  The quality grading of mangoes is a crucial task for mango growers as it
vastly affects their profit. However, until today, this process still relies on
laborious efforts of humans, who are prone to fatigue and errors. To remedy
this, the paper approaches the grading task with various convolutional neural
networks (CNN), a tried-and-tested deep learning technology in computer vision.
The models involved include Mask R-CNN (for background removal), the numerous
past winners of the ImageNet challenge, namely AlexNet, VGGs, and ResNets; and,
a family of self-defined convolutional autoencoder-classifiers (ConvAE-Clfs)
inspired by the claimed benefit of multi-task learning in classification tasks.
Transfer learning is also adopted in this work via utilizing the ImageNet
pretrained weights. Besides elaborating on the preprocessing techniques,
training details, and the resulting performance, we go one step further to
provide explainable insights into the model's working with the help of saliency
maps and principal component analysis (PCA). These insights provide a succinct,
meaningful glimpse into the intricate deep learning black box, fostering trust,
and can also be presented to humans in real-world use cases for reviewing the
grading results.
