Smart Agriculture: An AI-Driven System for Accurate Flower Classification and Optimal Crop Recommendation

This research paper introduces an innovative system that leverages machine learning techniques for the dual purpose of flower classification and crop recommendation. The proposed system combines image processing and data-driven algorithms to address challenges in agriculture and botany, ensuring accurate flower identification and efficient crop selection based on environmental factors. The flower classification module employs deep learning models, particularly convolutional neural networks (CNNs), to identify and categorize flower species with high accuracy. Simultaneously, the crop recommendation module utilizes a variety of machine learning algorithms to predict optimal crops tailored to specific soil, climate, and environmental parameters. Extensive experiments were conducted on publicly available datasets for flowers and agricultural data to validate the system's performance. Results demonstrate the system's capability to achieve robust classification and recommendation accuracy, making it a valuable tool for farmers, researchers, and horticulturists. This integrated solution showcases the potential of artificial intelligence in revolutionizing agricultural practices and biodiversity conservation.