A Lightweight, Computation-Efficient CNN Framework for an Optimization-Driven Detection of Maize Crop Disease

Shahinza Manzoor, Muhammad Mughal, Syed Irtaza, Saif Islam, Jalil Boudjadar

2024

Abstract

Detecting and mitigating crop diseases can prevent significant yield losses and economic damage. However, most state-of-the-art solutions can be expensive computation-wise. This paper presents an efficient Lightweight multi-layer convolutional neural network (ML-CNN) to identify maize crop diseases. The proposed model aims to improve disease identification accuracy and reduce computational costs. The model was optimized by adjusting parameters, setting convolutional layers, changing the combinations of the pooling layer, and adding dropout layers. By optimizing the model architecture, we create a software tool that can be deployed in resource-limited environments, an ideal choice for deployment on embedded platforms. The PlantVillage dataset was used to train and test the model implementation, including images of healthy and two disease-affected leaves. The performance of the proposed model was compared with pre-trained models such as InceptionV3, VGG16, VGG19, and ResNet50. The analysis results show that the proposed model improved identification accuracy by 16.32%, 1.48%, 1.28%, and 2.26%, respectively. Additionally, the proposed model achieved identification accuracy of 99.60% on the training data and 98.16% on the testing data and also reduced iteration convergences and computational costs.

Paper Citation

in Harvard Style

Manzoor S., Mughal M., Irtaza S., Islam S. and Boudjadar J. (2024). A Lightweight, Computation-Efficient CNN Framework for an Optimization-Driven Detection of Maize Crop Disease. In Proceedings of the 19th International Conference on Software Technologies - Volume 1: ICSOFT; ISBN 978-989-758-706-1, SciTePress, pages 271-282. DOI: 10.5220/0012836900003753

in Bibtex Style

@conference{icsoft24,
author={Shahinza Manzoor and Muhammad Mughal and Syed Irtaza and Saif Islam and Jalil Boudjadar},
title={A Lightweight, Computation-Efficient CNN Framework for an Optimization-Driven Detection of Maize Crop Disease},
booktitle={Proceedings of the 19th International Conference on Software Technologies - Volume 1: ICSOFT},
year={2024},
pages={271-282},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012836900003753},
isbn={978-989-758-706-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Software Technologies - Volume 1: ICSOFT
TI - A Lightweight, Computation-Efficient CNN Framework for an Optimization-Driven Detection of Maize Crop Disease
SN - 978-989-758-706-1
AU - Manzoor S.
AU - Mughal M.
AU - Irtaza S.
AU - Islam S.
AU - Boudjadar J.
PY - 2024
SP - 271
EP - 282
DO - 10.5220/0012836900003753
PB - SciTePress