultural yield and food security requires timely and precise detection of leaf diseases. Conventional methods of disease detection rely heavily on manual observation, which is time-consuming, subjective, and labor-intensive. This reduces accessibility to numerous farmers, causing intervention delay and higher risk of crop loss. Break- throughs in deep learning and computer vision have transformed disease detection practices into automated and scalable solutions. Convolutional Neural Networks (CNNs) have been very effective in image-based classification, allowing for precise plant disease identification with minimal human intervention. The paper introduces a CNN model with special design for leaf disease detection, trained on a database of 8,685 leaf images taken under controlled conditions. The model suggested takes advantage of the Convolutional layers and pooling operations to mine spatial hierarchies of features and thereby enhance classification accuracy. For improving model stability and generalization, preprocessing techniques such as data augmentation and normalization have been employed, minimizing overfitting tendency and with stable performance. Experimental results indicate that the model is very accurate with a rate of 97.2%, and has an F1-score of over 96.5%. Emphasizing its consistency in real-world agriculture use. To enhance usability and accessibility, the trained model has been deployed as a web-based application, enabling users to upload leaf images for real-time disease diagnosis. The system provides instant feedback, facilitating early disease detection and enabling proactive management strategies to minimize crop damage. Furthermore, the use of transfer learning methods maximizes computational effectiveness, minimizing processing time while preserving superior predictive accuracy. This study emphasizes the revolutionary potential of deep learning for agricultural disease control. Through the use of AI-based solutions, farmers and horticultural experts are able to efficiently track crop health, avoid risks, and maximize yield results. Future research can emphasize developing the capabilities of the model to identify diseases across different crop species, its integration with smartphone-based apps for in-field diagnosis, and edge computing for real-time offline disease detection. The results bring out the imperative of AI-driven precision agriculture in meeting contemporary farming challenges through scalable and sustainable technologies. Future advancements may focus on extending the model’s capabilities to identify diseases across multiple crop species, integrating smartphone-based applications for field use, and employing edge computing for real-time, offline disease detection. The study underscores the significance of AI- driven precision agriculture, offering sustainable and scalable solutions for modern farming challenges.

Paper Citation

in Harvard Style

P. R., Sricharan S. and Chinnathambi K. (2025). Revolutionizing Plant Health Monitoring with Machine Learning for Leaf Diseases. 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 767-778. DOI: 10.5220/0013889700004919

in Bibtex Style

@conference{icrdicct`2525,
author={Rohan P. and Sheru Sricharan and K. Chinnathambi},
title={Revolutionizing Plant Health Monitoring with Machine Learning for Leaf Diseases},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={767-778},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013889700004919},
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 - Revolutionizing Plant Health Monitoring with Machine Learning for Leaf Diseases
SN - 978-989-758-777-1
AU - P. R.
AU - Sricharan S.
AU - Chinnathambi K.
PY - 2025
SP - 767
EP - 778
DO - 10.5220/0013889700004919
PB - SciTePress