Abstract:

ABSTRACT- Plant identification is essential for biodiversity conservation, agriculture, and environmental monitoring. However, identifying species becomes challenging when dealing with imbalanced datasets, where some species are underrepresented. This project focuses on a combined imbalanced leaf dataset to develop a robust system for plant identification and damage assessment. Advanced machine learning and image processing techniques are employed to analyze leaf morphology and texture for accurate classification. The system also integrates innovative strategies to identify underrepresented species with equal precision. Moreover, it incorporates a damage detection module to assess leaf health and recommend remedies for diseased or damaged leaves, such as nutrient management, pest control, and environmental adjustments. Convolutional neural networks are used for feature learning, while data augmentation helps address dataset imbalances. Comprehensive testing shows significant improvements in precision and recall metrics. This approach has applications in agriculture, biodiversity research, and sustainable farming, enabling efficient species identification and health monitoring.

Description:

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