Implementation of Machine Learning Classification Algorithm Based on Ensemble Learning for Detection of Vegetable Crops Disease

In India, plant diseases pose a significant threat to food security, requiring precise detection and management protocols to minimize potential damage. Research introduces an innovative ensemble machine learning model for precise disease detection in tomato, potato, and bell pepper crops. Utilizing transfer learning, pre-trained models such as MobileNet and Inception are fine-tuned on a dataset of over 10,403 images of diseased and healthy plant leaves. The models are combined into a diverse ensemble, enhancing the precision and robustness of disease detection. The proposed ensemble models achieve an impressive accuracy rate of 98.95%, demonstrating their superiority over individual models in reducing misclassification and false positives. This advancement in plant disease detection provides valuable support to farmers and agricultural experts by enabling early disease identification and intervention.

@article{Jha2024,
  title     = {Implementation of Machine Learning Classification Algorithm Based on Ensemble Learning for Detection of Vegetable Crops Disease},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {1},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Pradeep Jha and Deepak Dembla and Widhi Dubey},
  doi       = {10.14569/IJACSA.2024.0150157},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150157}
}