Apple moldy core is a common internal fungal disease. The online detection and classification of apple moldy core plays a vital role in apple postharvest processing. In this paper, an online non-destructive detection system for apple moldy core disease was developed using near-infrared transmittance spectroscopy in spectral range of 600–1100 nm. A total of 120 apple samples were selected and randomly divided into a training set and a test set based on the ratio of 2:1. First, basic parameters for detection of apples with moldy core were determined through detection experiments of samples in a stationary state. Due to the random distribution of the diseased tissue inside diseased apples, stationary detection cannot accurately identify the diseased tissue. To solve this problem, the spectra of apples in motion state transmitted forward by the transmission line were acquired. Three placement orientations of the apple in the carrying fruit cup were tested to explore the influence of fruit orientation on spectral characteristics and prediction. According to the performance of the model, the optimal preprocessing method and modeling method were determined (fixed orientation model and arbitrary orientation model). SPA was used to select the characteristic wavelengths to further improve the online detection speed. The overall results showed that the multi-spectra model using mean spectra of three orientations was the best. The prediction accuracies of multi-spectra model using SPA for three orientations for three orientations were 96.7%, 97.5% and 97.5% respectively. As a conclusion, the arbitrary orientation model was beneficial to improve the online detection of apple moldy core disease.
Dense depth estimation based on a single image is a basic problem in computer vision and has exciting applications in many robotic tasks. Modelling fully supervised methods requires the acquisition of accurate and large ground truth data sets, which is often complex and expensive. On the other hand, self-supervised learning has emerged as a promising alternative to monocular depth estimation as it does not require ground truth depth data. In this paper, we propose a novel self-supervised joint learning framework for depth estimation using consecutive frames from monocular and stereo videos. Our architecture leverages two new ideas for improvement: (1) triplet attention and (2) funnel activation (FReLU). By adding triplet attention to the deep and pose networks, this module captures the importance of features across dimensions in a tensor without any information bottlenecks, making the optimisation learning framework more reliable. FReLU is used at the non-linear activation layer to grasp the local context adaptively in images, rather than using more complex convolutions at the convolution layer. FReLU extracts the spatial structure of objects by the pixel-wise modeling
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