Sensor fusion techniques in deep learning for multimodal fruit and vegetable quality assessment: A comprehensive review

Fruit and vegetable quality assessment is a critical task in agricultural and food industries, impacting various stages from production to consumption. Leveraging deep learning methods, particularly through sensor fusion, offers promising avenues to enhance the accuracy and robustness of quality assessment systems by amalgamating information from diverse sensor modalities such as visual, spectral, and tactile. The review scrutinizes a plethora of sensor fusion strategies, encompassing early fusion, late fusion, and hybrid fusion approaches, each with its distinct advantages and limitations. Furthermore, it explores the utilization of various deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their combinations, tailored specifically for multimodal data fusion. Additionally, attention is paid to the challenges and considerations associated with sensor fusion in this domain, including data heterogeneity, synchronization, and feature alignment. Moreover, the review discusses the implications of dataset size, diversity, and annotation quality on the effectiveness of deep learning-based fusion models. Furthermore, it sheds light on the transferability of fusion models across different fruit and vegetable types and environmental conditions, highlighting the need for domain adaptation techniques. Moreover, the review delves into the real-world applications and commercial implementations of sensor fusion-based quality assessment systems, providing insights into their scalability, efficiency, and economic viability.