###### Abstract  
The presence of bruises on fruits often indicates cell damage, which can lead to a decrease in the ability of the peel to keep oxygen away from the fruits, and as a result, oxygen breaks down cell walls and membranes damaging fruit content. When chemicals in the fruit are oxidized by enzymes such as polyphenol oxidase, the chemical reaction produces an undesirable and apparent brown color effect, among others. Early detection of bruising prevents low-quality fruit from entering the consumer market. Hereupon, the present paper aims at early identification of bruised lemon fruits using 3D-convolutional neural networks (3D-CNN) via a local spectral-spatial hyperspectral imaging technique, which takes into account adjacent image pixel information in both the frequency (wavelength) and spatial domains of a 3D-tensor hyperspectral image of input lemon fruits. A total of 70 sound lemons were picked up from orchards. First, all fruits were labeled and the hyperspectral images (wavelength range 400-1100 nm) were captured as belonging to the healthy (unbruised) class (class label 0). Next, bruising was applied to each lemon by freefall. Then, the hyperspectral images of all bruised samples were captured in a time gap of 8 (class label 1) and 16 h (class label 2) after bruising was induced, thus resulting in a 3-class ternary classification problem. Four well-known 3D-CNN model namely ResNet, ShuffleNet, DenseNet, and MobileNet were used to classify bruised lemons in Python. Results revealed that the highest classification accuracy (90.47%) was obtained by the ResNet model, followed by DenseNet (85.71%), ShuffleNet (80.95%) and MobileNet (73.80%); all over the test set. ResNet model had larger parameter sizes, but it was proven to be trained faster than other models with fewer number of free parameters. ShuffleNet and MobileNet were easier to train and they needed less storage, but they could not achieve a classification error as low as the other two counterparts. bruise; classification; 3D-CNN; fruit; hyperspectral imaging; lemon; machine learning; tensor imaging +
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used in the food and non-food industry. Lemon consumption is increasing in producing and non-producing countries around the globe. However, fruised fruit does not constitute a health hazard but it is displeasing in terms of visual appearance to consumers. Post-harvest operations include a complex route from the garden to the market [3; 4] which includes various operations such as harvesting, packaging, sorting, storage, and transportation. Although during these operations every effort is made not to damage the fruit, nevertheless often fruit suffers from mechanical damage caused by either dynamic or static load [5; 6]. Lemon bruises are of great concern to the lemon industry and retailers because they reduce not only the visual appearance, but also the quality of the fruit and thus can cause significant economic losses. Bruising usually occurs in the tissue under the fruit peel cover. The amount of bruising depends on a number of factors such as the time of harvest, the maturity of the product, and the number of days after harvest. Physiological, biochemical and environmental properties are affected by the sensitivity of a given fruit to bruising [7]. Several studies, including hyperspectral imaging and near-infrared spectroscopy, have been reported for bruising detection [8; 9; 10; 11]. These optical techniques detect and evaluate changes or differences in optical properties (i.e., reflection or transmission of light) between normal and bruised tissues. However, reflection and transmission are not intrinsic properties and their measurement often depends on the sensing configuration, lightning conditions and the type of sensor. For instance, by the integration of image processing and spectroscopy, a spatial map of spectral changes can be obtained which has promising applications in the field of agricultural products quality assessment [12; 13; 14].