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– ABSTRACT
Introduction:
Plant diseases pose a significant threat to both agricultural productivity and food security. Hence This study proposes a machine learning-driven solution for the rapid identification of plant diseases and the recommendation of appropriate remedies, aiming to mitigate crop losses.
Dataset Collection and Preprocessing:
Diverse datasets comprising images to list of diseased plants, environmental factors, and historical disease records are collected. Rigorous preprocessing involves image augmentation, normalization, and feature extraction to enhance the dataset’s quality and suitability for modeling.
Model Development Using Machine Learning Algorithms:
use state-of-the-art machine learning algorithms, including convolutional neural networks (CNNs), decision trees, and ensemble methods, to develop predictive models. These models learn from labeled images to classify and identify various plant diseases accurately.
Integration of Disease Identification and Treatment Recommendations:
Upon disease identification, the system matches the disease pattern with both the dataset and provides recommendations for suitable treatments or preventive measures. This integration streamlines the process from identification to actionable solutions.
Evaluation of Model Performance:
The models’ performance is rigorously evaluated using metrics like accuracy, precision, and recall. The assessment ensures the models’ reliability in disease identification and recommendation accuracy.
Significance in Agriculture and Food Security:
discuss the implications of rapid disease identification and targeted treatment recommendations for agriculture and food security. So this approach holds the potential to significantly reduce crop losses and enhance yield quality.
Enhancing Model Robustness and Deployment:
explore strategies like transfer learning, fine-tuning, and continuous learning from new data to strengthen model robustness. Additionally, we address considerations for deploying the system in real-time and scaling it for agricultural settings.
Conclusion:
The integration of machine learning techniques for identifying both plant diseases and recommending cures offers a promising avenue to revolutionize agricultural practices. This approach not only aids in early disease detection but also provides actionable insights for effective mitigation strategies.
Future Directions:
Future research directions involve following exploring AI-driven precision agriculture, leveraging IoT devices, and enhancing the system’s adaptability to diverse environmental conditions for more comprehensive disease management in agriculture.
