Enhance the growth of agriculture by predicting crop diseases using optimization and deep learning

T. Sathish Kumar (), Martin Margala, S. Siva Shankar and Prasun Chakrabarti
Additional contact information
T. Sathish Kumar: Hyderabad Institute of Technology and Management
Martin Margala: University of Louisiana at Lafayette
S. Siva Shankar: KG Reddy College of Engineering and Technology
Prasun Chakrabarti: Sir Padampat Singhania University

International Journal of System Assurance Engineering and Management, 2025, vol. 16, issue 7, No 2, 2355-2366

Abstract: Abstract Crop disease diagnostics is essential in addressing this problem, educating farmers about how to stop the spread of illnesses in their crops, and putting appropriate management in place. However, the advent of numerous crop-related illnesses impacts the agriculture sector’s production. Many techniques were developed to predict crop diseases early, but there are still issues of overfitting, less detection, and classification problems. To overcome these issues, design a novel Ant Lion-based Deep Belief Neural system for detecting and classifying crop diseases and enhancing agriculture’s growth. Initially, PlantVillage datasets were collected from the net source and trained in the system, and they were implemented in the MATLAB tool. Then, the noise and errors in the dataset were removed in the preprocessing phase, and the affected parts were segmented based on the pixels using the GrabCut algorithm. Additionally, feature extraction is employed using the Gray-Level Co-Occurrence Matrix, which extracts shape, texture, and color features. Finally, detect and classify the affected diseases in the crop using threshold values. The designed model can accurately predict crop diseases using ant lion fitness. The experimental results indicate the efficiency of the designed model by attaining better performance metrics, and the gained results are validated with other conventional models in terms of accuracy, precision, recall, F-score, AUC, and error rate.

Keywords: Smart agriculture; Gray-level co-occurrence matrix; Ant lion optimization; Principal component analysis; Detect crop diseases; GrabCut; Segmentation (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-025-02737-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:16:y:2025:i:7:d:10.1007_s13198-025-02737-0

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-025-02737-0

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().