A STUDY TO ASSESS THE EFFECTIVENESS OF PLANNED TEACHING PROGRAMME ON MENSTRUAL HYGIENE KNOWLEDGE AND PRACTICES AMONG ADOLESCENT

Fruit classification has emerged as a crucial field within agricultural automation, enabling quality control, ripeness assessment, and disease detection with greater precision and efficiency. This review paper explores contemporary advancements in fruit classification techniques, focusing on the integration of deep learning, computer vision, and machine learning algorithms. Traditional manual methods are increasingly being replaced by Convolutional Neural Networks (CNNs), transfer learning approaches, and hybrid models like CNN-LSTM, which offer superior accuracy and real-time applicability. Recent innovations also address major challenges such as dataset scarcity through synthetic data generation and domain adaptation techniques. Edge computing, lightweight architectures, and automated image annotation systems have further enhanced classification performance across diverse agricultural environments. By analyzing a wide range of studies, this paper highlights the effectiveness of modern classification frameworks in improving post-harvest management, supporting smart farming, and facilitating global agricultural trade. The review concludes by discussing potential future directions, including model generalization, robustness improvement, and real-world deployment strategies in precision agriculture.