CentralMaizeGuard: Enhanced deep learning model for maize disease detection and management
The detection of maize plant leaf diseases is a critical aspect of agricultural management, necessitating accurate and efficient methodologies. The field of maize plant leaf disease detection encounters several challenges that hinder the development of robust and effective solutions. One prominent c...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Polish Academy of Sciences
2025-07-01
|
| Series: | Archives of Control Sciences |
| Subjects: | |
| Online Access: | https://journals.pan.pl/Content/135714/PDF/art02_int.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The detection of maize plant leaf diseases is a critical aspect of agricultural management, necessitating accurate and efficient methodologies. The field of maize plant leaf disease detection encounters several challenges that hinder the development of robust and effective solutions. One prominent challenge is the diversity of disease indicators, wherein various pathogens can cause similar symptoms, making it challenging to differentiate between diseases accurately. Additionally, limited annotated datasets pose a constraint, hindering the training of deep learning models and potentially leading to suboptimal generalization. The dynamic nature of plant growth and environmental conditions further complicates disease detection, as the appearance of symptoms may vary at different stages of plant development. Another challenge lies in achieving a balance between accuracy and computational efficiency, especially in real-time applications, as many existing models struggle to provide rapid and precise results simultaneously. This paper addresses prevailing challenges in disease detection by introducing a customized CenterNet approach, incorporating DenseNet-65 as its foundational architecture. Common issues encountered in traditional disease detection models are addressed through our proposed approach, which demonstrates superior performance in both disease classification and precise localization of affected regions. A comparative analysis is conducted against conventional and contemporary methodologies, emphasizing the innovation and competitiveness of our model in advancing maize plant leaf disease detection. We evaluated our approach on a standard dataset CD&S and attained a classification accuracy of 98.62%. This contribution not only expands the current understanding of disease detection in agriculture but also offers a practical and efficient solution for improved maize crop management. |
|---|---|
| ISSN: | 1230-2384 |