As an important export, cleanliness control on edible bird’s nest (EBN) is paramount. Automatic impurities detection is in urgent need to replace manual practices. However, effective impurities detection algorithm is yet to be developed due to the unresolved inhomogeneous optical properties of EBN. The objective of this work is to develop a novel U-net based algorithm for accurate impurities detection. The algorithm leveraged the convolution mechanisms of U-net for precise and localized features extraction. Output probability tensors were then generated from the deconvolution layers for impurities detection and positioning. The U-net based algorithm outperformed previous image processing-based methods with a higher impurities detection rate of 96.69% and a lower misclassification rate of 10.08%. The applicability of the algorithm was further confirmed with a reasonably high dice coefficient of more than 0.8. In conclusion, the developed U-net based algorithm successfully mitigated intensity inhomogeneity in EBN and improved the impurities detection rate.
Cheese has been high in demand because it is very convenient to be eaten on its own or to be used as an accompaniment to food. However, the time‐consuming and energy‐intensive manufacturing process together with the increased price of milk has made cheese an expensive product. Besides, cheese is found to contain high saturated fat content and has been linked to high cholesterol and increased risk of heart disease. Hence, there has been a shift toward developing a cheese product based on vegetable sources that imitates the unique characteristics of natural cheese such as stretchable or stringy upon melting. A great number of endeavors have been made to produce high‐quality imitation cheese. This paper reviews the uniqueness and various other aspects of natural cheese and the challenges of duplicating them into imitation cheese. Novelty impact statements Imitation cheese is a sustainable alternative to dairy cheese. Imitation cheese can be tailor‐made to offer nutritional and functionality advantages over natural cheese. Imitation cheese is not a threat but a promise to the cheese market.
Deep learning (DL) approaches have received extensive attention in plant growth monitoring due to their ground-breaking performance in image classification; however, the approaches have yet to be fully explored. This review article, therefore, aims to provide a comprehensive overview of the work and the DL developments accomplished over the years. This work includes a brief introduction on plant growth monitoring and the image-based techniques used for phenotyping. The bottleneck in image analysis is discussed and the need of DL methods in plant growth monitoring is highlighted. A number of research works focused on DL based plant growth monitoring-related applications published since 2017 have been identified and included in this work for review. The results show that the advancement in DL approaches has driven plant growth monitoring towards more complicated schemes, from simple growth stages identification towards temporal growth information extraction. The challenges, such as resource-demanding data annotation, data-hungriness for training, and extraction of both spatial and temporal features simultaneously for accurate plant growth prediction, however, remain unsolved.
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