Fruit maturity detection using deep learning: An overview

Abstract

Accurate and efficient fruit maturity assessment plays a critical role in the agricultural sector, impacting post-harvest management, fruit quality, and economic returns. Traditional methods for maturity detection often rely on destructive techniques or subjective visual inspection, leading to limitations. Deep learning (DL) has emerged as a powerful tool for automated fruit maturity prediction, offering a non-destructive, objective, and rapid approach. This review paper comprehensively explores the application of deep learning in fruit maturity detection. We delve into the theoretical foundations of deep learning architectures commonly employed for this task, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their combinations. We then analyze various deep learning techniques used for fruit maturity assessment, such as image segmentation, object detection, and regression analysis. We present a detailed review of existing research on deep learning applications for fruit maturity detection in various fruits, including apples, mangoes, bananas, citrus fruits, and berries. This review critically evaluates the strengths and limitations of deep learning approaches, highlighting their accuracy, efficiency, and generalizability. We discuss the impact of factors like image quality, data augmentation techniques, and transfer learning on model performance. Finally, we explore future research directions for deep learning in fruit maturity detection, focusing on areas like multi-modal learning, explainable AI approaches, and real-time applications for on-farm deployment.