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2025 Vol.16, Issue 1 Preview Page

General Article

Sustainable basil leaf disease classification: Benchmarking seven deep learning models using transfer learning for urban and rural farming
Divya Singla1
,
Deepali Gupta2
,
Nitin Goyal3*
1Research Scholar, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India
2Professor, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India
3Assistant Professor, Department of Computer Science and Engineering, School of Engineering and Technology, Central University of Haryana, Mahendergarh, Haryana, India
*Corresponding Author
31 March 2025. pp. 141-157
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Abstract

Basil (Ocimum basilicum) is a widely cultivated aromatic herbs from the Lamiaceae family. People value it for its culinary, medicinal, and economic significance. It contributes approximately 5–7% of global herb production, with demand in the food, pharmaceutical, and essential oil industries due to its antioxidant and antibacterial properties. However substantial yield losses are often seen in basil production due to viral, bacterial, and fungal diseases. Accurate and early disease detection is crucial for sustainable basil farming, yet manual diagnosis remains challenging, particularly in regions with limited agricultural experts. Automated disease detection and decision-support systems can enhance basil protection and yield preservation in both urban and rural agricultural settings. Despite various classification systems, a reliable and secure approach for basil disease detection is still lacking. This paper employs transfer learning for classifying basil leaf diseases using two distinct datasets. Seven pre-trained convolutional neural network (CNN) models—EfficientNetB3, EfficientNetB2S, Inception ResNetV2, MobileNetV2, ResNet152V2, VGG16, and VGG19—are fine-tuned to distinguish between healthy and diseased basil leaves. The datasets, containing diverse basil leaf images affected by common diseases, help evaluate model robustness under varying environmental and imaging conditions. Performance parameters such as accuracy, precision, recall, and loss are analysed to compare model efficacy. Results demonstrate that transfer learning significantly improves disease classification, with EfficientNetB3 achieving the highest accuracy for Dataset 1, while all models performed well on Dataset 2. The study demonstrates deep learning’s potential in sustainable agricultural disease management and emphasizes the importance of dataset diversity for model generalization. Future research could explore model ensembling and data augmentation to enhance detection accuracy, supporting resilient and sustainable farming practices.

Keywords
ocimum basilicum
basil leaf disease
transfer learning
sustainable agriculture
CNN models
urban farming
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Information
  • Publisher :Sustainable Building Research Center (ERC) Innovative Durable Building and Infrastructure Research Center
  • Publisher(Ko) :건설구조물 내구성혁신 연구센터
  • Journal Title :International Journal of Sustainable Building Technology and Urban Development
  • Volume : 16
  • No :1
  • Pages :141-157
  • Received Date : 2025-03-04
  • Accepted Date : 2025-03-17
  • DOI :https://doi.org/10.22712/susb.20250009
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